diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index 8733366e..1deeeb0c 100755 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -1,18 +1,10 @@ -# For help debugging build failures open an issue on the RStudio community with the 'github-actions' tag. -# https://community.rstudio.com/new-topic?category=Package%20development&tags=github-actions +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help on: push: - branches: - - MCMCglmm - - main - - master - - release + branches: [main, master, release] pull_request: - branches: - - MCMCglmm - - main - - master - - release + branches: [main, master, release] name: R-CMD-check @@ -26,73 +18,34 @@ jobs: fail-fast: false matrix: config: - - {os: windows-latest, r: 'release', vignettes: ', "--ignore-vignettes"), build_args = ("--no-bulid-vignettes"'} - - {os: macOS-latest, r: 'release', vignettes: ', "--ignore-vignettes"), build_args = ("--no-bulid-vignettes"'} - - {os: ubuntu-20.04, r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"} + - {os: macos-latest, r: 'release'} + - {os: windows-latest, r: 'release'} + - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} + - {os: ubuntu-latest, r: 'release'} + - {os: ubuntu-latest, r: 'oldrel-1'} env: - RGL_USE_NULL: true - _R_CHECK_CRAN_INCOMING_: true # Seemingly not set by --as-cran - _R_CHECK_FORCE_SUGGESTS_: false # CRAN settings - R_COMPILE_AND_INSTALL_PACKAGES: 'never' - _R_CHECK_THINGS_IN_CHECK_DIR_: false - R_REMOTES_STANDALONE: true - R_REMOTES_NO_ERRORS_FROM_WARNINGS: true - _R_CHECK_CRAN_INCOMING_USE_ASPELL_: false # Set to true when can figure how to install aspell on Windows - RSPM: ${{ matrix.config.rspm }} GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - + R_KEEP_PKG_SOURCE: yes + R_REMOTES_NO_ERRORS_FROM_WARNINGS: true + steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - - uses: r-lib/actions/setup-r@v1 + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 with: r-version: ${{ matrix.config.r }} + http-user-agent: ${{ matrix.config.http-user-agent }} + use-public-rspm: true - - uses: r-lib/actions/setup-pandoc@v1 - - - name: Query dependencies - run: | - install.packages('remotes') - saveRDS(remotes::dev_package_deps(dependencies = c("soft", "Config/Needs/check")), - ".github/depends.Rds", version = 2) - writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), - ".github/R-version") - shell: Rscript {0} - - - name: Cache R packages - uses: actions/cache@v2 + - uses: r-lib/actions/setup-r-dependencies@v2 with: - path: ${{ env.R_LIBS_USER }} - key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} - restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- - - - name: Install system dependencies - if: runner.os == 'Linux' - run: | - sudo apt-get install ghostscript texlive-latex-base texlive-latex-extra texlive-fonts-recommended texlive-fonts-extra - while read -r cmd - do - eval sudo $cmd - done < <(Rscript -e 'writeLines(remotes::system_requirements("ubuntu", "20.04"))') - - - name: Install dependencies - run: | - remotes::install_deps(dependencies = c("soft", "Config/Needs/check")) - # pak::pkg_install("rcmdcheck") - shell: Rscript {0} - - - name: Check - env: - _R_CHECK_CRAN_INCOMING_REMOTE_: false - run: | - rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran" ${{ matrix.config.vignettes }}), - error_on = "error", check_dir = "check") - shell: Rscript {0} + extra-packages: any::rcmdcheck + needs: check - - name: Upload check results - if: failure() - uses: actions/upload-artifact@main + - uses: r-lib/actions/check-r-package@v2 with: - name: ${{ runner.os }}-r${{ matrix.config.r }}-results - path: check + upload-snapshots: true + error-on: '"error"' diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml new file mode 100755 index 00000000..4b654182 --- /dev/null +++ b/.github/workflows/test-coverage.yaml @@ -0,0 +1,31 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + +name: test-coverage + +jobs: + test-coverage: + runs-on: ubuntu-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + + steps: + - uses: actions/checkout@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::covr + needs: coverage + + - name: Test coverage + run: covr::codecov(quiet = FALSE) + shell: Rscript {0} diff --git a/.travis.yml b/.travis.yml index adf8cafb..e9f946ef 100755 --- a/.travis.yml +++ b/.travis.yml @@ -25,4 +25,4 @@ r_packages: after_success: - - Rscript -e 'library(covr); codecov(token = "5f041826-63f1-47fa-b4a8-9a32633f47fa")' + - Rscript -e 'library(covr); codecov(token = "14042dcc-32e4-418a-b5ca-fa368414b775")' diff --git a/DESCRIPTION b/DESCRIPTION index 8ebff51d..6a540d25 100755 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,10 +1,14 @@ Package: dispRity Title: Measuring Disparity -Author: Thomas Guillerme [aut, cre, cph], - Mark N Puttick [aut, cph] +Authors@R: c(person("Thomas", "Guillerme", role = c("aut", "cre", "cph"), + email = "guillert@tcd.ie", + comment = c(ORCID = "0000-0003-4325-1275")), + person("Mark", "Puttick", role = c("aut", "cph")), + person("Jack", "Hatfield", role = c("aut", "cph")) + ) Maintainer: Thomas Guillerme -Version: 1.7.0 -Date: 2022-08-08 +Version: 1.8 +Date: 2023-12-11 Description: A modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis. Depends: R (>= 3.6.0), @@ -16,6 +20,7 @@ Imports: Claddis, ellipse, geometry, + GET, graphics, grDevices, MASS, @@ -24,17 +29,16 @@ Imports: parallel, phangorn, phyclust, + phylolm, utils, vegan, scales, - spptest, + zoo, License: GPL-3 | file LICENSE -Remotes: - github::myllym/spptest@no_fastdepth Suggests: MCMCglmm, geoscale, testthat, knitr -RoxygenNote: 7.2.0 +RoxygenNote: 7.2.3 URL: https://github.com/TGuillerme/dispRity diff --git a/NAMESPACE b/NAMESPACE index adfa5706..2a664db7 100755 --- a/NAMESPACE +++ b/NAMESPACE @@ -10,16 +10,18 @@ importFrom("geometry", "convhulln", "dot") importFrom("graphics", "abline", "axis", "boxplot", "hist", "image", "lines", "mtext", "par", "plot", "points", "polygon", "text", "layout", "legend", "barplot") importFrom("grDevices", "colorRampPalette", "grey", "hcl", "heat.colors", "adjustcolor") importFrom("MASS", "mvrnorm") -importFrom("methods", "hasArg", "formalArgs")#, "is") +importFrom("methods", "hasArg", "formalArgs", "is") importFrom("mnormt", "dmnorm", "rmnorm") # importFrom("paleotree", "timeSliceTree") importFrom("phangorn", "dist.hamming", "NJ", "RF.dist", "CI", "RI", "optim.parsimony", "parsimony") importFrom("phyclust", "gen.seq.HKY") +importFrom("phylolm", "phylolm") importFrom("utils", "combn", "data", "capture.output", "tail") -importFrom("vegan", "adonis", "vegdist") +importFrom("vegan", "adonis2", "vegdist") importFrom("scales", "rescale") -importFrom("spptest", "create_curve_set", "rank_envelope") +importFrom("GET", "create_curve_set", "rank_envelope") importFrom("parallel", "parLapply", "detectCores", "makeCluster", "clusterExport", "stopCluster") +importFrom("zoo", "rollmean") # importFrom("geomorph", "gpagen") # importFrom("RCurl", "getURL", "url.exists") @@ -61,7 +63,8 @@ export(dimension.level2.fun) export(dimension.level3.fun) export(disalignment) export(displacements) -export(ellipse.volume) +export(ellipse.volume) # alias for ellipsoid.volume +export(ellipsoid.volume) export(edge.length.tree) export(func.div) export(func.eve) @@ -77,6 +80,7 @@ export(projections.tree) export(quantiles) export(radius) export(ranges) +export(roundness) export(span.tree.length) export(variances) @@ -88,6 +92,7 @@ export(model.test) export(model.test.sim) export(model.test.wrapper) export(null.test) +export(pgls.dispRity) export(randtest.dispRity) # export(sequential.test) @@ -103,7 +108,9 @@ export(get.subsets) export(get.covar) export(n.subsets) export(make.dispRity) -export(rescale.dispRity) +export(name.subsets) +export(rescale.dispRity) # alias for scale +export(scale.dispRity) export(size.subsets) export(sort.dispRity) export(add.tree) @@ -120,9 +127,10 @@ export(MCMCglmm.traits) export(MCMCglmm.levels) export(MCMCglmm.sample) export(MCMCglmm.covars) +export(MCMCglmm.variance) export(pair.plot) export(random.circle) -export(randtest.dist) +export(distance.randtest) export(reduce.matrix) export(reduce.space) export(remove.zero.brlen) @@ -140,8 +148,8 @@ export(get.contrast.matrix) export(sim.morpho) export(multi.ace) -##S3 -S3method(adonis, dispRity) +##S3 +S3method(scale, dispRity) S3method(matrix, dispRity) S3method(plot, char.diff) S3method(plot, dispRity) diff --git a/NEWS.md b/NEWS.md index 8b024e7d..03135839 100755 --- a/NEWS.md +++ b/NEWS.md @@ -1,27 +1,52 @@ - - - - - + * `ellipse.volume` has been changed to `ellipsoid.volume` to more accurately reflect what it is measuring. + * `rescale.dispRity` has been changed to `scale.dispRity` and correctly registered as a S3 method. + * `randtest.dist` has been changed to `distance.randtest` to avoid conflict with `*.dist` S3 methods. dispRity v1.7 (2022-08-08) *MacMacGlimm* ========================= @@ -73,7 +98,7 @@ dispRity v1.7 (2022-08-08) *MacMacGlimm* * `standardGeneric` functions are now correctly interpreted as functions throughout the package. * Fixed bug when plotting level 1 disparity metric results without bootstrapped (`observed = TRUE` is now used as the default). * Fixed bug when plotting `test.metric` plots with `save.steps` options with more than two types of shifts. - * Fixed bug with `null.test` which is now correctly managing the number of dimensions inherited from `dispRity` objects (thanks to [Alex Slavenko](https://alexslavenko.weebly.com/) for spotting this one and the two above). + * Fixed bug with `null.test` which is now correctly managing the number of dimensions inherited from `dispRity` objects (thanks to Alex Slavenko for spotting this one and the two above). * Fixed bug when using level 2 dimension metrics on unidimensional data (the metric is now detected as a level 2 correctly; thanks to Catherine Klein and [Rachel Warnock](https://www.gzn.nat.fau.de/palaeontologie/team/professors/rachel-warnock/) for noticing that one). * Update internal use of `is(data, c("array", "matrix"))` to `is.array(data)` for R 4.1.2. @@ -92,7 +117,7 @@ dispRity v1.6.0 (2021-04-16) *dispRitree* * You can now save the shifts results in `test.metric` with `save.steps` and then visualise them with `plot.dispRity` along side the disparity metric test results. * *New* utility function `n.subsets` to directly get the number of subsets in a `dispRity` object. * *New* statistical test: `randtest.dispRity` that is a wrapper for `ade4::randtest` applied to `dispRity` objects (not dissimilar from `null.test`). - * Six more demo datasets have been added to the package! These datasets are the ones used in [Guillerme et al. 2020](https://scholar.google.co.uk/scholar?hl=en&as_sdt=0%2C5&q=Shifting+spaces%3A+Which+disparity+or+dissimilarity+measurement+best+summarize+occupancy+in+multidimensional+spaces%3F&btnG=) and published originally in [Beck & Lee 2014](https://royalsocietypublishing.org/doi/full/10.1098/rspb.2014.1278) (that one was originally the only demo dataset in the package), [Wright 2017](https://www.cambridge.org/core/journals/journal-of-paleontology/article/bayesian-estimation-of-fossil-phylogenies-and-the-evolution-of-early-to-middle-paleozoic-crinoids-echinodermata/E37972902541CD0995AAD08A1122BD54), [Marcy et al. 2016](https://link.springer.com/article/10.1186/s12862-016-0782-1), [Hopkins & Pearson 2016](https://pdfs.semanticscholar.org/a3f0/50944d2aefa1df811ea94a3eea630d82c24f.pdf)), [Jones et al. 2015](https://scholar.google.co.uk/scholar?hl=en&as_sdt=0%2C5&q=Dispersal+mode+mediates+the+effect+of+patch+size+and+patch+connectivity+on+metacommunity+diversity&btnG=), [Healy et al. 2019](https://www.nature.com/articles/s41559-019-0938-7). Thanks to all these authors for their open science work! + * Six more demo datasets have been added to the package! These datasets are the ones used in [Guillerme et al. 2020](https://scholar.google.co.uk/scholar?hl=en&as_sdt=0%2C5&q=Shifting+spaces%3A+Which+disparity+or+dissimilarity+measurement+best+summarize+occupancy+in+multidimensional+spaces%3F&btnG=) and published originally in [Beck & Lee 2014](https://doi.org/10.1098/rspb.2014.1278) (that one was originally the only demo dataset in the package), [Wright 2017](https://www.cambridge.org/core/journals/journal-of-paleontology/article/bayesian-estimation-of-fossil-phylogenies-and-the-evolution-of-early-to-middle-paleozoic-crinoids-echinodermata/E37972902541CD0995AAD08A1122BD54), [Marcy et al. 2016](https://link.springer.com/article/10.1186/s12862-016-0782-1), [Hopkins & Pearson 2016](https://pdfs.semanticscholar.org/a3f0/50944d2aefa1df811ea94a3eea630d82c24f.pdf)), [Jones et al. 2015](https://scholar.google.co.uk/scholar?hl=en&as_sdt=0%2C5&q=Dispersal+mode+mediates+the+effect+of+patch+size+and+patch+connectivity+on+metacommunity+diversity&btnG=), [Healy et al. 2019](https://www.nature.com/articles/s41559-019-0938-7). Thanks to all these authors for their open science work! * `dispRity` objects now have a reserved `$tree` component that contain any number of trees attached to the data. This allows any function to use the reserved argument name `tree` to extract directly the relevant tree from the `dispRity` object, for functions like `chrono.subsets` or metrics like `ancestral.dist`! To help manipulate the `tree` component of the `dispRity` object, you can now use the new utility functions `add.tree`, `get.tree` and `remove.tree`. ### MINOR IMPROVEMENT @@ -390,7 +415,7 @@ dispRity v0.3 (2017-01-25) *dispRity lite* ### NEW FEATURES - * Complete change of the `dispRity` object architecture (see more [here](https://github.com/TGuillerme/dispRity/blob/master/disparity_object.md)). + * Complete change of the `dispRity` object architecture. ### MINOR IMPROVEMENTS diff --git a/R/MCMCglmm.subsets.R b/R/MCMCglmm.subsets.R index 014d9d9d..f36d7a31 100755 --- a/R/MCMCglmm.subsets.R +++ b/R/MCMCglmm.subsets.R @@ -72,7 +72,7 @@ MCMCglmm.subsets <- function(data, posteriors, group, tree, rename.groups, set.l ## Extracting the residuals and randoms posterior_levels <- MCMCglmm.levels(posteriors) - posterior_terms <- lapply(posterior_levels, split.term.name) + posterior_terms <- lapply(posterior_levels, term.name.split) ## Extracting the group from the posteriors extracted_group <- lapply(posterior_terms, get.one.group, group_classifier, elements = rownames(cleaned_data)) @@ -108,7 +108,7 @@ MCMCglmm.subsets <- function(data, posteriors, group, tree, rename.groups, set.l ## Replace the location for the invariant groups for(group in 1:length(invariants)) { if(invariants[group]) { - covar_matrices <- update.location(covar_matrices, cleaned_data, subsets, group, dimensions) + covar_matrices <- location.update(covar_matrices, cleaned_data, subsets, group, dimensions) } } } @@ -122,7 +122,7 @@ MCMCglmm.subsets <- function(data, posteriors, group, tree, rename.groups, set.l } ## Create a dispRity style object - output <- dispRity::make.dispRity(data = cleaned_data, call = list("subsets" = "covar", "dimensions" = dimensions), subsets = subsets) + output <- make.dispRity(data = cleaned_data, call = list("subsets" = "covar", "dimensions" = dimensions), subsets = subsets) ## Add the covar element output$covar <- covar_matrices ## Update the call (bootstrap part) diff --git a/R/MCMCglmm.subsets_fun.R b/R/MCMCglmm.subsets_fun.R index a7f92335..98b434cf 100755 --- a/R/MCMCglmm.subsets_fun.R +++ b/R/MCMCglmm.subsets_fun.R @@ -26,7 +26,7 @@ get.one.group <- function(one_term, group_classifier, elements) { } ## Splitting a term name -split.term.name <- function(one_term) { +term.name.split <- function(one_term) { ## Initialise the factor and level factor <- level <- NULL ## Split the term @@ -45,7 +45,7 @@ split.term.name <- function(one_term) { } ## Set the location -update.location <- function(covar, data, subsets, group, dimensions) { +location.update <- function(covar, data, subsets, group, dimensions) { ## Get the group mean centroid <- unname(colMeans(data[subsets[[group]]$elements, ])[dimensions]) ## Update the centroids diff --git a/R/MCMCglmm.utilities.R b/R/MCMCglmm.utilities.R index 17529e05..24eee3b4 100755 --- a/R/MCMCglmm.utilities.R +++ b/R/MCMCglmm.utilities.R @@ -1,5 +1,5 @@ #' @name MCMCglmm.utilities -#' @aliases MCMCglmm.traits MCMCglmm.levels MCMCglmm.sample MCMCglmm.covars +#' @aliases MCMCglmm.traits MCMCglmm.levels MCMCglmm.sample MCMCglmm.covars MCMCglmm.variance #' @title MCMCglmm object utility functions #' #' @description Different utility functions to extract aspects of a \code{MCMCglmm} object. @@ -8,18 +8,22 @@ #' @usage MCMCglmm.levels(MCMCglmm, convert) #' @usage MCMCglmm.sample(MCMCglmm, n) #' @usage MCMCglmm.covars(MCMCglmm, n, sample) +#' @usage MCMCglmm.variance(MCMCglmm, n, sample, levels, scale) #' #' @param MCMCglmm A \code{MCMCglmm} object. #' @param n Optional, a number of random samples to extract. #' @param sample Optional, the specific samples to extract (is ignored if \code{n} is present). #' @param convert Logical, whether to return the raw term names names as expressed in the model column names (\code{FALSE}) or to convert it to something more reader friendly (\code{TRUE}; default). +#' @param levels Optional, a vector \code{"character"} values (matching \code{MCMCglmm.levels(..., convert = TRUE)}) or of \code{"numeric"} values designating which levels to be used to calculate the variance (if left empty, all the levels are used). +#' @param scale Logical, whether to scale the variance relative to all the levels (\code{TRUE}; default) or not (\code{FALSE})/ #' #' @details #' \itemize{ #' \item \code{MCMCglmm.levels} returns the different random and residual terms levels of a \code{MCMCglmm} object. This function uses the default option \code{convert = TRUE} to convert the names into something more readable. Toggle to \code{convert = FALSE} for the raw names. #' \item \code{MCMCglmm.traits} returns the column names of the different traits of a \code{MCMCglmm} formula object. #' \item \code{MCMCglmm.sample} returns a vector of sample IDs present in the \code{MCMCglmm} object. If \code{n} is missing, all the samples IDs are returned. Else, a random series of sample IDs are returned (with replacement if n greater than the number of available samples). -#' \item \code{MCMCglmm.covars} returns a list of covariance matrices and intercepts from a \code{MCMCglmm} object (respectively from \code{MCMCglmm$VCV} and \code{MCMCglmm$Sol}). By default, all the covariance matrices and intercepts are returned but you can use either of the arguments \code{sample} to return specific samples (e.g. \code{MCMCglmm.covars(data, sample = c(1, 42))} for returning the first and 42nd samples) or \code{n} to return a specific number of random samples (e.g. \code{MCMCglmm.covars(data, n = 42)} for returning 42 random samples). +#' \item \code{MCMCglmm.covars} returns a list of covariance matrices and intercepts from a \code{MCMCglmm} object (respectively from \code{MCMCglmm$VCV} and \code{MCMCglmm$Sol}). By default, all the covariance matrices and intercepts are returned but you can use either of the arguments \code{sample} to return specific samples (e.g. \code{MCMCglmm.covars(data, sample = c(1, 42))} for returning the first and 42nd samples) or \code{n} to return a specific number of random samples (e.g. \code{MCMCglmm.covars(data, n = 42)} for returning 42 random samples). +#' \item \code{MCMCglmm.variance} returns a list of covariance matrices and intercepts from a \code{MCMCglmm} object (respectively from \code{MCMCglmm$VCV} and \code{MCMCglmm$Sol}). By default, all the covariance matrices and intercepts are returned but you can use either of the arguments \code{sample} to return specific samples (e.g. \code{MCMCglmm.covars(data, sample = c(1, 42))} for returning the first and 42nd samples) or \code{n} to return a specific number of random samples (e.g. \code{MCMCglmm.covars(data, n = 42)} for returning 42 random samples). #' } #' #' @examples @@ -45,7 +49,11 @@ #' MCMCglmm.covars(model, sample = 42) #' ## Get two random samples from the model #' MCMCglmm.covars(model, n = 2) -#' + +## Get the variance for each terms in the model +# terms_variance <- MCMCglmm.variance(model) +# boxplot(terms_variance, horizontal = TRUE) + #' @seealso \code{\link{MCMCglmm.subsets}} #' #' @author Thomas Guillerme @@ -76,6 +84,10 @@ MCMCglmm.levels <- function(MCMCglmm, convert = TRUE) { } } + ## Sanitizing + check.class(MCMCglmm, "MCMCglmm") + check.class(convert, "logical") + ## Get the random terms random_formula <- as.character(MCMCglmm$Random$formula[2]) if(length(random_formula) == 0) { @@ -121,6 +133,9 @@ MCMCglmm.levels <- function(MCMCglmm, convert = TRUE) { ## Get the number of traits from a MCMCglmm MCMCglmm.traits <- function(MCMCglmm) { + ## Sanitizing + check.class(MCMCglmm, "MCMCglmm") + ## Get the variables variables <- as.character(MCMCglmm$Fixed$formula[2]) @@ -141,10 +156,15 @@ MCMCglmm.traits <- function(MCMCglmm) { ## Get the samples from a MCMCglmm object MCMCglmm.sample <- function(MCMCglmm, n) { + + ## Sanitizing + check.class(MCMCglmm, "MCMCglmm") + n_samples <- nrow(MCMCglmm$Sol) if(missing(n)) { return(1:n_samples) } else { + check.class(n, c("numeric", "integer")) replace = n > n_samples if(replace) { warning(paste0("The required number of samples ", n, " is larger than the available number of samples ", n_samples, ". Some samples will be used more than once.")) @@ -156,6 +176,9 @@ MCMCglmm.sample <- function(MCMCglmm, n) { ## Get some covar matrices MCMCglmm.covars <- function(MCMCglmm, n, sample){ + ## Sanitizing + check.class(MCMCglmm, "MCMCglmm") + ## The number of traits traits <- MCMCglmm.traits(MCMCglmm) n_traits <- length(traits) @@ -168,6 +191,8 @@ MCMCglmm.covars <- function(MCMCglmm, n, sample){ if(missing(sample)) { sample <- MCMCglmm.sample(MCMCglmm) } else { + check.class(sample, c("numeric", "integer")) + ## Check for incorect samples if(length(incorect_sample <- which(sample > length(MCMCglmm.sample(MCMCglmm)))) > 0) { #dispRity_export in: MAKE dispRity STOP STYLE @@ -175,6 +200,7 @@ MCMCglmm.covars <- function(MCMCglmm, n, sample){ } } } else { + check.class(n, c("numeric", "integer")) if(!missing(sample)) { #dispRity_export in: MAKE dispRity WARNING STYLE warning("sample argument is ignored since n = ", n, " random samples are asked for.") @@ -193,3 +219,49 @@ MCMCglmm.covars <- function(MCMCglmm, n, sample){ } return(results_out) } + +## Get the variance per VCV for each level +MCMCglmm.variance <- function(MCMCglmm, n, sample, levels, scale = TRUE) { + + match_call <- match.call() + + ## Sanitizing + check.class(MCMCglmm, "MCMCglmm") + check.class(scale, "logical") + + ## Extract sum of each VCV matrices + VCV_sums <- lapply(MCMCglmm.covars(MCMCglmm, n, sample), lapply, function(x, what) sum(diag(x[[what]])), what = "VCV") + + ## Make that into a matrix + model_variances <- matrix(unlist(VCV_sums), ncol = length(VCV_sums), byrow = FALSE) + + ## Extract only the relevant levels + avail_levels <- MCMCglmm.levels(MCMCglmm, convert = TRUE) + + if(!missing(levels)) { + ## Remove some levels + level_class <- check.class(levels, c("numeric", "integer", "character")) + if(level_class == "character") { + if(!all(is_match <- levels %in% avail_levels)) { + stop.call(msg.pre = paste0("The following level(s): ", paste0(levels[!is_match], collapse = ", "), " are not found in "), match_call$MCMCglmm, msg = ".") + } else { + levels <- match(levels, avail_levels) + } + } + if(any(levels > length(avail_levels))) { + stop.call(msg.pre = paste0("Only ", length(avail_levels), " levels (terms) are present in "), match_call$MCMCglmm, msg = ".") + } + avail_levels <- avail_levels[levels] + model_variances <- model_variances[, levels] + } + + ## Rename the levels columns + colnames(model_variances) <- avail_levels + + if(scale){ + ## Scale the results + return(model_variances/rowSums(model_variances)) + } else { + return(model_variances) + } +} \ No newline at end of file diff --git a/R/adonis.dispRity.R b/R/adonis.dispRity.R index 6ea72e81..3d36a9d0 100755 --- a/R/adonis.dispRity.R +++ b/R/adonis.dispRity.R @@ -70,7 +70,6 @@ #' ## Running the NPMANOVA with each time bin as a predictor #' adonis.dispRity(time_subsets, matrix ~ chrono.subsets) #' -#' @seealso \code{\link{test.dispRity}}, \code{\link{custom.subsets}}, \code{\link{chrono.subsets}} #' #' @author Thomas Guillerme # @export diff --git a/R/bhatt.coeff.R b/R/bhatt.coeff.R index 99262ccb..72585414 100755 --- a/R/bhatt.coeff.R +++ b/R/bhatt.coeff.R @@ -1,6 +1,6 @@ #' @name bhatt.coeff #' -#' @title Bhattacharrya Coefficient +#' @title Bhattacharyya Coefficient #' #' @description Calculates the probability of overlap between two distributions. #' diff --git a/R/boot.matrix.R b/R/boot.matrix.R index 4437eef8..d53585ee 100755 --- a/R/boot.matrix.R +++ b/R/boot.matrix.R @@ -28,6 +28,7 @@ #' \itemize{ #' \item \code{"full"}: resamples all the rows of the matrix and replaces them with a new random sample of rows (with \code{replace = TRUE}, meaning all the elements can be duplicated in each bootstrap). #' \item \code{"single"}: resamples only one row of the matrix and replaces it with a new randomly sampled row (with \code{replace = FALSE}, meaning that only one element can be duplicated in each bootstrap). +#' \item \code{"null"}: resamples all rows of the matrix across subsets. I.e. for each subset of \emph{n} elements, this algorithm resamples \emph{n} elements across \emph{ALL} subsets. If only one subset (or none) is used, this does the same as the \code{"full"} algorithm. #' } #' #' \code{prob}: This option allows to attribute specific probability to each element to be drawn. @@ -86,17 +87,21 @@ # bootstraps <- 3 # rarefaction <- TRUE -boot.matrix <- function(data, bootstraps = 100, rarefaction = FALSE, dimensions, verbose = FALSE, boot.type = "full", prob) { +boot.matrix <- function(data, bootstraps = 100, rarefaction = FALSE, dimensions = NULL, verbose = FALSE, boot.type = "full", prob = NULL) { match_call <- match.call() ## ---------------------- ## Cleaning and checking ## ---------------------- + is_multi <- FALSE + ## DATA ## If class is dispRity, data is serial if(!is(data, "dispRity")) { ## Data must be a matrix - data <- check.dispRity.data(data) + data <- check.dispRity.data(data, returns = c("matrix", "multi")) + is_multi <- any(is_multi, data$multi) + data <- data$matrix ## Check whether it is a distance matrix if(check.dist.matrix(data[[1]], just.check = TRUE)) { @@ -106,7 +111,6 @@ boot.matrix <- function(data, bootstraps = 100, rarefaction = FALSE, dimensions, ## Creating the dispRity object data <- make.dispRity(data = data) } else { - ## Must not already been bootstrapped if(!is.null(data$call$bootstrap)) { stop.call(msg.pre = "", match_call$data, msg = " was already bootstrapped.") @@ -134,6 +138,32 @@ boot.matrix <- function(data, bootstraps = 100, rarefaction = FALSE, dimensions, } } + check.class(verbose, "logical") + + ## If is multi lapply the stuff + if((!is.null(data$call$dispRity.multi) && data$call$dispRity.multi) || is_multi) { + ## Split the data + split_data <- dispRity.multi.split(data) + + ## Change the verbose call + boot.matrix.call <- boot.matrix + if(verbose) { + ## Find the verbose lines + start_verbose <- which(as.character(body(boot.matrix.call)) == "if (verbose) message(\"Bootstrapping\", appendLF = FALSE)") + end_verbose <- which(as.character(body(boot.matrix.call)) == "if (verbose) message(\"Done.\", appendLF = FALSE)") + ## Comment out both lines + body(boot.matrix.call)[[start_verbose]] <- body(boot.matrix.call)[[end_verbose]] <- substitute(empty_line <- NULL) + } + + if(verbose) message("Bootstrapping", appendLF = FALSE) + + ## Apply the custom.subsets + output <- dispRity.multi.apply(split_data, fun = boot.matrix.call, bootstraps = bootstraps, rarefaction = rarefaction, dimensions = dimensions, verbose = verbose, boot.type = boot.type, prob = prob) + + if(verbose) message("Done.", appendLF = FALSE) + return(output) + } + ## Data must contain a first "bootstrap" (empty list) if(length(data$subsets) == 0) { data <- fill.dispRity(data) @@ -157,7 +187,7 @@ boot.matrix <- function(data, bootstraps = 100, rarefaction = FALSE, dimensions, } } - if(!missing(prob)) { + if(!is.null(prob)) { if(probabilistic_subsets || has_multiple_trees) { stop.call(match_call$data, paste0(" was generated using a gradual time-slicing or using multiple trees (", data$call$subsets[2], ").\nThe prob option is not yet implemented for this case.")) } else { @@ -260,16 +290,13 @@ boot.matrix <- function(data, bootstraps = 100, rarefaction = FALSE, dimensions, } } - ## VERBOSE - check.class(verbose, "logical") - ## BOOT.TYPE check.class(boot.type, "character") boot.type <- tolower(boot.type) check.length(boot.type, 1, " must be a single character string") ## Must be one of these methods - check.method(boot.type, c("full", "single"), "boot.type") + check.method(boot.type, c("full", "single", "null"), "boot.type") ## Change boot type to full if single and multiple trees if(boot.type == "single" && has_multiple_trees) { @@ -292,6 +319,14 @@ boot.matrix <- function(data, bootstraps = 100, rarefaction = FALSE, dimensions, } else { boot.type.fun <- boot.single } + }, + "null" = { + if(probabilistic_subsets) { + boot.type.fun <- boot.null #TODO: needs to be boot.null.proba + warning("Bootstrap with the null algorithm not implemented for probabilities. Please remind the maintainer to eventually do it!") + } else { + boot.type.fun <- boot.null + } } ) @@ -301,7 +336,7 @@ boot.matrix <- function(data, bootstraps = 100, rarefaction = FALSE, dimensions, ## RM.LAST.AXIS ## If TRUE, set automatic threshold at 0.95 - if(!missing(dimensions)) { + if(!is.null(dimensions)) { ## Else must be a single numeric value (proportional) check.class(dimensions, c("numeric", "integer"), " must be a proportional threshold value.") if(length(dimensions == 1)) { @@ -343,7 +378,7 @@ boot.matrix <- function(data, bootstraps = 100, rarefaction = FALSE, dimensions, lapply( ## Opens 3 data$subsets, ## Fun 3: Split the data per tree - split.subsets, n_trees = n_trees), + do.split.subsets, n_trees = n_trees), ## Fun 2: Apply the bootstraps lapply, bootstrap.wrapper, bootstraps_per_tree, rarefaction, boot.type.fun, verbose), ## Fun 1: Merge into one normal bootstrap table @@ -351,7 +386,7 @@ boot.matrix <- function(data, bootstraps = 100, rarefaction = FALSE, dimensions, ) } else { ## Bootstrap the data set - bootstrap_results <- lapply(data$subsets, bootstrap.wrapper, bootstraps, rarefaction, boot.type.fun, verbose) + bootstrap_results <- lapply(data$subsets, bootstrap.wrapper, bootstraps, rarefaction, boot.type.fun, verbose, all.elements = 1:dim(data$matrix[[1]])[1]) } if(verbose) message("Done.", appendLF = FALSE) diff --git a/R/boot.matrix_fun.R b/R/boot.matrix_fun.R index b1bf1039..bf460f55 100755 --- a/R/boot.matrix_fun.R +++ b/R/boot.matrix_fun.R @@ -16,14 +16,18 @@ elements.sampler <- function(elements) { return(do.call(cbind, lapply(set_samples, function(x) apply(elements[,x, drop = FALSE] , 1, sampler)))) } +## Null bootstrap replacement +boot.null <- function(elements, rarefaction, all.elements) { + return(sample(all.elements, rarefaction, replace = TRUE)) +} ## Full bootstrap replacement -boot.full <- function(elements, rarefaction) { +boot.full <- function(elements, rarefaction, all.elements) { return(sample(na.omit(elements), rarefaction, replace = TRUE)) } ## Proba version -boot.full.proba <- function(elements, rarefaction) { +boot.full.proba <- function(elements, rarefaction, all.elements) { if(is.na(elements[1,2])) { ## Simple sampling return(sample(elements[,1], rarefaction, prob = elements[,3], replace = TRUE)) @@ -35,7 +39,7 @@ boot.full.proba <- function(elements, rarefaction) { ## Single bootstrap: for each bootstrap, select one row and replace it by a ## randomly chosen remaining row (for n rows, only one row can be present twice). -boot.single <- function(elements, rarefaction, ...) { +boot.single <- function(elements, rarefaction, all.elements, ...) { ## Rarefy the data rarefied_sample <- sample(elements, rarefaction, replace = FALSE) ## Select the row to remove @@ -47,7 +51,7 @@ boot.single <- function(elements, rarefaction, ...) { } ## Proba version -boot.single.proba <- function(elements, rarefaction) { +boot.single.proba <- function(elements, rarefaction, all.elements) { if(is.na(elements[1,2])) { ## Simple sampling (rarefy) rarefied_sample <- sample(elements[,1], rarefaction, replace = FALSE) @@ -65,7 +69,7 @@ boot.single.proba <- function(elements, rarefaction) { } ## Performs bootstrap on one subsets and all rarefaction levels -replicate.bootstraps <- function(rarefaction, bootstraps, subsets, boot.type.fun) { +replicate.bootstraps <- function(rarefaction, bootstraps, subsets, boot.type.fun, all.elements) { verbose_place_holder <- FALSE if(nrow(subsets$elements) == 1) { if(length(subsets$elements) > 1) { @@ -77,17 +81,17 @@ replicate.bootstraps <- function(rarefaction, bootstraps, subsets, boot.type.fun } } else { ## Normal bootstrap (sample through the elements matrix) - return(replicate(bootstraps, boot.type.fun(subsets$elements, rarefaction))) + return(replicate(bootstraps, boot.type.fun(subsets$elements, rarefaction, all.elements))) } } ## Performs bootstrap on multiple subsets and all rarefaction levels -bootstrap.wrapper <- function(subsets, bootstraps, rarefaction, boot.type.fun, verbose) { +bootstrap.wrapper <- function(subsets, bootstraps, rarefaction, boot.type.fun, verbose, all.elements) { if(verbose) { ## Making the verbose version of disparity.bootstraps body(replicate.bootstraps)[[2]] <- substitute(message(".", appendLF = FALSE)) } - return(lapply(select.rarefaction(subsets, rarefaction), replicate.bootstraps, bootstraps, subsets, boot.type.fun)) + return(lapply(select.rarefaction(subsets, rarefaction), replicate.bootstraps, bootstraps, subsets, boot.type.fun, all.elements)) } ## Rarefaction levels selection @@ -101,7 +105,7 @@ combine.bootstraps <- function(one_bs_result, one_subsets) { } ## Split the subsets -split.subsets <- function(one_subset, n_trees) { +do.split.subsets <- function(one_subset, n_trees) { ## split the whole dataset ncol_out <- ncol(one_subset$elements)/n_trees splitted <- lapply( diff --git a/R/char.diff.R b/R/char.diff.R index e88e9eda..50eec2cf 100755 --- a/R/char.diff.R +++ b/R/char.diff.R @@ -2,7 +2,7 @@ #' #' @description Calculates the character difference from a discrete matrix #' -#' @param matrix A discrete matrix or a list containing discrete characters. The differences is calculated between the columns (usually characters). Use \code{t(matrix)} to calculate the differences between the rows. +#' @param matrix A discrete matrix or a list containing discrete characters. The differences is calculated between the columns (usually characters). Use \code{t(matrix)} or \code{by.col = FALSE} to calculate the differences between the rows. #' @param method The method to measure difference: \code{"hamming"} (default; Hamming 1950), \code{"manhattan"}, \code{"comparable"}, \code{"euclidean"}, \code{"maximum"}, \code{"mord"} (Lloyd 2016), \code{"none"} or \code{"binary"}. #' @param translate \code{logical}, whether to translate the characters following the \emph{xyz} notation (\code{TRUE} - default; see details - Felsenstein 2004) or not (\code{FALSE}). Translation works for up to 26 tokens per character. #' @param special.tokens optional, a named \code{vector} of special tokens to be passed to \code{\link[base]{grep}} (make sure to protect the character with \code{"\\\\"}). By default \code{special.tokens <- c(missing = "\\\\?", inapplicable = "\\\\-", polymorphism = "\\\\&", uncertainty = "\\\\/")}. Note that \code{NA} values are not compared and that the symbol "@" is reserved and cannot be used. diff --git a/R/chrono.subsets.R b/R/chrono.subsets.R index 80773790..59d48283 100755 --- a/R/chrono.subsets.R +++ b/R/chrono.subsets.R @@ -4,12 +4,12 @@ #' @description Splits the data into a chronological (time) subsets list. #' #' @param data A \code{matrix} or a \code{list} of matrices. -#' @param tree A \code{phylo} or a \code{multiPhylo} object matching the data and with a \code{root.time} element. This argument can be left missing if \code{method = "discrete"} and all elements are present in the optional \code{FADLAD} argument. +#' @param tree \code{NULL} (default) or an optional \code{phylo} or \code{multiPhylo} object matching the data and with a \code{root.time} element. This argument can be left missing if \code{method = "discrete"} and all elements are present in the optional \code{FADLAD} argument. #' @param method The time subsampling method: either \code{"discrete"} (or \code{"d"}) or \code{"continuous"} (or \code{"c"}). #' @param time Either a single \code{integer} for the number of discrete or continuous samples or a \code{vector} containing the age of each sample. #' @param model One of the following models: \code{"acctran"}, \code{"deltran"}, \code{"random"}, \code{"proximity"}, \code{"equal.split"} or \code{"gradual.split"}. Is ignored if \code{method = "discrete"}. #' @param inc.nodes A \code{logical} value indicating whether nodes should be included in the time subsets. Is ignored if \code{method = "continuous"}. -#' @param FADLAD An optional \code{data.frame} containing the first and last occurrence data. +#' @param FADLAD \code{NULL} (default) or an optional \code{data.frame} or \code{list} of \code{data.frame}s containing the first and last occurrence data. #' @param verbose A \code{logical} value indicating whether to be verbose or not. Is ignored if \code{method = "discrete"}. #' @param t0 If \code{time} is a number of samples, whether to start the sampling from the \code{tree$root.time} (\code{TRUE}), or from the first sample containing at least three elements (\code{FALSE} - default) or from a fixed time point (if \code{t0} is a single \code{numeric} value). #' @param bind.data If \code{data} contains multiple matrices and \code{tree} contains the same number of trees, whether to bind the pairs of matrices and the trees (\code{TRUE}) or not (\code{FALSE} - default). @@ -95,16 +95,73 @@ # t0 = 5 # bind.data = TRUE -chrono.subsets <- function(data, tree, method, time, model, inc.nodes = FALSE, FADLAD, verbose = FALSE, t0 = FALSE, bind.data = FALSE) { +chrono.subsets <- function(data, tree = NULL, method, time, model, inc.nodes = FALSE, FADLAD = NULL, verbose = FALSE, t0 = FALSE, bind.data = FALSE) { match_call <- match.call() ## ---------------------- ## SANITIZING ## ---------------------- ## DATA - ## data must be a matrix or a list - data <- check.dispRity.data(data) + # data <- check.dispRity.data(data, returns = "matrix") + if(!is.null(tree)) { + data <- check.dispRity.data(data, tree, returns = c("matrix", "tree", "multi")) + } else { + data <- check.dispRity.data(data, returns = c("matrix", "multi")) + } + + ## VERBOSE + check.class(verbose, "logical") + + # If is multi lapply the stuff + if(((!is.null(data$call$dispRity.multi) && data$call$dispRity.multi) || data$multi)) { + ## Split the data + split_data <- dispRity.multi.split(data) + + ## Get only the matrices and/or the trees + matrices <- unlist(lapply(split_data, `[[`, "matrix"), recursive = FALSE) + + ## Get the trees + if(!is.null(split_data[[1]]$tree)) { + tree <- unlist(lapply(split_data, `[[`, "tree"), recursive = FALSE) + } else { + tree <- NULL + } + + ## Toggle bind data (each is now a pair of matrix + tree) + bind.data <- FALSE + + ## Toggle verbose (if required) + chrono.subsets.call <- chrono.subsets + if(verbose) { + ## Changing the chrono.subsets function name (verbose line edited out) + ## Find the verbose lines + start_verbose <- which(as.character(body(chrono.subsets.call)) == "if (method == \"discrete\") {\n chrono.subsets.fun <- chrono.subsets.discrete\n if (verbose) \n message(\"Creating \", length(time) - 1, \" time bins through time:\", appendLF = FALSE)\n} else {\n chrono.subsets.fun <- chrono.subsets.continuous\n if (verbose) \n message(\"Creating \", length(time), \" time samples through \", ifelse(length(tree) > 1, paste0(length(tree), \" trees:\"), \"one tree:\"), appendLF = FALSE)\n}") + end_verbose <- which(as.character(body(chrono.subsets.call)) == "if (verbose) message(\"Done.\\n\", appendLF = FALSE)") + + ## Blank out the lines + body(chrono.subsets.call)[[start_verbose]][[3]][[3]] <- body(chrono.subsets.call)[[start_verbose]][[4]][[3]] <- body(chrono.subsets.call)[[end_verbose]] <- substitute(empty_line <- NULL) + } + + ## Apply the custom.subsets + if(method == "discrete") { + if(verbose) message("Creating ", length(time)-1, " time bins through time:", appendLF = FALSE) + } else { + if(verbose) message("Creating ", length(time), " time samples through ", length(matrices), " trees and matrices:", appendLF = FALSE) + } + + output <- dispRity.multi.apply(matrices, fun = chrono.subsets.call, tree = tree, method = method, time = time, model = model, inc.nodes = inc.nodes, FADLAD = FADLAD, verbose = verbose, t0 = t0, bind.data = bind.data) + + if(verbose) message("Done.\n", appendLF = FALSE) + return(output) + + } else { + if(!is.null(tree)) { + tree <- data$tree + } + data <- data$matrix + } + ## Check whether it is a distance matrix if(check.dist.matrix(data[[1]], just.check = TRUE)) { warning("chrono.subsets is applied on what seems to be a distance matrix.\nThe resulting matrices won't be distance matrices anymore!", call. = FALSE) @@ -115,19 +172,17 @@ chrono.subsets <- function(data, tree, method, time, model, inc.nodes = FALSE, F ## TREE (1) ## tree must be a phylo object - if(!missing(tree)) { + if(!is.null(tree)) { tree_class <- check.class(tree, c("phylo", "multiPhylo")) - is_multiPhylo <- ifelse(tree_class == "multiPhylo", TRUE, FALSE) + is_multiPhylo <- ifelse((tree_class == "multiPhylo" && length(tree) > 1), TRUE, FALSE) ## Make the tree into a single multiPhylo object - if(!is_multiPhylo) { - tree <- list(tree) - class(tree) <- "multiPhylo" - } else { - ## Check if all the trees are the same - tips <- lapply(tree, function(x) x$tip.label) - if(!all(unique(unlist(tips)) %in% tips[[1]])) { - stop.call(match_call$tree, msg.pre = "The trees in ", msg = " must have the same tip labels.") - } + + ## Check if all the trees are the same + tips <- lapply(tree, function(x) x$tip.label) + if(!all(unique(unlist(tips)) %in% tips[[1]])) { + stop.call(match_call$tree, msg.pre = "The trees in ", msg = " must have the same tip labels.") + } + if(inc.nodes) { nodes <- lapply(tree, function(x) x$node.label) unique_nodes <- unique(unlist(nodes)) if(is.null(unique_nodes) || !all(unique_nodes %in% nodes[[1]])) { @@ -173,17 +228,8 @@ chrono.subsets <- function(data, tree, method, time, model, inc.nodes = FALSE, F ## tree.age_tree variable declaration tree.age_tree <- lapply(tree, tree.age) } else { - ## Default tree list is_multiPhylo <- FALSE - - # ## If the data has a tree attached, use that one - # TG: this is not supposed to happen - # if(!is.null(data$tree[[1]])) { - # tree <- data$tree - # tree.age_tree <- lapply(tree, tree.age) - # is_multiPhylo <- length(data$tree) > 1 - # } } ## METHOD @@ -200,8 +246,8 @@ chrono.subsets <- function(data, tree, method, time, model, inc.nodes = FALSE, F if(method == "c") method <- "continuous" ## If the tree is missing, the method can intake a star tree (i.e. no phylogeny) - if(missing(tree)) { - if(missing(FADLAD)) { + if(is.null(tree)) { + if(is.null(FADLAD)) { stop.call("", "If no phylogeny is provided, all elements must be present in the FADLAD argument.") } if(method == "continuous") { @@ -347,7 +393,7 @@ chrono.subsets <- function(data, tree, method, time, model, inc.nodes = FALSE, F ## If FADLAD is missing, set it to NULL (skipped in the chrono.subsets.fun) ## Remove adjust FADLAD and associated functions from the whole package - if(missing(FADLAD)) { + if(is.null(FADLAD)) { if(method != "continuous") { ## If missing, create the FADLAD table make.fadlad <- function(tree.age_tree, Ntip_tree) { @@ -403,10 +449,6 @@ chrono.subsets <- function(data, tree, method, time, model, inc.nodes = FALSE, F } } - - ## VERBOSE - check.class(verbose, "logical") - ## ------------------------------- ## GENRATING THE TIME subsets ## ------------------------------- @@ -457,5 +499,4 @@ chrono.subsets <- function(data, tree, method, time, model, inc.nodes = FALSE, F } else { return(make.dispRity(data = data, call = list("subsets" = c(method, model, "trees" = length(tree), "matrices" = length(data), "bind" = bind.data)), subsets = time_subsets)) } - } diff --git a/R/chrono.subsets_fun.R b/R/chrono.subsets_fun.R index 96dc61a2..9743509d 100755 --- a/R/chrono.subsets_fun.R +++ b/R/chrono.subsets_fun.R @@ -117,7 +117,7 @@ make.origin.subsets <- function(data) { } ## cbind with missing data -cbind.fill <- function(x, y) { +do.cbind.fill <- function(x, y) { ## Check the number of rows if(dim(x)[1] == dim(y)[1]) { ## Simple cbind @@ -146,11 +146,11 @@ cbind.fill <- function(x, y) { recursive.combine.list <- function(list) { if(length(list) == 2) { ## Do cbind on the two elements of the list - return(mapply(function(x,y) cbind.fill(x$elements, y$elements), + return(mapply(function(x,y) do.cbind.fill(x$elements, y$elements), list[[1]], list[[length(list)]], SIMPLIFY = FALSE)) } else { ## Do cbind on the first and last elements of the list - list[[1]] <- mapply(function(x,y) cbind.fill(x$elements, y$elements), + list[[1]] <- mapply(function(x,y) do.cbind.fill(x$elements, y$elements), list[[1]], list[[length(list)]], SIMPLIFY = FALSE) ## Remove the last element of the list list[[length(list)]] <- NULL diff --git a/R/clean.data.R b/R/clean.data.R index ecbd246c..703a3635 100755 --- a/R/clean.data.R +++ b/R/clean.data.R @@ -50,7 +50,14 @@ clean.data <- function(data, tree, inc.nodes = FALSE) { } ## tree - tree_class <- check.class(tree, c("phylo", "multiPhylo"), " must be a phylo or multiPhylo object.") + tree_class <- check.class(tree, c("phylo", "multiPhylo", "list"), " must be a phylo or multiPhylo object.") + if(tree_class == "list") { + ## Check if all phylo + tree_class <- unique(unlist(lapply(tree, is))) + if(length(tree_class) > 1 || tree_class[1] != "phylo") { + stop.call(call = match_call$tree, msg = " must be a phylo or multiPhylo object.") + } + } ## inc.nodes check.class(inc.nodes, "logical") @@ -88,7 +95,7 @@ clean.data <- function(data, tree, inc.nodes = FALSE) { for(i in 1:length(wrongs_trees)) { error_msg <- c(error_msg, paste0("Node", ifelse(length(wrongs_nodes[[i]]) > 1, "s ", " "), paste(wrongs_nodes[[i]], collapse = ", "), " from tree ", wrongs_trees[[i]]," not found in the data.")) } - stop(paste(error_msg, "(nodes cannot be trimmed automatically).") , call. = FALSE) + stop(paste(error_msg, "Nodes cannot be trimmed automatically. You can try using the following to remove them\n my_tree$node.labels <- NULL") , call. = FALSE) } ## Selecting the tips to drop @@ -136,7 +143,7 @@ clean.data <- function(data, tree, inc.nodes = FALSE) { } if(!is(cleaned_data, "list")) { - stop(paste0("Node", ifelse(length(cleaned_data) > 1, "s ", " "), paste(cleaned_data, collapse = ", "), " not found in the data (nodes cannot be trimmed automatically)."), call. = FALSE) + stop(paste0("Node", ifelse(length(cleaned_data) > 1, "s ", " "), paste(cleaned_data, collapse = ", "), " not found in the data. Nodes cannot be trimmed automatically. You can try using the following to remove them\n my_tree$node.labels <- NULL"), call. = FALSE) } else { return(cleaned_data) } diff --git a/R/covar.plot.R b/R/covar.plot.R index 1be8b3e4..ea86531c 100755 --- a/R/covar.plot.R +++ b/R/covar.plot.R @@ -26,7 +26,7 @@ #' \itemize{ #' \item A \code{function} to calculate the centre from a group like the default \code{colMeans} function that calculates the centroid coordinates of each group; #' \item A \code{numeric} value to be replicated as the coordinates for the centre of each group (e.g. \code{centres = 0} sets all the centres at the coordinates \code{c(0,0,0,...)}); or a vector of numeric values to be directly used as the coordinates for each group (e.g. \code{centres = c(1,2,3)} sets all the centres at the coordinates \code{c(1,2,3)}); or a list of numeric values or numeric vectors to be used as the coordinates for the centres of each group; -#' \item code{"intercept"} for using the estimated posterior intercept for each sample. +#' \item \code{"intercept"} for using the estimated posterior intercept for each sample. #' } #' #' \emph{NOTE} that if the input contains more dimensions than the visualised dimensions (by default \code{dimensions = c(1,2)}) the ellipses and major axes are projected from an n-dimensional space onto a 2D space which might make them look incorrect. @@ -136,7 +136,7 @@ covar.plot <- function(data, n, points = TRUE, major.axes = FALSE, ellipses = FA ## Scaling the VCVs if(do_scale) { scale_VCV <- covars[[scale]] - covars <- lapply(covars, function(one_covar, scale) mapply(scale.VCV, one_covar, scale, SIMPLIFY = FALSE), scale = scale_VCV) + covars <- lapply(covars, function(one_covar, scale) mapply(VCV.scale, one_covar, scale, SIMPLIFY = FALSE), scale = scale_VCV) } ## Measuring the axes @@ -187,17 +187,43 @@ covar.plot <- function(data, n, points = TRUE, major.axes = FALSE, ellipses = FA } ## Get the plot limits - lims <- range(data$matrix[[1]], na.rm = TRUE) + ylims <- xlims <- c(-0, 0) + if(points) { + point_xlims <- range(c(data$matrix[[1]][, dimensions[1]]), na.rm = TRUE) + point_ylims <- range(c(data$matrix[[1]][, dimensions[2]]), na.rm = TRUE) + xlims <- range(c(xlims, point_xlims)) + ylims <- range(c(ylims, point_ylims)) + } if(do_major_axes) { - lims <- max(range(c(range(abs(data$matrix[[1]])), abs(unlist(all_axes))))) - lims <- c(-lims, lims) + axes_xlims <- range(unlist(lapply(all_axes, lapply, function(x, dim) return(x[, dim]), dim = dimensions[1]))) + axes_ylims <- range(unlist(lapply(all_axes, lapply, function(x, dim) return(x[, dim]), dim = dimensions[2]))) + xlims <- range(c(xlims, axes_xlims)) + ylims <- range(c(ylims, axes_ylims)) } if(do_ellipses) { - lims <- max(c(range(abs(data$matrix[[1]])), range(abs(unlist(all_ellipses))))) - lims <- c(-lims, lims) - } - plot_args <- get.dots(plot_args, plot_args, "xlim", lims) - plot_args <- get.dots(plot_args, plot_args, "ylim", lims) + ellipses_xlims <- range(unlist(lapply(all_ellipses, lapply, function(x, dim) return(x[, dim]), dim = dimensions[1]))) + ellipses_ylims <- range(unlist(lapply(all_ellipses, lapply, function(x, dim) return(x[, dim]), dim = dimensions[2]))) + xlims <- range(c(xlims, ellipses_xlims)) + ylims <- range(c(ylims, ellipses_ylims)) + } + + ## Get the x centre of the plot + centre_x <- mean(xlims) + ## Get the y centre of the plit + centre_y <- mean(ylims) + + ## Center the limits on the range + if(diff(xlims) > diff(ylims)) { + ## Large range is x, scale and centre the ylims on it + ylims <- c(centre_y - diff(xlims)/2, centre_y + diff(xlims)/2) + } + if(diff(ylims) < diff(xlims)) { + ## Large range is y, scale and centre the xlims on it + xlims <- c(centre_x - diff(ylims)/2, centre_x + diff(ylims)/2) + } + + plot_args <- get.dots(plot_args, plot_args, "xlim", xlims) + plot_args <- get.dots(plot_args, plot_args, "ylim", ylims) ## Setting the x/y labels percentage <- apply(data$matrix[[1]], 2, var) diff --git a/R/covar.plot_fun.R b/R/covar.plot_fun.R index 33516584..88b71134 100755 --- a/R/covar.plot_fun.R +++ b/R/covar.plot_fun.R @@ -68,7 +68,7 @@ VCV.cent.tend <- function(one_covar, fun) { } ## Scale a VCV matrix to another one -scale.VCV <- function(VCV1, VCV2) { +VCV.scale <- function(VCV1, VCV2) { ## Dividing both VCVs ## Getting the off diagonal (the scaling ratio) diff --git a/R/crown.stem.R b/R/crown.stem.R index 6fe1663a..2a636020 100755 --- a/R/crown.stem.R +++ b/R/crown.stem.R @@ -2,7 +2,7 @@ #' #' @description Selects the crown #' -#' @param tree a code{"phylo"} object +#' @param tree a \code{"phylo"} object #' @param inc.nodes whether to include the nodes (\code{TRUE}; default) or not (\code{FALSE}) in the output. #' @param output.names whether to output the taxa names (\code{TRUE}; default) or two phylogenetic trees (\code{FALSE}). #' diff --git a/R/custom.subsets.R b/R/custom.subsets.R index c3ed5b5e..9f2aaef4 100755 --- a/R/custom.subsets.R +++ b/R/custom.subsets.R @@ -5,7 +5,7 @@ #' #' @param data A \code{matrix} or a \code{list} of matrices. #' @param group Either a \code{list} of row numbers or names to be used as different groups, a \code{data.frame} with the same \eqn{k} elements as in \code{data} as rownames or a \code{factor} vector. If \code{group} is a \code{phylo} object matching \code{data}, groups are automatically generated as clades (and the tree is attached to the resulting \code{dispRity} object). -#' @param tree Optional, a \code{phylo} or \code{multiPhylo} object to attach to the resulting \code{dispRity} data. +#' @param tree \code{NULL} (default) or an optional \code{phylo} or \code{multiPhylo} object to be attached to the data. #' #' @details #' Note that every element from the input data can be assigned to multiple groups! @@ -50,7 +50,7 @@ # group2 <- list("A" = c("a", "b", "c", "d"), "B" = c(letters[5:10])) # group3 <- as.data.frame(matrix(data = c(rep(1,5), rep(2,5)), nrow = 10, ncol = 1, dimnames = list(letters[1:10]))) -custom.subsets <- function(data, group, tree) { +custom.subsets <- function(data, group, tree = NULL) { ## Saving the call match_call <- match.call() @@ -60,7 +60,32 @@ custom.subsets <- function(data, group, tree) { ## ---------------------- ## DATA ## data must be a matrix - data <- check.dispRity.data(data) + if(!is.null(tree)) { + data <- check.dispRity.data(data, tree, returns = c("matrix", "tree", "multi")) + } else { + data <- check.dispRity.data(data, returns = c("matrix", "multi")) + } + + ## If is multi lapply the stuff + if(((!is.null(data$call$dispRity.multi) && data$call$dispRity.multi) || data$multi)) { + ## Split the data + split_data <- dispRity.multi.split(data) + ## Get only the matrices and/or the trees + matrices <- unlist(lapply(split_data, `[[`, "matrix"), recursive = FALSE) + ## Get the trees + if(!is.null(split_data[[1]]$tree)) { + tree <- unlist(lapply(split_data, `[[`, "tree"), recursive = FALSE) + } else { + tree <- NULL + } + ## Apply the custom.subsets + return(dispRity.multi.apply(matrices, fun = custom.subsets, group = group, tree = tree)) + } else { + if(!is.null(tree)) { + tree <- data$tree + } + data <- data$matrix + } ## Check whether it is a distance matrix if(check.dist.matrix(data[[1]], just.check = TRUE)) { @@ -91,7 +116,7 @@ custom.subsets <- function(data, group, tree) { subsets_list <- lapply(group_list, function(x) list(elements = matrix(x, ncol = 1))) ## Attach the tree - if(group_class == "phylo" || !missing(tree)) { + if(group_class == "phylo" || !is.null(tree)) { ## Output as a dispRity object (with tree) return(make.dispRity(data = data, call = list("subsets" = "customised"), subsets = subsets_list, tree = tree)) } else { diff --git a/R/dispRity-package.R b/R/dispRity-package.R index d1dae112..ae7983d4 100755 --- a/R/dispRity-package.R +++ b/R/dispRity-package.R @@ -26,7 +26,7 @@ NULL #' } #' #' @format three matrices and one phylogenetic tree. -#' @source \url{https://www.royalsocietypublishing.org/doi/abs/10.1098/rspb.2014.1278} +#' @doi \url{https://doi.org/10.1098/rspb.2014.1278} #' @references Beck RMD & Lee MSY. 2014. Ancient dates or accelerated rates? #' Morphological clocks and the antiquity of placental mammals. #' Proc. R. Soc. B 2014 281 20141278; DOI: 10.1098/rspb.2014.1278 diff --git a/R/dispRity.R b/R/dispRity.R index fa1fe019..1cb2f02e 100755 --- a/R/dispRity.R +++ b/R/dispRity.R @@ -123,7 +123,7 @@ # start_mem <- mem_used() -dispRity <- function(data, metric, dimensions, ..., between.groups = FALSE, verbose = FALSE, tree = NULL){#, parallel) { +dispRity <- function(data, metric, dimensions = NULL, ..., between.groups = FALSE, verbose = FALSE, tree = NULL){#, parallel) { ## ---------------------- ## SANITIZING ## ---------------------- @@ -134,32 +134,104 @@ dispRity <- function(data, metric, dimensions, ..., between.groups = FALSE, verb # warning("DEBUG") ; return(match_call) ## Check data input + is_multi <- FALSE if(!is(data, "dispRity")) { ## Adding the tree if(!is.null(tree)) { - data <- fill.dispRity(make.dispRity(data = check.dispRity.data(data), tree = tree)) + data_check <- check.dispRity.data(data, tree, returns = c("matrix", "tree", "multi")) + is_multi <- data_check$multi + data <- fill.dispRity(make.dispRity(data = data_check$matrix, tree = data_check$tree), check = FALSE) } else { - data <- fill.dispRity(make.dispRity(data = check.dispRity.data(data))) + data_check <- check.dispRity.data(data, returns = c("matrix", "multi")) + is_multi <- data_check$multi + data <- fill.dispRity(make.dispRity(data = data_check$matrix), check = FALSE) } - } else { - ## Make sure that data is not a dual class - if(length(class(data)) > 1) { - stop.call(match_call$data, " must be a raw dispRity object (i.e. not dual class).") + if(is_multi) { + data$call$dispRity.multi <- is_multi } - ## Making sure matrix exist - if(is.null(data$matrix[[1]])) { - stop.call(match_call$data, " must contain a matrix or a list of matrices.") + } else { + if(!is(data, "multi")) { + ## Make sure that data is not a dual class + if(length(class(data)) > 1) { + stop.call(match_call$data, " must be a raw dispRity object (i.e. not dual class).") + } + ## Making sure matrix exist + if(is.null(data$matrix[[1]])) { + stop.call(match_call$data, " must contain a matrix or a list of matrices.") + } } + ## Adding tree (if possible) if(!is.null(tree)) { data <- remove.tree(data) - data <- add.tree(data, tree = check.dispRity.tree(tree, data = data)) + data <- add.tree(data, tree = check.dispRity.data(data = data, tree = tree, returns = "tree")) + } + + ## Togggle multi? + if(is(data, "dispRity") && is(data, "multi")) { + is_multi <- TRUE + } else { + ## Fill in dimensionality + if(is.null(data$call$dimensions)) { + data$call$dimensions <- 1:ncol(data$matrix[[1]]) + } + } + } + + ## dispRity.multi + if(is_multi) { + + ## Check if data needs splitting (if not *.subsets or boot.matrix) + do_split <- !(is(data, "dispRity") && is(data, "multi")) + + if(do_split) { + ## Split the data + split_data <- dispRity.multi.split(data) + data$call$dispRity.multi <- TRUE + ## Get only the matrices and/or the trees + matrices <- unlist(lapply(split_data, `[[`, "matrix"), recursive = FALSE) + ## Get the trees + if(!is.null(split_data[[1]]$tree)) { + tree <- unlist(lapply(split_data, `[[`, "tree"), recursive = FALSE) + } else { + tree <- NULL + } + } else { + ## Get the first element in data as a template + split_data <- data + data <- dispRity.multi.merge.data(data) + ## Get the correct elements + matrices <- split_data[which(unlist(lapply(split_data, class)) == "dispRity")] + tree <- NULL } - ## Make sure dimensions exist in the call - if(is.null(data$call$dimensions)) { - data$call$dimensions <- 1:ncol(data$matrix[[1]]) + ## Change the call in dispRity (if verbose) + dispRity.call <- dispRity + if(verbose) { + ## Changing the dispRit yfunction name (verbose line edited out) + ## Find the verbose lines + start_verbose <- which(as.character(body(dispRity.call)) == "if (verbose) message(\"Calculating disparity\", appendLF = FALSE)") + end_verbose <- which(as.character(body(dispRity.call)) == "if (verbose) message(\"Done.\\n\", appendLF = FALSE)") + + ## Comment out both lines + body(dispRity.call)[[start_verbose]] <- body(dispRity.call)[[end_verbose]] <- substitute(empty_line <- NULL) + } + ## Set up the function to call + dispRity.int.call <- function(data, tree, metric, dimensions, between.groups, verbose, ...) { + return(dispRity.call(data = data, metric = metric, dimensions = dimensions, ..., between.groups = between.groups, verbose = verbose, tree = tree)) } + + ## Run the apply + if(verbose) message("Calculating multiple disparities", appendLF = FALSE) + + output <- dispRity.multi.apply(matrices, fun = dispRity.int.call, metric = metric, tree = tree, dimensions = dimensions, between.groups = between.groups, verbose = verbose, ...) + # output <- dispRity.multi.apply(matrices, fun = dispRity.int.call, metric = metric, trees = trees, dimensions = dimensions, between.groups = between.groups, verbose = verbose) ; warning("DEBUG") + # test <- dispRity.int.call(matrices[[1]], trees[[1]], metric = metric, dimensions = dimensions, between.groups = between.groups, verbose = verbose) ; warning("DEBUG") + + if(verbose) message("Done.\n", appendLF = FALSE) + + ## Return the merged results + return(dispRity.multi.merge(data, output, match_call)) } ## Get the metric list @@ -212,7 +284,7 @@ dispRity <- function(data, metric, dimensions, ..., between.groups = FALSE, verb has_probabilities <- ifelse(length(grep("\\.split", data$call$subsets)) == 0, FALSE, TRUE) ## Dimensions - if(!missing(dimensions)) { + if(!is.null(dimensions)) { ## Else must be a single numeric value (proportional) check.class(dimensions, c("numeric", "integer"), " must be a proportional threshold value.") if(length(dimensions) == 1) { @@ -404,19 +476,19 @@ dispRity <- function(data, metric, dimensions, ..., between.groups = FALSE, verb c(## Data is bound to a tree is_bound, ## Data has multiple matrices and the metric needs matrix decomp - length(data$matrix) > 1 && matrix_decomposition && is.null(data$call$subsets["trees"]), + length(data$matrix) > 1 && matrix_decomposition && (is.null(data$call$subsets["trees"]) || is.na(data$call$subsets["trees"])), ## Data has multiple trees and the metric needs a tree length(data$tree) > 1 && any(metric_has_tree) ) )) { ## Make the lapply loops - n_trees <- ifelse(is.null(data$call$subsets["trees"]), 1, as.numeric(data$call$subsets["trees"])) + n_trees <- ifelse((is.null(data$call$subsets["trees"]) || is.na(data$call$subsets["trees"])), 1, as.numeric(data$call$subsets["trees"])) ## Splitting the lapply loop for bound trees - lapply_loops <- split.lapply_loop(lapply_loop, n_trees) + lapply_loops <- lapply_loop.split(lapply_loop, n_trees) ## Make the matrix list - splitted_data <- split.data(data) + splitted_data <- bound.data.split(data) splitted_data[[1]]$call$dimensions @@ -424,7 +496,7 @@ dispRity <- function(data, metric, dimensions, ..., between.groups = FALSE, verb disparities <- mapply(mapply.wrapper, lapply_loops, splitted_data, MoreArgs = list(metrics_list, matrix_decomposition, verbose, metric_has_tree, ...), SIMPLIFY = FALSE) - # disparities <- mapply(mapply.wrapper, lapply_loops, matrices_data, MoreArgs = list(metrics_list, matrix_decomposition, verbose, metric_has_tree), SIMPLIFY = FALSE) ; warning("DEBUG dispRity") + # disparities <- mapply(mapply.wrapper, lapply_loops, splitted_data, MoreArgs = list(metrics_list, matrix_decomposition, verbose, metric_has_tree), SIMPLIFY = FALSE) ; warning("DEBUG dispRity") ## Reformat to normal disparity object disparity <- unlist(lapply(as.list(1:ifelse(is.null(data$call$subsets["trees"]), n_trees, length(disparities[[1]]))), @@ -435,6 +507,9 @@ dispRity <- function(data, metric, dimensions, ..., between.groups = FALSE, verb ## Normal disparity lapply disparity <- lapply(lapply_loop, lapply.wrapper, metrics_list, data, matrix_decomposition, verbose, metric_has_tree, ...) #TG: check out the file disparity_internal_logic.md (located on the root of the package) for explanation about the logic in this lapply + + ## If multiple matrices, split the resulting output into columns + } # } diff --git a/R/dispRity.covar.projections.R b/R/dispRity.covar.projections.R index d6bab666..c91a8926 100755 --- a/R/dispRity.covar.projections.R +++ b/R/dispRity.covar.projections.R @@ -9,9 +9,10 @@ #' @param n optional, a random number of covariance matrices to sample (if left empty, all are used). #' @param major.axis which major axis to use (default is \code{1}; see \code{\link{axis.covar}} for more details). #' @param level the confidence interval to estimate the major axis (default is \code{0.95}; see \code{\link{axis.covar}} for more details)). -#' @param output which values to output from the projection. By default, the three values \code{c("position", "distance", "degree")} are used to respectively output the projection, rejection and angle values (see \code{\link{projections}} for more details). +#' @param output which values to output from the projection. By default, the three values \code{c("position", "distance", "degree")} are used to respectively output the projection, rejection and angle values (see \code{\link{projections}} for more details). The argument \code{"orthogonality"} can also be added to this vector. #' @param inc.base logical, when using \code{type = "elements"} with a supplied \code{base} argument, whether to also calculate the projections for the base group (\code{TRUE}) or not (\code{FALSE}; default). -#' @param ... any optional arguments to pass to \code{\link{projections}} (such as \code{centre} or \code{abs}). +# @param distance.method which method to use to calculate the distance (rejection). Can be either \code{"euclidean"} (default) or \code{"CI"} to change the unit vector to either the projection of the confidence interval (see details). +#' @param ... any optional arguments to pass to \code{\link{projections}} (such as \code{centre} or \code{abs}). \emph{NOTE that this function uses by default \code{centre = TRUE} and \code{abs = TRUE} which are not the defaults for \code{\link{projections}}}. #' @param verbose logical, whether to be verbose (\code{TRUE}) or not (\code{FALSE}, default). #' #' @details @@ -26,6 +27,12 @@ #' \item \code{type = "groups"} will run pairs elements each subset and \code{base} (instead of the full pairwise analyses). #' \item \code{type = "elements"} will run the projection of each subset onto the major axis from \code{base} rather than its own. #' } +#' +# If \code{output} contains \code{"distance"}, the \code{distance.method} allows for the two following calculations of the rejections: +# \itemize{ +# \item \code{"euclidean"} (default): calculates the distance values (rejections) as true euclidean distances in the space using typically the unit vector from the space (or from the rescaled space if the optional argument (\code{...}), \code{scale = TRUE} - default - is used). With this method, a rejection (\code{"distance"}) of 1 is at the same distance from the center of the space as a projection (\code{"position"}) of 1. +# \item \code{"CI"}: calculates the distance values (rejections) as non-euclidean distances but relative distances from the confidence interval (from the argument \code{level}). With this method, a rejection (\code{"distance"}) of 1 must be interpreted as a distance relative to the confidence interval ellipse: a value of equals that the rejection is on the confidence interval ellipse, and values above and below that value respectively mean within and without that confidence interval. With this method, a rejection of 1 is not at the same distance from the center of the space as a projection of 1 but are both on the same place relative to the confidence interval ellipse. +#} #' #' @returns #' A \code{list} of class \code{"dispRity"} and \code{"projection"} which contains \code{dispRity} objects corresponding to each projection value from \code{output}. @@ -63,7 +70,7 @@ #' @author Thomas Guillerme #' @export -dispRity.covar.projections <- function(data, type, base, sample, n, major.axis = 1, level = 0.95, output = c("position", "distance", "degree"), inc.base = FALSE, ..., verbose = FALSE) { +dispRity.covar.projections <- function(data, type, base, sample, n, major.axis = 1, level = 0.95, output = c("position", "distance", "degree"), inc.base = FALSE, ..., verbose = FALSE) { #distance.method = "euclidean" match_call <- match.call() @@ -85,7 +92,7 @@ dispRity.covar.projections <- function(data, type, base, sample, n, major.axis = } ## output - check.method(output, c("position", "distance", "degree"), "output must be") + check.method(output, c("position", "distance", "degree", "orthogonality"), "output must be") ## Check logicals check.class(verbose, "logical") diff --git a/R/dispRity.covar.projections_fun.R b/R/dispRity.covar.projections_fun.R index 1c777b60..b8e5ec89 100755 --- a/R/dispRity.covar.projections_fun.R +++ b/R/dispRity.covar.projections_fun.R @@ -1,85 +1,31 @@ ## Projection of elements on an axis projections.fast <- function(matrix, point1 = 0, point2 = colMeans(matrix), measure = "position", scale = TRUE, centre = TRUE, abs = TRUE) { - ## Get the point1 and point2 - if(length(point1) != ncol(matrix)) { - point1 <- rep(point1, ncol(matrix))[1:ncol(matrix)] - } - if(length(point2) != ncol(matrix)) { - point2 <- rep(point2, ncol(matrix))[1:ncol(matrix)] - } - - ## Get the base vector - base_vector <- rbind(point1, point2) - - ## Get all the space (with the two last rows being the base vectors) - space <- rbind(matrix, base_vector) - - ## Centre the matrix on point1 - if(sum(point1) != 0) { - ## Centre all the space - space <- space - rep(point1, rep.int(nrow(space), ncol(space))) - ## Re-attribute the centred variables - matrix <- space[1:nrow(matrix), , drop = FALSE] - base_vector <- space[-c(1:nrow(matrix)), , drop = FALSE] - } - - ## Scale the space - if(scale) { - ## The scaled space - space <- space/dist(space[-c(1:nrow(matrix)), , drop = FALSE]) - } - - ## Get the base vector axis (x) and the projection vector (former unit vector; y) - x <- base_vector[2, ] - y <- c(sqrt(sum(base_vector[2,]^2)), rep(0, (ncol(matrix)-1))) - ## If the base vector and the unit vector are different... - if(any(x != y)) { - ## ...rotate the matrix on the x-axis - space <- space %*% get.rotation.matrix(x, y) - } - - ## Re-attributing the matrix and the vector - matrix <- space[1:nrow(matrix), , drop = FALSE] - base_vector <- space[-c(1:nrow(matrix)), , drop = FALSE] - - ## Project the vectors - projections <- t(apply(matrix, 1, geometry::dot, y = base_vector[2,], d = 2)) - ## Calculate the angles - if("degree" %in% measure) { - angles <- t(t(apply(matrix, 1, vector.angle, base_vector[2,]))) - angles <- ifelse(is.nan(angles), 0, angles) + ## Get the projection + proj_results <- linear.algebra.projection(matrix, point1, point2, do_angle = any(measure %in% c("degree", "radian", "orthogonality")), scale = scale) + projections <- proj_results$projections + if(any(measure %in% c("degree", "radian", "orthogonality"))) { + angles <- proj_results$angles } # "position" #distance on # "distance" #distance from # "angle" #angle between - ## Measure the thingy + ## Measure the thingies values <- list() if("position" %in% measure) { - if(centre && abs) { - values[["position"]] <- abs(projections[,1] - 0.5)/0.5 - } - if(centre && !abs) { - values[["position"]] <- (projections[,1] - 0.5)/0.5 - } - if(!centre && abs) { - values[["position"]] <- abs(projections[,1]) - } - if(!centre && !abs) { - values[["position"]] <- projections[,1] - } + values[["position"]] <- correct.position(projections[, 1], centre, abs) } if("distance" %in% measure) { - values[["distance"]] <- apply(matrix - projections, 1, function(row) sqrt(sum(row^2))) - # if(centre) { - # values[["distance"]] <- values[["distance"]]/2 - # } + values[["distance"]] <- get.distance(proj_results$centred_matrix, projections) } if("degree" %in% measure) { values[["degree"]] <- angles[,1] } + if("orthogonality" %in% measure) { + values[["orthogonality"]] <- sapply(angles, orthogonise) + } ## DEBUG # values_out <<- values diff --git a/R/dispRity.fast.R b/R/dispRity.fast.R index c0e512ce..42a2f223 100755 --- a/R/dispRity.fast.R +++ b/R/dispRity.fast.R @@ -41,13 +41,13 @@ dispRity.fast <- function(group, space, metric, ...) { if(is.null(names(metric))) { return(metric[[1]](do.call(metric[[2]], args))) } - ## Handle the named arguments - args <- c(args, metric[-1]) + # ## Handle the named arguments + # args <- c(args, metric[-1]) - ## Level 1 metric + args - if(length(metric[[1]]) == 1) { - return(do.call(metric[[1]], args)) - } - ## Level 2 + 1 metric + args - return(metric[[1]][[1]](do.call(metric[[1]][[2]], args))) + # ## Level 1 metric + args + # if(length(metric[[1]]) == 1) { + # return(do.call(metric[[1]], args)) + # } + # ## Level 2 + 1 metric + args + # return(metric[[1]][[1]](do.call(metric[[1]][[2]], args))) } \ No newline at end of file diff --git a/R/dispRity.metric.R b/R/dispRity.metric.R index c65dcfa8..6ec18d41 100755 --- a/R/dispRity.metric.R +++ b/R/dispRity.metric.R @@ -1,5 +1,5 @@ #' @name dispRity.metric -#' @aliases dimension.level3.fun dimension.level2.fun dimension.level1.fun between.groups.fun variances ranges centroids mode.val ellipse.volume edge.length.tree convhull.surface convhull.volume diagonal ancestral.dist pairwise.dist span.tree.length n.ball.volume radius neighbours displacements quantiles func.eve func.div angles deviations group.dist point.dist projections projections.tree projections.between disalignment +#' @aliases dimension.level3.fun dimension.level2.fun dimension.level1.fun between.groups.fun variances ranges centroids mode.val ellipsoid.volume ellipse.volume edge.length.tree convhull.surface convhull.volume diagonal ancestral.dist pairwise.dist span.tree.length n.ball.volume radius neighbours displacements quantiles func.eve func.div angles deviations group.dist point.dist projections projections.tree projections.between disalignment roundness #' @title Disparity metrics #' #' @description Different implemented disparity metrics. @@ -11,7 +11,7 @@ #' #' @param matrix A matrix. #' @param matrix2 Optional, a second matrix for metrics between groups. -#' @param ... Optional arguments to be passed to the function. Usual optional arguments are \code{method} for specifying the method for calculating distance passed to \code{\link[vegan]{vegdist}} (e.g. \code{method = "euclidean"} - default - or \code{method = "manhattan"}) or \code{k.root} to scale the result using the eqn{kth} root. See details below for available optional arguments for each function. +#' @param ... Optional arguments to be passed to the function. Usual optional arguments are \code{method} for specifying the method for calculating distance passed to \code{\link[vegan]{vegdist}} (e.g. \code{method = "euclidean"} - default - or \code{method = "manhattan"}) or \code{k.root} to scale the result using the \eqn{kth} root. See details below for available optional arguments for each function. #' #' @details #' These are inbuilt functions for calculating disparity. See \code{\link{make.metric}} for details on \code{dimension.level3.fun}, \code{dimension.level2.fun}, \code{dimension.level1.fun} and \code{between.groups.fun}. The dimensions levels (1, 2 and 3) can be seen as similar to ranks in linear algebra. @@ -31,7 +31,7 @@ #' \item WARNING: This function is the generalisation of Pythagoras' theorem and thus \bold{works only if each dimensions are orthogonal to each other}. #' } #' -#' \item \code{ellipse.volume}: calculates the ellipsoid volume of a matrix. This function tries to determine the nature of the input matrix and uses one of these following methods to calculate the volume. You can always specify the method using \code{method = "my_choice"} to overrun the automatic method choice. +#' \item \code{ellipsoid.volume}: calculates the ellipsoid volume of a matrix. This function tries to determine the nature of the input matrix and uses one of these following methods to calculate the volume. You can always specify the method using \code{method = "my_choice"} to overrun the automatic method choice. #' \itemize{ #' \item \code{"eigen"}: this method directly calculates the eigen values from the input matrix (using \code{\link{eigen}}). This method is automatically selected if the input matrix is "distance like" (i.e. square with two mirrored triangles and a diagonal). #' \item \code{"pca"}: this method calculates the eigen values as the sum of the variances of the matrix (\code{abs(apply(var(matrix),2, sum))}). This is automatically selected if the input matrix is NOT "distance like". Note that this method is faster than \code{"eigen"} but only works if the input matrix is an ordinated matrix from a PCA, PCO, PCoA, NMDS or MDS. @@ -39,13 +39,15 @@ #' \item \code{}: finally, you can directly provide a numeric vector of eigen values. This method is never automatically selected and overrides any other options. #' } #' -#' \item \code{func.div}: The functional divergence (Vill'{e}ger et al. 2008): the ratio of deviation from the centroid (this is similar to \code{FD::dbFD()$FDiv}). +#' \item \code{func.div}: The functional divergence (Villeger et al. 2008): the ratio of deviation from the centroid (this is similar to \code{FD::dbFD()$FDiv}). #' -#' \item \code{func.eve}: The functional evenness (Vill'{e}ger et al. 2008): the minimal spanning tree distances evenness (this is similar to \code{FD::dbFD()$FEve}). If the matrix used is not a distance matrix, the distance method can be passed using, for example \code{method = "euclidean"} (default). +#' \item \code{func.eve}: The functional evenness (Villeger et al. 2008): the minimal spanning tree distances evenness (this is similar to \code{FD::dbFD()$FEve}). If the matrix used is not a distance matrix, the distance method can be passed using, for example \code{method = "euclidean"} (default). #' #' \item \code{mode.val}: calculates the modal value of a vector. #' #' \item \code{n.ball.volume}: calculate the volume of the minimum n-ball (if \code{sphere = TRUE}) or of the ellipsoid (if \code{sphere = FALSE}). +#' +#' \item \code{roundness}: calculate the roundness of an elliptical representation of a variance-covariance matrix as the integral of the ranked distribution of the major axes. A value of 1 indicates a sphere, a value between 1 and 0.5 indicates a more pancake like representation and a value between 0.5 and 0 a more cigar like representation. You can force the variance-covariance calculation by using the option \code{vcv = TRUE} (default) that will calculate the variance-covariance matrix if the input is not one. #' #' } #' @@ -57,7 +59,7 @@ #' #' \item \code{angles}: calculates the angles of the main axis of variation per dimension in a \code{matrix}. The angles are calculated using the least square algorithm from the \code{\link[stats]{lm}} function. The unit of the angle can be changed through the \code{unit} argument (either \code{"degree"} (default), \code{radian} or \code{slope}) and a base angle to measure the angle from can be passed through the \code{base} argument (by default \code{base = 0}, measuring the angle from the horizontal line (note that the \code{base} argument has to be passed in the same unit as \code{unit}). When estimating the slope through \code{\link[stats]{lm}}, you can use the option \code{significant} to only consider significant slopes (\code{TRUE}) or not (\code{FALSE} - default). #' -#' \item \code{centroids}: calculates the distance between each row and the centroid of the matrix (Lalibert'{e} 2010). This function can take an optional arguments \code{centroid} for defining the centroid (if missing (default), the centroid of the matrix is used). This argument can be either a subset of coordinates matching the matrix's dimensions (e.g. \code{c(0, 1, 2)} for a matrix with three columns) or a single value to be the coordinates of the centroid (e.g. \code{centroid = 0} will set the centroid coordinates to \code{c(0, 0, 0)} for a three dimensional matrix). NOTE: distance is calculated as \code{"euclidean"} by default, this can be changed using the \code{method} argument. +#' \item \code{centroids}: calculates the distance between each row and the centroid of the matrix (Laliberte 2010). This function can take an optional arguments \code{centroid} for defining the centroid (if missing (default), the centroid of the matrix is used). This argument can be either a subset of coordinates matching the matrix's dimensions (e.g. \code{c(0, 1, 2)} for a matrix with three columns) or a single value to be the coordinates of the centroid (e.g. \code{centroid = 0} will set the centroid coordinates to \code{c(0, 0, 0)} for a three dimensional matrix). NOTE: distance is calculated as \code{"euclidean"} by default, this can be changed using the \code{method} argument. #' #' \item \code{deviations}: calculates the minimal Euclidean distance between each element in and the hyperplane (or line if 2D, or a plane if 3D). You can specify equation of hyperplane of \emph{d} dimensions in the \eqn{intercept + ax + by + ... + nd = 0} format. For example the line \eqn{y = 3x + 1} should be entered as \code{c(1, 3, -1)} or the plane \eqn{x + 2y - 3z = 44} as \code{c(44, 1, 2, -3)}. If missing the \code{hyperplane} (default) is calculated using a least square regression using a gaussian \code{\link[stats]{glm}}. Extra arguments can be passed to \code{\link[stats]{glm}} through \code{...}. When estimating the hyperplane, you can use the option \code{significant} to only consider significant slopes (\code{TRUE}) or not (\code{FALSE} - default). #' \item \code{displacements}: calculates the ratio between the distance to the centroid (see \code{centroids} above) and the distance from a reference (by default the origin of the space). The reference can be changed through the \code{reference} argument. NOTE: distance is calculated as \code{"euclidean"} by default, this can be changed using the \code{method} argument. @@ -79,7 +81,7 @@ #' } #' By default, \code{point1} is the centre of the space (coordinates \code{0, 0, 0, ...}) and \code{point2} is the centroid of the space (coordinates \code{colMeans(matrix)}). Coordinates for \code{point1} and \code{point2} can be given as a single value to be repeated (e.g. \code{point1 = 1} is translated into \code{point1 = c(1, 1, ...)}) or a specific set of coordinates. #' Furthermore, by default, the space is scaled so that the vector (\code{point1}, \code{point2}) becomes the unit vector (distance (\code{point1}, \code{point2}) is set to 1; option \code{scale = TRUE}; default). You can use the unit vector of the space using the option \code{scale = FALSE}. -#' Other options include the centering of the projections on 0.5 (code{centre = TRUE}; default) ranging the projection onto the vector (\code{point1}, \code{point2}) between -1 and 1 (higher or lower values project beyond the vector); and whether to output the projection values as absolute values (\code{abs = TRUE}; default). These two last options only affect the results from \code{measure = "position"}. +#' Other options include the centering of the projections on 0.5 (\code{centre = TRUE}; default is set to \code{FALSE}) ranging the projection onto the vector (\code{point1}, \code{point2}) between -1 and 1 (higher or lower values project beyond the vector); and whether to output the projection values as absolute values (\code{abs = FALSE}; default is set to \code{FALSE}). These two last options only affect the results from \code{measure = "position"}. #' #' \item \code{projections.tree}: calculates the \code{projections} metric but drawing the vectors from a phylogenetic tree. This metric can intake any argument from \code{projections} (see above) but for \code{point1} and \code{point2} that are replaced by the argument \code{type}. \code{type} is a \code{vector} or a \code{list} of two elements that designates which vector to draw and can be any pair of the following options (the first element being the origin of the vector and the second where the vector points to): #' \itemize{ @@ -92,7 +94,7 @@ #' \item any numeric values that can be interpreted as \code{point1} and \code{point2} in \code{\link{projections}}; #' \item or a user defined function that with the inputs \code{matrix} and \code{tree} and \code{row} (the element's ID, i.e. the row number in \code{matrix}). #' } -#' \emph{NOTE:} the elements to calculate the origin and end points of the vector are calculated by default on the provided input \code{matrix} which can be missing data from the tree if used with \code{\link{custom.subsets}} or \code{\link{chrono.subsets}}. You can always provide the full matrix using the option \code{reference.data = my_matrix}. +#' \emph{NOTE:} the elements to calculate the origin and end points of the vector are calculated by default on the provided input \code{matrix} which can be missing data from the tree if used with \code{\link{custom.subsets}} or \code{\link{chrono.subsets}}. You can always provide the full matrix using the option \code{reference.data = my_matrix}. Additional arguments include any arguments to be passed to \code{\link{projections}} (e.g. \code{centre} or \code{abs}). #' #' \item \code{quantiles}: calculates the quantile range of each axis of the matrix. The quantile can be changed using the \code{quantile} argument (default is \code{quantile = 95}, i.e. calculating the range on each axis that includes 95\% of the data). An optional argument, \code{k.root}, can be set to \code{TRUE} to scale the ranges by using its \eqn{kth} root (where \eqn{k} are the number of dimensions). By default, \code{k.root = FALSE}. #' @@ -109,6 +111,7 @@ #' The currently implemented between.groups metrics are: #' \itemize{ #' \item \code{disalignment}: calculates the rejection of a point from \code{matrix} from the major axis of \code{matrix2}. Options are, \code{axis} to choose which major axis to reject from (default is \code{axis = 1}); \code{level} for the ellipse' confidence interval (to calculate the axis) (default is \code{level = 0.95}) and \code{point.to.reject}, a numeric value for designating which point in \code{matrix} to use or a function for calculating it (default is \code{point.to.reject = colMeans} for \code{matrix}'s centroid). +#Additional arguments include any arguments to be passed to \code{\link{projections}} (e.g. \code{centre} or \code{abs}). #' #' \item \code{group.dist}: calculates the distance between two groups (by default, this is the minimum euclidean vector norm distance between groups). Negative distances are considered as 0. This function must intake two matrices (\code{matrix} and \code{matrix2}) and the quantiles to consider. For the minimum distance between two groups, the 100th quantiles are considered (default: \code{probs = c(0,1)}) but this can be changed to any values (e.g. distance between the two groups accounting based on the 95th CI: \code{probs = c(0.025, 0.975)}; distance between centroids: \code{probs = c(0.5)}, etc...). This function is the linear algebra equivalent of the \code{hypervolume::hypervolume_distance} function. #' @@ -215,13 +218,13 @@ #' ## The edge lengths for each edge leading to the elements in the matrix #' edge.length.tree(named_matrix, tree = dummy_tree, to.root = FALSE) #' -#' ## ellipse.volume +#' ## ellipsoid.volume #' ## Ellipsoid volume of a matrix -#' ellipse.volume(dummy_matrix) +#' ellipsoid.volume(dummy_matrix) #' ## Calculating the same volume with provided eigen values #' ordination <- prcomp(dummy_matrix) #' ## Calculating the ellipsoid volume by providing your own eigen values -#' ellipse.volume(ordination$x, method = ordination$sdev^2) +#' ellipsoid.volume(ordination$x, method = ordination$sdev^2) #' #' ## func.div #' ## Functional divergence @@ -337,6 +340,14 @@ #' ## ranges of each column in the matrix corrected using the kth root #' ranges(dummy_matrix, k.root = TRUE) #' +#' ## roundness +#' ## calculating the variance-covariance of the dummy_matrix +#' vcv <- var(dummy_matrix) +#' ## calculating the roundness of it +#' roundness(vcv) +#' ## calculating the roundness of the dummy matrix by calculating the vcv +#' roundness(dummy_matrix, vcv = TRUE) +#' #' ## span.tree.length #' ## Minimum spanning tree length (default) #' span.tree.length(dummy_matrix) @@ -386,12 +397,13 @@ dimension.level1.fun <- function(matrix, ...) { cat("\n?convhull.surface") cat("\n?convhull.volume") cat("\n?diagonal") - cat("\n?ellipse.volume") + cat("\n?ellipsoid.volume") cat("\n?func.div") cat("\n?func.eve") cat("\n?group.dist") cat("\n?mode.val") cat("\n?n.ball.volume") + cat("\n?roundness") } between.groups.fun <- function(matrix, matrix2, ...) { @@ -503,7 +515,7 @@ mode.val <- function(matrix, ...){ } ## Calculate the ellipse volume of matrix -ellipse.volume <- function(matrix, method, ...) { +ellipsoid.volume <- function(matrix, method, ...) { ## Initialising the variables ncol_matrix <- ncol(matrix) @@ -528,7 +540,7 @@ ellipse.volume <- function(matrix, method, ...) { "eigen" = {sqrt(eigen(matrix)$values)}, ## The eigenvalue is equal to the sum of the variance/covariance within each axis (* nrow(matrix) as used in pco/pcoa) "pca" = {sqrt(abs(apply(var(matrix, na.rm = TRUE), 2, sum)))}, - ## Calculate the + ## Calculate the axes directly "axes" = {(sapply(1:ncol(matrix), function(dim, VCV) {dist(get.one.axis(VCV, axis = dim, ...))}, VCV = matrix))/2}) } else { semi_axes <- method[1:ncol_matrix] @@ -538,6 +550,10 @@ ellipse.volume <- function(matrix, method, ...) { ## Volume (from https://keisan.casio.com/exec/system/1223381019) return(pi^(ncol_matrix/2)/gamma((ncol_matrix/2)+1)*prod(semi_axes)) } +## Alias +ellipse.volume <- function(x, ...) { + ellipsoid.volume(matrix = x, ...) +} ## Calculate the convex hull hypersurface convhull.surface <- function(matrix, ...) { @@ -819,7 +835,7 @@ point.dist <- function(matrix, matrix2, point = colMeans, method = "euclidean", ## Angle between two vectors vector.angle <- function(v1, v2){ - + ## Transform a vector into an angle return(acos(geometry::dot(v1, v2, d = 1) / (sqrt(sum(v1^2))*sqrt(sum(v2^2)))) *180/pi) } ## Rotate a matrix along one axis (y) @@ -856,11 +872,8 @@ orthogonise <- function(angle) { return(ortho/90) } - -## Projection of elements on an axis -projections <- function(matrix, point1 = 0, point2 = colMeans(matrix), measure = "position", scale = TRUE, centre = TRUE, abs = TRUE, ...) { - ## IMPORTANT: edits in this function must also be copy/pasted to dispRity.covar.projections_fun.R/projections.fast - +## Calculate the linear algebraic projection of matrix onto the vector (point1, point2) +linear.algebra.projection <- function(matrix, point1, point2, do_angle, scale) { ## Get the point1 and point2 if(length(point1) != ncol(matrix)) { point1 <- rep(point1, ncol(matrix))[1:ncol(matrix)] @@ -904,11 +917,44 @@ projections <- function(matrix, point1 = 0, point2 = colMeans(matrix), measure = base_vector <- space[-c(1:nrow(matrix)), , drop = FALSE] ## Project the vectors - projections <- t(apply(matrix, 1, geometry::dot, y = base_vector[2,], d = 2)) + projs <- t(apply(matrix, 1, geometry::dot, y = base_vector[2,], d = 2)) + ## Calculate the angles - if(measure %in% c("degree", "radian", "orthogonality")) { + if(do_angle) { angles <- t(t(apply(matrix, 1, vector.angle, base_vector[2,]))) angles <- ifelse(is.nan(angles), 0, angles) + } else { + angles <- NULL + } + return(list("projections" = projs, "angles" = angles, "centred_matrix" = matrix)) +} +## Correct the position value (centring and absoluting) +correct.position <- function(values, centre, abs) { + if(centre && abs) { + return(abs(values - 0.5)/0.5) + } + if(centre && !abs) { + return((values - 0.5)/0.5) + } + if(!centre && abs) { + return(abs(values)) + } + return(values) +} +## Correct the distance value (scaling the unit distance to be equal to the unit position) +get.distance <- function(matrix, projections) { + ## Calculating the raw distances + return(apply(matrix - projections, 1, function(row) sqrt(sum(row^2)))) +} + +## Projection of elements on an axis +projections <- function(matrix, point1 = 0, point2 = colMeans(matrix), measure = "position", scale = TRUE, centre = FALSE, abs = FALSE, ...) { + + ## Get the projection (and angles, if asked) + proj_results <- linear.algebra.projection(matrix, point1, point2, do_angle = measure %in% c("degree", "radian", "orthogonality"), scale = scale) + projections <- proj_results$projections + if(measure %in% c("degree", "radian", "orthogonality")) { + angles <- proj_results$angles } # "position" #distance on @@ -923,7 +969,7 @@ projections <- function(matrix, point1 = 0, point2 = colMeans(matrix), measure = }, "distance" = { #distance from ## Get the rejection distance - apply(matrix - projections, 1, function(row) sqrt(sum(row^2))) + get.distance(proj_results$centred_matrix, projections) }, "degree" = { c(angles) @@ -937,28 +983,14 @@ projections <- function(matrix, point1 = 0, point2 = colMeans(matrix), measure = ## If position, apply correction if(measure == "position") { - if(centre && abs) { - values <- abs(values - 0.5)/0.5 - } - if(centre && !abs) { - values <- (values - 0.5)/0.5 - } - if(!centre && abs) { - values <- abs(values) - } - } - ## If distance, apply correction - if(measure == "distance") { - if(centre) { - values <- values/2 - } + values <- correct.position(values, centre, abs) } return(unname(values)) } ## Projections between covar matrices -projections.between <- function(matrix, matrix2, axis = 1, level = 0.95, measure = "position", scale = TRUE, centre = TRUE, abs = TRUE, ...) { +projections.between <- function(matrix, matrix2, axis = 1, level = 0.95, measure = "position", scale = TRUE, centre = FALSE, abs = FALSE, ...) { ## Get the main axes from the VCV matrices # source("covar.utilities_fun.R") @@ -999,7 +1031,7 @@ disalignment <- function(matrix, matrix2, axis = 1, level = 0.95, point.to.rejec } ## Measure the projection - return(projections(rejection_point, point1 = base_vector[1,], point2 = base_vector[2,], measure = "distance")) + return(projections(rejection_point, point1 = base_vector[1,], point2 = base_vector[2,], measure = "distance", centre = TRUE, abs = TRUE)) } ## Select the root coords @@ -1082,6 +1114,10 @@ projections.tree <- function(matrix, tree, type = c("root","ancestor"), referenc } } } + ## Sanitizing (to avoid obscure error message!) + if(any(is_null <- unlist(lapply(from_to, is.null)))) { + stop(paste0("The following type argument is not recognised in projections.tree: ", paste0(type[is_null], collapse = ", "))) + } if(all(invariables)) { ## Point1 and point2 are invariant @@ -1092,3 +1128,24 @@ projections.tree <- function(matrix, tree, type = c("root","ancestor"), referenc } } + +## The roundness function +roundness <- function(matrix, vcv = TRUE) { + ## Check the vcv + if(vcv) { + ## Check the dimensions and the triangles + if(length(unique(dim(matrix))) == 1 && all(matrix[upper.tri(matrix)] == matrix[rev(lower.tri(matrix))], na.rm = TRUE)) { + vcv <- matrix + } else { + vcv <- var(matrix) + } + } + + ## Sort and scale the eigen values + y <- sort(diag(matrix)) + y <- y/max(y) + x <- seq(from = 0, to = 1, length.out = length(y)) + ## Measure the integral + return(sum(diff(x)*zoo::rollmean(y, 2))) +} + diff --git a/R/dispRity.multi.R b/R/dispRity.multi.R new file mode 100644 index 00000000..9385251c --- /dev/null +++ b/R/dispRity.multi.R @@ -0,0 +1,217 @@ +# Internal function for applying a function to multiple data and trees +# If data$call$dispRity.multi = TRUE (sorted by check.dispRity.tree && check.dispRity.data) then apply the disparity function as a lapply + + + + +# e.g. + +# if(data$call$dispRity.multi) { +# ## Splitting the dispRity object into a list to be fed to lapply +# data_split <- dispRity.multi.split(data) +# ## Apply the function (lapply style) and override split for RAM management +# data_split <- dispRity.multi.apply(data_split, fun = my_fun, ...) # where my_fun = c(custom.subsets, chrono.subsets, boot.matrix or dispRity) +# ## Merge the resulting list (and override for RAM management) +# data_split <- dispRity.multi.merge(data) +# ## Returns the dispRity object (with call set to dispRity.multi = TRUE) +# return(data_split) +# } + +# ## Final version should be streamlined to +# if(data$call$dispRity.multi) { +# return(dispRity.multi.merge(dispRity.multi.apply(dispRity.multi.split(data), fun = my_fun, ...))) +# } + +## TODO 1: make check.dispRity.tree ping out data$call$dispRity.multi + + +## Splits the data into pairs of matrix + tree +dispRity.multi.split <- function(data) { + + ## Check if tree is needed + has_tree <- !is.null(data$tree[[1]]) + + ## List holder + multi.list <- list() + + if(has_tree && length(data$matrix) == length(data$tree)) { + ## Make pairs + while(length(data$matrix) != 0) { + multi.list[[length(multi.list)+1]] <- data + multi.list[[length(multi.list)]]$matrix <- multi.list[[length(multi.list)]]$matrix[1] + multi.list[[length(multi.list)]]$tree <- multi.list[[length(multi.list)]]$tree[1] + if(!is.null(data$disparity)) { + multi.list[[length(multi.list)]]$disparity <- lapply(data$disparity, function(x) return(x[1, ])) + data$disparity <- lapply(data$disparity, function(x) return(x[-1, ])) + } + data$matrix <- data$matrix[-1] + data$tree <- data$tree[-1] + } + } else { + ## Make multiples + n_matrices <- length(data$matrix) + n_trees <- length(data$tree) + + if(has_tree) { + ## Detect if any of the matrices or trees are unique + if(length(unique <- which(c(n_matrices, n_trees) == 1)) > 0) { + ## Find the variable + not_unique <- which(!(c("matrix", "tree") %in% switch(as.character(unique), "1" = "matrix", "2" = "tree"))) + ## Make the list + while(length(data[[not_unique]]) != 0) { + multi.list[[length(multi.list)+1]] <- data + multi.list[[length(multi.list)]][[not_unique]] <- multi.list[[length(multi.list)]][[not_unique]][1] + if(!is.null(data$disparity)) { + multi.list[[length(multi.list)]]$disparity <- lapply(data$disparity, function(x) return(x[1, ])) + data$disparity <- lapply(data$disparity, function(x) return(x[-1, ])) + } + data[[not_unique]] <- data[[not_unique]][-1] + } + } else { + ## Multiply the list + n_out <- expand.grid(1:n_matrices, 1:n_trees) + ## Make the list + while(nrow(n_out) > 0) { + multi.list[[length(multi.list)+1]] <- data + multi.list[[length(multi.list)]]$matrix <- data$matrix[n_out[1,1]] + multi.list[[length(multi.list)]]$tree <- data$tree[n_out[1,2]] + if(!is.null(data$disparity)) { + multi.list[[length(multi.list)]]$disparity <- lapply(data$disparity, function(x) return(x[1, ])) + data$disparity <- lapply(data$disparity, function(x) return(x[-1, ])) + } + n_out <- n_out[-1, ] + } + } + } else { + ## Just change the matrices (data has no tree) + not_unique <- which(names(data) == "matrix") + ## Make the list + while(length(data[[not_unique]]) != 0) { + multi.list[[length(multi.list)+1]] <- data + multi.list[[length(multi.list)]][[not_unique]] <- multi.list[[length(multi.list)]][[not_unique]][1] + if(!is.null(data$disparity)) { + multi.list[[length(multi.list)]]$disparity <- lapply(data$disparity, function(x) return(x[1, ])) + data$disparity <- lapply(data$disparity, function(x) return(x[-1, ])) + } + data[[not_unique]] <- data[[not_unique]][-1] + } + } + } + + if(has_tree) { + ## Clean the data (should be checked prior normally) + return(lapply(multi.list, lapply.clean.data)) + } else { + return(multi.list) + } +} +## Clean data for dispRity.multi.split +lapply.clean.data <- function(x) { + ## Clean the data + cleaned <- clean.data(x$matrix[[1]], x$tree[[1]], inc.nodes = !is.null(x$tree[[1]]$node.label)) + tree_out <- list(cleaned$tree) + class(tree_out) <- "multiPhylo" + return(list(matrix = list(cleaned$data), tree = list(tree_out), multi = x$multi)) +} + +## Apply the function to any pair of matrix + tree +dispRity.multi.apply <- function(matrices, fun, tree = NULL, ...) { + + ## Handle extra args + dots <- list(...) + match_call <- match.call() + + ## Detect the type: + type <- ifelse(any(c(is.null(tree), (length(tree) == 1))), "lapply", "mapply") + + ## Making argument list for chrono.subsets if FADLAD is provided as a list + if(!is.null(dots$FADLAD) && is(dots$FADLAD, "list")) { + ## Use a do.call + type <- "do.call" + + ## Get the list of arguments + chrono_args <- mapply(function(x, y) list(data = x, tree = y), matrices, tree, SIMPLIFY = FALSE) + + ## Adding the FADLADs + chrono_args <- mapply(function(x, y) list(data = x$data, tree = x$tree, "FADLAD" = y), chrono_args, dots$FADLAD, SIMPLIFY = FALSE) + + ## Removing FADLADs + dots$FADLAD <- NULL + ## Adding all the other arguments + chrono_args <- lapply(chrono_args, function(x, args) c(x, args), args = dots) + } + + ## Toggle to bootstraps (no tree argument) + if(is.null(tree) && match_call$fun == "boot.matrix.call") { + type <- "boot" + } + + ## Applying the fun + out <- switch(type, + "lapply" = lapply(matrices, fun, tree, ...), + "mapply" = mapply(fun, matrices, tree, MoreArgs = list(...), SIMPLIFY = FALSE), + "do.call" = do.call(fun, chrono_args), + "boot" = lapply(matrices, fun, ...)) + ## New class + class(out) <- c("dispRity", "multi") + return(out) +} + +## Merge the apply results into one classic dispRity object +dispRity.multi.merge <- function(data, output, match_call, ...) { + + ## Combine the data + data_out <- dispRity.multi.merge.data(data) + + ## Combine the disparity results + all_disparity <- lapply(output, `[[`, "disparity") + data_out$disparity <- dispRity.multi.merge.disparity(all_disparity) + + ## Update the call + data_out$call$disparity <- output[[1]]$call$disparity + ## Update the metric call name + data_out$call$disparity$metrics$name <- match_call$metric + ## Make it dispRity multi + data_out$call$dispRity.multi <- TRUE + return(data_out) +} + +## Merges data from a split (not output) +dispRity.multi.merge.data <- function(data) { + + ## Check if data is already dispRity formated + if(is(data, "dispRity") && is(data, "multi")) { + data_out <- data[[1]] + data_out$matrix <- unlist(lapply(data, `[[`, "matrix"), recursive = FALSE) + if(!is.null(data_out$tree[[1]])) { + tree <- lapply(data, `[[`, "tree") + class(tree) <- "multiPhylo" + data_out$tree <- tree + } + ## Merge subset names + if(!is.null(names(data_out$subsets))) { + names(data_out$subsets) <- apply(do.call(cbind, lapply(data, name.subsets)), 1, function(row) paste0(unique(row), collapse = "/")) + } + } else { + data_out <- data + } + + ## Make it dispRity multi + data_out$call$dispRity.multi <- TRUE + + return(data_out) +} + +## Merging disparity results +dispRity.multi.merge.disparity <- function(all_disparity) { + merge.subset.pair <- function(subset1, subset2) { + return(mapply(FUN = function(x,y)return(matrix(c(x, y), nrow = dim(x)[1])), x = subset1, y = subset2, SIMPLIFY = FALSE)) + } + while(length(all_disparity) != 1) { + ## Merge all subsets + all_disparity[[1]] <- mapply(merge.subset.pair, all_disparity[[1]], all_disparity[[2]], SIMPLIFY = FALSE) + ## Removed merged set + all_disparity[[2]] <- NULL + } + return(unlist(all_disparity, recursive = FALSE)) +} diff --git a/R/dispRity.utilities.R b/R/dispRity.utilities.R index 83a58338..dec1016a 100755 --- a/R/dispRity.utilities.R +++ b/R/dispRity.utilities.R @@ -6,12 +6,13 @@ #' @description Creating an empty \code{dispRity} object from a matrix #' #' @usage make.dispRity(data, tree, call, subsets) -#' @usage fill.dispRity(data, tree) +#' @usage fill.dispRity(data, tree, check) #' #' @param data A \code{matrix}. #' @param tree Optional, a \code{phylo} or \code{multiPhylo} object. #' @param call Optional, a \code{list} to be a \code{dispRity} call. #' @param subsets Optional, a \code{list} to be a \code{dispRity} subsets list. +#' @param check Logical, whether to check the data (\code{TRUE}; default, highly advised) or not (\code{FALSE}). #' #' @examples #' ## An empty dispRity object @@ -43,13 +44,13 @@ make.dispRity <- function(data, tree, call, subsets) { list = {dispRity_object$matrix <- data}) } - ## Add the tree + ## Add the call if(!missing(call)) { check.class(call, "list") dispRity_object$call <- call } - ## Add the call + ## Add the tree if(!missing(tree)) { class_tree <- check.class(tree, c("multiPhylo", "phylo")) if(class_tree == "multiPhylo") { @@ -71,11 +72,13 @@ make.dispRity <- function(data, tree, call, subsets) { return(dispRity_object) } -fill.dispRity <- function(data, tree) { +fill.dispRity <- function(data, tree, check = TRUE) { ## Data have a matrix if(!is.null(data)) { - data$matrix <- check.dispRity.data(data$matrix) + if(check) { + data$matrix <- check.dispRity.data(data$matrix, returns = "matrix") + } ## Dimensions if(length(data$call$dimensions) == 0) { @@ -94,7 +97,11 @@ fill.dispRity <- function(data, tree) { if(!missing(tree)) { ## Add the trees - data$tree <- check.dispRity.tree(tree, data = data) + if(check) { + data$tree <- check.dispRity.data(tree = tree, data = data, returns = "tree") + } else { + data$tree <- tree + } } return(data) } @@ -218,6 +225,10 @@ get.disparity <- function(data, subsets, rarefaction, observed = TRUE, concatena output <- lapply(data$disparity[subsets], lapply.observed) } else { output <- lapply(data$disparity[subsets], function(X) X$elements) + ## Flatten the output matrix if 1 row + if(all(unique(unlist(lapply(output, nrow))) == 1)) { + output <- lapply(output, c) + } } } else { output <- lapply(as.list(subsets), extract.disparity.values, data, rarefaction, concatenate) @@ -250,12 +261,13 @@ extract.dispRity <- function(...) { #' @name get.subsets -#' @aliases n.subsets size.subsets get.subsets combine.subsets +#' @aliases n.subsets name.subsets size.subsets get.subsets combine.subsets #' #' @title Extracts or modify subsets from a \code{dispRity} object. #' @description Extracting or modify some subsets' data and information from a \code{dispRity} object. #' #' @usage n.subsets(data) +#' @usage name.subsets(data) #' @usage size.subsets(data) #' @usage get.subsets(data, subsets) #' @usage combine.subsets(data, subsets) @@ -275,6 +287,9 @@ extract.dispRity <- function(...) { #' ## How many subsets are in disparity? #' n.subsets(disparity) #' +#' ## What are the subset names +#' name.subsets(disparity) +#' #' ## What are the number of elements per subsets? #' size.subsets(disparity) #' @@ -324,7 +339,6 @@ get.subsets <- function(data, subsets) { class(data_out) <- "dispRity" return(data_out) } - combine.subsets <- function(data, subsets) { ## Internal cleaning function for only selecting the elements of the list in a subset @@ -443,6 +457,10 @@ n.subsets <- function(data) { ## Getting the size of subsets return(length(data$subsets)) } +name.subsets <- function(data) { + ## Getting the subsets names + return(names(data$subsets)) +} @@ -456,18 +474,23 @@ n.subsets <- function(data) { #' @name add.tree -#' @aliases add.tree get.tree remove.tree +#' @aliases add.tree remove.tree get.tree #' -#' @title Add, get or remove tree +#' @title Add, remove or get trees (or subtrees) #' -#' @usage add.tree(data, tree) -#' @usage get.tree(data) +#' @usage add.tree(data, tree, replace = FALSE) +#' @usage get.tree(data, subsets = FALSE, to.root = FALSE) #' @usage remove.tree(data) #' -#' @description Adding, extracting or removing the tree component from a \code{dispRity} object +#' @description Adding, extracting or removing the tree component from a \code{dispRity} object. +#' +#' @details \code{get.tree} allows to extract the trees specific to each subsets. #' #' @param data A \code{dispRity} object. #' @param tree A \code{phylo} or \code{mutiPhylo} object. +#' @param replace Logical, whether to replace any existing tree (\code{TRUE}) or add to it (\code{FALSE}; default). +#' @param subsets Either a logical whether to extract the tree for each subset (\code{TRUE}) or not (\code{FALSE}; default) or specific subset names or numbers. +#' @param to.root Logical, whether to return the subset tree including the root of the tree (\code{TRUE}) or only containing the elements in the subset (and their most recent common ancestor; \code{FALSE}; default). If \code{data} contains time bins (from \code{\link{chrono.subsets}} with \code{method = "discrete"}), and \code{to.root = FALSE} it returns the subtrees containing only what's in the bin. #' #' @examples #' ## Loading a dispRity object @@ -486,26 +509,106 @@ n.subsets <- function(data) { #' #' ## Extracting the tree #' get.tree(tree_data) # is a "phylo" object -#' +#' +#' ## Adding the same tree again +#' tree_data <- add.tree(tree_data, tree = BeckLee_tree) +#' get.tree(tree_data) # is a "multiPhylo" object (2 trees) +#' +#' ## Replacing the two trees by one tree +#' tree_data <- add.tree(tree_data, tree = BeckLee_tree, replace = TRUE) +#' get.tree(tree_data) # is a "phylo" object +#' #' @seealso \code{\link{custom.subsets}}, \code{\link{chrono.subsets}}, \code{\link{boot.matrix}}, \code{\link{dispRity}}. #' -#' @author Thomas Guillerme -add.tree <- function(data, tree) { +#' @author Thomas Guillerme and Jack Hadfield +add.tree <- function(data, tree, replace = FALSE) { ## Add the tree if(is.null(data$tree[[1]])) { - data$tree <- check.dispRity.tree(tree = tree, data = data) + data$tree <- check.dispRity.data(data = data, tree = tree, returns = "tree") } else { - data$tree <- check.dispRity.tree(tree = c(get.tree(data$tree), tree), data = data) + if(replace) { + ## Remove existing trees + data <- remove.tree(data) + data <- add.tree(data, tree) + } else { + data$tree <- check.dispRity.data(tree = c(get.tree(data), tree), data = data, returns = "tree") + } } return(data) } -get.tree <- function(data) { - ## Return the tree - tree <- data$tree - if(length(tree) == 1) { - return(tree[[1]]) +get.tree <- function(data, subsets = FALSE, to.root = FALSE) { + ## Check for tree + match_call <- match.call() + if(is.null(data$tree)) { + stop.call(match_call$data, " does not contain any tree(s).") + } + + ## Returns just the tree + if((is(subsets, "logical") && !subsets) || is.null(data$subsets)) { + + ## Get the tree + tree <- data$tree + + ## Return the tree + if(length(tree) == 1) { + return(tree[[1]]) + } else { + return(tree) + } + } else { - return(tree) + + ## Extract subset trees + if((is(subsets, "logical") && subsets)) { + ## Get all subsets + subsets <- name.subsets(data) + } + + ## Check the subsets names + check.subsets(subsets, data) + + ## Check to root + check.class(to.root, "logical") + + ## Check whether to use slicing + slice.type <- data$call$subsets[[1]] + + ## Get the trees for each subset + if(slice.type != "discrete") { + + ## Get the sliced trees for custom subsets + if(slice.type == "customised") { + trees_list <- lapply(data$subsets[subsets], get.one.tree.subset, data$tree[[1]], to.root) + } + + ## Get the sliced trees for custom subsets + if(slice.type == "continuous") { + trees_list <- lapply(data$subsets[subsets], get.slice.subsets, data, to.root) + } + + } else { + bin_names <- subsets + ## Get the bin ages + bin_ages <- lapply(strsplit(bin_names, split = " - "), as.numeric) + names(bin_ages) <- bin_names + + ## Get all the tree subsets + all_subsets <- lapply(data$tree, get.interval.subtrees, bin_ages, to.root) + + ## Combine into multiphylo or not + if(length(all_subsets) != 1) { + ## Recursive merge all the trees + while(length(all_subsets) != 1) { + all_subsets[[1]] <- mapply(c, all_subsets[[1]], all_subsets[[2]], SIMPLIFY = FALSE) + all_subsets[[2]] <- NULL + } + } + ## Return the tree list + return(all_subsets[[1]]) + } + + ## return the trees + return(trees_list) } } remove.tree <- function(data) { @@ -523,26 +626,28 @@ remove.tree <- function(data) { - - #' @title Rescaling and centering disparity results. #' +#' @aliases rescale.dispRity +#' #' @description Scales or/and centers the disparity measurements. #' -#' @param data a \code{dispRity} object. +#' @param x a \code{dispRity} object. #' @param center either a \code{logical} value or a \code{numeric} vector of length equal to the number of elements of \code{data} (default is \code{FALSE}). #' @param scale either a \code{logical} value or a \code{numeric} vector of length equal to the number of elements of \code{data} (default is \code{TRUE}). -#' @param use.all \code{logical}, whether to scale/center using the full distribution (i.e. all the disparity values) or only the distribution within each subsets of bootstraps (default is \code{TRUE}). #' @param ... optional arguments to be passed to \code{scale}. #' +#' @details +#' To scale or and center using the full distribution (i.e. all the disparity values) or only the distribution within each subsets of bootstraps you can use the optional argument \code{use.all} as a logical. By default is \code{use.all = TRUE} and uses all the disparity values not only the ones in the subset. +#' #' @examples #' ## Load the disparity data based on Beck & Lee 2014 #' data(disparity) #' #' ## Scaling the data -#' summary(rescale.dispRity(disparity, scale = TRUE)) # Dividing by the maximum +#' summary(scale.dispRity(disparity, scale = TRUE)) # Dividing by the maximum #' ## Multiplying by 10 (dividing by 0.1) -#' summary(rescale.dispRity(disparity, scale = 0.1)) +#' summary(scale.dispRity(disparity, scale = 0.1)) #' #' @seealso \code{\link{dispRity}}, \code{\link{test.dispRity}}, \code{\link[base]{scale}}. #' @@ -558,11 +663,19 @@ remove.tree <- function(data) { # data <- dispRity(bootstrapped_data, metric = c(sum, centroids)) # summary(data) # No scaling -# summary(rescale.dispRity(data, scale = TRUE)) # Dividing by the maximum -# summary(rescale.dispRity(data, scale = 0.1)) # Multiplying by 10 -# summary(rescale.dispRity(data, center = TRUE, scale = TRUE)) # Scaling and centering -rescale.dispRity <- function(data, center = FALSE, scale = TRUE, use.all = TRUE, ...) { - +# summary(scale.dispRity(data, scale = TRUE)) # Dividing by the maximum +# summary(scale.dispRity(data, scale = 0.1)) # Multiplying by 10 +# summary(scale.dispRity(data, center = TRUE, scale = TRUE)) # Scaling and centering +scale.dispRity <- function(x, center = FALSE, scale = TRUE, ...) { + + dots <- list(...) + if(!is.null(dots$use.all)) { + use.all <- dots$use.all + } else { + use.all <- TRUE + } + + data <- x match_call <- match.call() ## data @@ -604,7 +717,9 @@ rescale.dispRity <- function(data, center = FALSE, scale = TRUE, use.all = TRUE, return(data) } - +rescale.dispRity <- function(x, ...) { + scale.dispRity(x, ...) +} @@ -706,7 +821,7 @@ sort.dispRity <- function(x, decreasing = FALSE, sort, ...) { -#' @title Getting the time subsets from at and after an extinction event +#' @title Getting the time subsets before and after an extinction event #' #' @description Getting the reference (pre-extinction) and the comparison (post-extinction) time subsets #' diff --git a/R/dispRity.utilities_fun.R b/R/dispRity.utilities_fun.R index fd645651..bead0626 100755 --- a/R/dispRity.utilities_fun.R +++ b/R/dispRity.utilities_fun.R @@ -132,4 +132,256 @@ add.dimnames <- function(one_output, one_subset, data) { "3" = {names(one_output) <- rownames(input); one_output} ) ) +} + +## Detect the edges containing the requested elements +detect.edges <- function(tree, elements, to.root) { + ## Detect which edges to keep + selected_edges <- match(elements, tree$edge[, 2]) + ## Has an NA (grabbed the root) + if(any(to_drop <- is.na(selected_edges))) { + ## Drop the root + selected_edges <- selected_edges[!to_drop] + ## Manually set the root + root <- Ntip(tree) + 1 + } else { + if(to.root) { + ## Manually set the root + root <- Ntip(tree) + 1 + } else { + ## Get the local root + root <- getMRCA(tree, elements) + } + } + + ## Get the nodes to test if all edges connect + test_nodes <- tree$edge[selected_edges, 1] + ## Remove the root + if(root %in% test_nodes) { + test_nodes <- test_nodes[-c(which(test_nodes == root))] + } + ## Check each node + while(length(test_nodes) != 0) { + if(test_nodes[1] %in% tree$edge[selected_edges, 2]) { + ## Node is OK + test_nodes <- test_nodes[-1] + } else { + ## Node is not connected + selected_edges <- c(selected_edges, which(tree$edge[,2] == test_nodes[1])) + ## Get the nodes to test if all edges connect + test_nodes <- tree$edge[selected_edges, 1] + ## Remove the root + if(root %in% test_nodes) { + test_nodes <- test_nodes[-c(which(test_nodes == root))] + } + } + } + return(selected_edges) +} + +## Get the tree containing requested elements +get.new.tree <- function(elements, tree, to.root) { + + ## Detect which edges to keep + new_edges <- unique(detect.edges(tree, elements, to.root)) + if(length(new_edges) < 2) { + return(NULL) + } + + ## Tracking the new tree + tree_track <- as.data.frame(tree$edge) + tree_track <- cbind(tree_track, is.tip = tree_track[,2] <= Ntip(tree)) + tree_track <- cbind(tree_track, label = c(tree$tip.label,tree$node.label)[tree_track[,2]]) + tree_track <- cbind(tree_track, selected = 1:nrow(tree_track) %in% new_edges) + + ## Get the root node name + root_lab <- tree$node.label[min(tree_track[tree_track$selected, 1])-Ntip(tree)] + + ## Build the new tree + new_tree <- list(edge = matrix(unlist(tree_track[tree_track$selected, c(1,2)]), ncol = 2), + edge.length = tree$edge.length[new_edges]) + + ## Update tips (some nodes become tips) + new_tips <- c(tree$tip.label,tree$node.label)[tree$edge[new_edges, 2][!(tree$edge[new_edges, 2] %in% tree$edge[new_edges ,1])]] + tree_track$is.tip[tree_track$label %in% new_tips] <- TRUE + + ## Convert the edge numbers + new_edge_table <- tree_track[tree_track$selected, ] + + ## Sort the node values + all_nodes <- c(new_edge_table[, 1], new_edge_table[!new_edge_table$is.tip, 2]) + all_nodes <- match(all_nodes, unique(all_nodes)) + sum(new_edge_table$is.tip) + + ## Update the table nodes + new_edge_table[, 1] <- all_nodes[1:nrow(new_edge_table)] + new_edge_table[!new_edge_table$is.tip, 2] <- all_nodes[-c(1:nrow(new_edge_table))] + + ## Update the table tips + new_edge_table[new_edge_table$is.tip, 2] <- 1:sum(new_edge_table$is.tip) + + ## Update the edge table + new_tree$edge <- matrix(unlist(new_edge_table[, c(1,2)]), ncol = 2) + ## Update the Nnodes + new_tree$Nnode <- sum(!new_edge_table$is.tip) + 1 #+1 is for the root + ## Update the tip labels + new_tree$tip.label <- new_edge_table$label[new_edge_table$is.tip] + ## Update the node labels + new_tree$node.label <- c(root_lab, new_edge_table$label[!new_edge_table$is.tip]) + + ## Update root.time + if(!is.null(tree$root.time)) { + if(to.root) { + new_tree$root.time <- tree$root.time + } else { + ages <- tree.age(tree) + ## Not necessary the first node label? + new_tree$root.time <- ages$ages[which(ages$elements == new_tree$node.label[1])] + } + } + + class(new_tree) <- "phylo" + return(new_tree) +} + +## Toggle an output to phylo or multiphylo +toggle.multiphylo.list <- function(x) { + if(is(x, "list")) { + ## Check if the elements are phylo + elements_class <- unlist(lapply(x, class)) + if(length(elements_class) == 1 && elements_class == "phylo") { + return(x[[1]]) + } else { + if(length(all_elems <- unique(elements_class)) == 1) { + if(all_elems == "phylo") { + x <- "multiPhylo" + return(x) + } + } + } + } + return(x) +} + +## Return a subseted tree +get.one.tree.subset <- function(one_subset, one_tree, to.root) { + ## Normal behaviour + output <- lapply(one_subset, function(x, tree, to.root) apply(x, 2, function(x, tree, to.root) get.new.tree(elements = x, tree = tree, to.root = to.root), tree = tree, to.root = to.root), tree = one_tree, to.root = to.root) + ## Change the output objects + output <- lapply(output, toggle.multiphylo.list) + + return(output) +} + + +## Slide nodes from the root +#@param bin_age the ages of the bin limits +#@param tree the original tree +#@return a tree with all old nodes slided +slide.node.root <- function(bin_age, tree) { + ## Get the age to slide the nodes to + time <- bin_age[1] + ## Get all ages + tree_ages <- tree.age(tree) + ## Get the yongest age for late + younger <- min(tree_ages$ages) + ## Remove the older tips + to_drop <- tree_ages$elements[which(tree_ages[tree_ages$elements %in% tree$tip.label, ]$ages > time)] + tree <- drop.tip(tree, tip = to_drop) + ## Update the root time + new_ages <- tree.age(tree)$ages + tree$root.time <- max(new_ages) + (younger - min(new_ages)) + + # warning("DEBUG slide.node.root") + # plot(tree, main = "dropped old tips") ; nodelabels(); axisPhylo() + # abline(v = 3-c(2, 1), col = "red", lty = 1) + + ## Recalculate the ages for the nodes + node_ages <- tree.age(tree) + younger <- min(node_ages$ages) + node_ages <- node_ages[!(node_ages$elements %in% tree$tip.label), ] + ## Get the nodes that are older than the time + nodes_to_slide <- which(node_ages$ages > time) + + ## reorder the nodes to slide by age + nodes_to_slide <- nodes_to_slide[match(sort(node_ages$ages[nodes_to_slide]), node_ages$ages[nodes_to_slide])] + + ## Recursively slide all nodes + while(length(nodes_to_slide) > 0) { + ## Get the node name and sliding value + sliding_value <- node_ages$ages[nodes_to_slide[1]] - time + node_name <- node_ages$elements[nodes_to_slide[1]] + ## slide it! + tree <- slide.nodes(node_name, tree, slide = sliding_value, allow.negative.root = TRUE) + ## Update the root time + new_ages <- tree.age(tree)$ages + tree$root.time <- max(new_ages) + (younger - min(new_ages)) + + ## Update the list of nodes to slide + nodes_to_slide <- nodes_to_slide[-1] + + # warning("DEBUG slide.node.root") + # plot(tree, main = "slid one node") ; nodelabels(); axisPhylo() + # abline(v = 3-c(2, 1), col = "red", lty = 1) + } + return(tree) +} + +## Get the subset of trees for one tree in an interval +get.interval.subtrees <- function(one_tree, bin_ages, to.root) { + ## Slice the right sides of the trees + slice.one.tree <- function(age, tree) { + slice.tree(tree, age[2], model = "acctran", keep.all.ancestors = TRUE) + } + subset_subtrees <- lapply(bin_ages, slice.one.tree, one_tree) # TODO need fix for multiphylo + + if(!to.root) { + ## Compressing the root of the tree up until the slice lower boundary + subset_subtrees <- mapply(slide.node.root, bin_ages, subset_subtrees, SIMPLIFY = FALSE) + } + + ## Name the subsets + names(subset_subtrees) <- names(bin_ages) + return(subset_subtrees) +} + +## Get the trees from slices +get.slice.subsets <- function(one_subset, data, to.root) { + ## ONLY FOR ELEMENTS FOR NOW + subset <- one_subset$elements + + ## Handeling split slices + if(!is.null(data$call$subsets[[2]]) && length(grep("split", data$call$subsets[[2]])) > 0) { + ## Sample the probabilities and collapse the matrix + sample.x <- function(x) {sample(x[1:2], 1, prob = c(x[3], 1-x[3]))} + sampled_subset <- matrix(apply(subset[, 1:3, drop = FALSE], 1, sample.x), ncol = 1) + ## remove the sampled + subset <- subset[, -c(1:3), drop = FALSE] + ## Sample for multiple trees + while(ncol(subset) > 0) { + sampled_subset <- cbind(sampled_subset, apply(subset[, 1:3, drop = FALSE], 1, sample.x)) + subset <- subset[, -c(1:3), drop = FALSE] + } + subset <- sampled_subset + } + + trees_list <- data$tree + trees_out <- list() + while(ncol(subset) > 0) { + ## Extract the elements for the tree recursively + new_tree <- get.new.tree(subset[,1], trees_list[[1]], to.root) + if(is.null(new_tree)) { + new_tree <- list(NULL) + } + trees_out[[length(trees_out) + 1]] <- new_tree + ## Remove the tree and the subset row + subset <- subset[, -1, drop = FALSE] + trees_list[1] <- NULL + } + ## Handle the tree output + if(length(trees_out) > 1) { + class(trees_out) <- "multiPhylo" + return(trees_out) + } else { + return(trees_out[[1]]) + } } \ No newline at end of file diff --git a/R/dispRity_fun.R b/R/dispRity_fun.R index f261f2ca..96f0b174 100755 --- a/R/dispRity_fun.R +++ b/R/dispRity_fun.R @@ -390,7 +390,7 @@ mapply.wrapper <- function(lapply_loop, data, metrics_list, matrix_decomposition } ## Split the lapply_loop for bound tree/matrices -split.lapply_loop <- function(lapply_loop, n_trees) { +lapply_loop.split <- function(lapply_loop, n_trees) { split.matrix <- function(matrix, n_trees) { ncol_out <- ncol(matrix)/n_trees @@ -403,7 +403,7 @@ split.lapply_loop <- function(lapply_loop, n_trees) { } ## Split the data for bound tree/matrices -split.data <- function(data) { +bound.data.split <- function(data) { ## Extract the necessary variables matrices <- data$matrix diff --git a/R/randtest.dist.R b/R/distance.randtest.R similarity index 89% rename from R/randtest.dist.R rename to R/distance.randtest.R index fa0315da..ecd5c452 100755 --- a/R/randtest.dist.R +++ b/R/distance.randtest.R @@ -2,7 +2,7 @@ #' #' @description Measures the distance between the observed statistic from a \code{"randtest"} object and some specific quantile of the simulated data. #' -#' @param randtest an object of class \code{"randtest"} +#' @param xtest an object of class \code{"randtest"} #' @param quantile a \code{numeric} value for the quantile edges to compare the observed data to on either sides (by default \code{quantile = c(0.025. 0.975)}). #' @param abs \code{logical}, whether to calculate the distance as an absolute value (\code{TRUE}) or not (\code{FALSE} - default). #' @@ -22,17 +22,19 @@ #' dummy_test ; plot(dummy_test) #' #' ## The distance between the observed data and the 95% quantile -#' randtest.dist(dummy_test) +#' distance.randtest(dummy_test) #' #' ## The absolute distance from the median -#' randtest.dist(dummy_test, quantile = 0.5, abs = TRUE) +#' distance.randtest(dummy_test, quantile = 0.5, abs = TRUE) #' #' @seealso \code{\link[ade4]{randtest}} \code{\link{randtest.dispRity}} #' #' @author Thomas Guillerme #' @export -randtest.dist <- function(randtest, quantile = c(0.025, 0.975), abs = FALSE) { +distance.randtest <- function(xtest, quantile = c(0.025, 0.975), abs = FALSE) { + + randtest <- xtest ## Checking randtest check.class(randtest, "randtest") diff --git a/R/dtt.dispRity.R b/R/dtt.dispRity.R index 3f98ab3a..47d612fe 100755 --- a/R/dtt.dispRity.R +++ b/R/dtt.dispRity.R @@ -9,7 +9,7 @@ #' @param model A evolutionary model for the simulations (see \code{geiger::sim.char} - default is \code{"BM"}). #' @param alternative The H1 alternative (for calculating the p-value). Can be \code{"two-sided"} (default), \code{"greater"} or \code{"lesser"}; see details. #' @param scale.time Optional, whether to scale the time (between 0 and 1; \code{TRUE}, default) or not (\code{FALSE}). -#' @param ... Any other arguments to be passed to \\code{geiger::dtt}. +#' @param ... Any other arguments to be passed to \code{geiger::dtt}. #' #' @details #' See \code{geiger::dtt} for details. diff --git a/R/make.metric.R b/R/make.metric.R index a1d76148..6761ac9a 100755 --- a/R/make.metric.R +++ b/R/make.metric.R @@ -73,7 +73,7 @@ make.metric <- function(fun, ..., silent = FALSE, check.between.groups = FALSE, matrix_text <- paste0("matrix(rnorm(",data.dim[1],"*",data.dim[2],"), ",data.dim[1], ", ",data.dim[2], ")") if(covar) { - matrix <- list(VCV = matrix, loc = diag(matrix)) + matrix <- list(VCV = as.matrix(dist(matrix)), loc = diag(matrix)) matrix_text <- "" } diff --git a/R/match.tip.edge.R b/R/match.tip.edge.R index 53cba92b..edfe60b6 100755 --- a/R/match.tip.edge.R +++ b/R/match.tip.edge.R @@ -1,13 +1,14 @@ -#' @title Match tips edge vector +#' @title Match tips or nodes edge vector #' -#' @description Match a vector of tips with the an edge list +#' @description Match a vector of tips or tips and nodes with the an edge list from a \code{"phylo"} or \code{"multiPhylo"}. #' -#' @param vector a vector of variables (equal to the number of tips). -#' @param phylo a phylo object. +#' @param vector a vector of variables (equal to the number of tips or to the number of tips and nodes). +#' @param phylo a phylo or multiPhylo object. #' @param replace.na optional, what to replace NAs with. +#' @param use.parsimony logical, whether to also colour internal edges parsimoniously (\code{TRUE} - default; i.e. if two nodes have the same unique ancestor node and the same variable, the ancestor node is assume to be the of the same value as its descendants) or not (\code{FALSE}). #' #' @returns -#' A vector of variables equal to the number of edges in the tree +#' A vector of variables equal to the number of edges in the tree (or a list of vectors if the \code{phylo} input is of class \code{"multiPhylo"}). #' #' @examples #' ## A random tree @@ -23,18 +24,46 @@ #' plot(tree, show.tip.label = FALSE, edge.color = edge_colors) #' tiplabels(1:20, bg = tip_values) #' +#' ## Same but without assuming parsimony for the internal nodes +#' plot(tree, show.tip.label = FALSE, +#' edge.color = match.tip.edge(tip_values, tree, +#' use.parsimony = FALSE, +#' replace.na = "grey")) #' +#' ## Matching the tips and nodes colors with the edges +#' node_values <- sample(c("blue", "red"), 19, replace = TRUE) +#' edge_colors <- match.tip.edge(c(tip_values, node_values), tree) +#' plot(tree, show.tip.label = FALSE, edge.color = edge_colors) +#' tiplabels(1:20, bg = tip_values) +#' nodelabels(1:19, bg = node_values) #' @author Thomas Guillerme #' @export ## Matching edges and colours -match.tip.edge <- function(vector, phylo, replace.na) { +match.tip.edge <- function(vector, phylo, replace.na, use.parsimony = TRUE) { + + match_call <- match.call() ## Sanitizing - check.class(phylo, "phylo") + phylo_class <- check.class(phylo, c("phylo", "multiPhylo")) + if(is(phylo, "multiPhylo")) { + if(length(unique(Ntip(phylo))) != 1) { + stop.call(msg.pre = "The trees from ", call = match_call$phylo, msg = " must have the same number of tips.") + } + ## Run the function on the list of trees + return(lapply(phylo, function(tree, vector, replace.na, use.parsimony) match.tip.edge(vector, tree, replace.na, use.parsimony), vector, replace.na, use.parsimony)) + } check.class(vector, c("factor", "character", "numeric", "integer")) - if(length(vector) != Ntip(phylo)) { - stop(paste0("The input vector must of the same length as the number of tips in phylo (", Ntip(phylo), ").")) + + ## TODO: check number of nodes as well + if(length(vector) != Ntip(phylo)[1]) { + if(length(vector) != Ntip(phylo)[1]+Nnode(phylo)[1]) { + stop(paste0("The input vector must of the same length as the number of tips (", Ntip(phylo)[1], ") or tips and nodes (", Ntip(phylo)[1]+Nnode(phylo)[1] ,") in phylo.")) + } + } + if(length(vector) == Ntip(phylo)[1]+Nnode(phylo)[1]) { + ## Don't use parsimony if node info is available + use.parsimony <- FALSE } ## Fill in the edges @@ -71,25 +100,27 @@ match.tip.edge <- function(vector, phylo, replace.na) { # counter <- 0 ## Recursively find any cherries - focal_edges <- which(edge_table[, 2] %in% tips) - while(any(duplicated(edge_table[focal_edges, 1]))) { - - # # DEBUG - # warning("DEBUG") - # counter <- counter + 1 - # print(counter) + if(use.parsimony) { + focal_edges <- which(edge_table[, 2] %in% tips) + while(any(duplicated(edge_table[focal_edges, 1]))) { + + # # DEBUG + # warning("DEBUG") + # counter <- counter + 1 + # print(counter) - ## Find and cherries of the same group - nodes <- edge_table[focal_edges, 1][which(duplicated(edge_table[focal_edges, 1]))] - - ## Update the group edges - selected_edges <- c(selected_edges, which(edge_table[, 2] %in% nodes)) + ## Find and cherries of the same group + nodes <- edge_table[focal_edges, 1][which(duplicated(edge_table[focal_edges, 1]))] + + ## Update the group edges + selected_edges <- c(selected_edges, which(edge_table[, 2] %in% nodes)) - ## Update the tips to check - tips <- c(tips[!(tips %in% edge_table[which(edge_table[, 1] %in% nodes), 2])], nodes) + ## Update the tips to check + tips <- c(tips[!(tips %in% edge_table[which(edge_table[, 1] %in% nodes), 2])], nodes) - ## Update the selected edges - focal_edges <- which(edge_table[, 2] %in% tips) + ## Update the selected edges + focal_edges <- which(edge_table[, 2] %in% tips) + } } ## Replace the selected edges by the group value diff --git a/R/model.test.R b/R/model.test.R index 3cc71248..2d479f70 100755 --- a/R/model.test.R +++ b/R/model.test.R @@ -17,27 +17,22 @@ #' The models are fit using maximum likelihood optimisation using the function optim. Fine-tuning of the search algorithms can be applied using the \code{control.list} argument. Models can be fit using a homogenous model with the same process applied to the entire sequence or models with time splits that represent a change in parameters or a shift in mode. When a heterogeneous and/or a time-shift model is specified with a specified \code{time.split} then the shift is tested at that value only. If no time shift is supplied then multiple shift times are tested, with all bins that allow for at least 10 bins either side of the split. If the entire sample is fewer than 30 samples long then no time splits are searched for (unless a time split is supplied by the user). Parameters are shared across different modes. For example, \code{c("BM", "OU")} would fit a model in which the process starts with a BM model and shifts to an OU process. The ancestral value at the start of the sequence and sigma squared value are shared across the models. Any combination of the following homogenous models (with the exception of \code{"multi.OU"}) can be fit to the data: #' #' \itemize{ -#' \item{BM}{ Fits a unbiased random walk model of Brownian motion evolution (Felsenstein 1973; 1985; Hunt 2006). The model optimises the ancestral state and the 'step-variance' (sigma-squared)} -#' -#' \item{OU}{ The Ornstein-Uhlenbeck model of evolution in which the change in variance is constrained to an optimum value (Hansen 1997). In this model there are three parameters: optima, alpha, and ancestral state. The strength of attraction based on the parameter alpha and the ancestral state is estimated from the data. The optima value is estimated from the data, and this can lead to optima being found outside the known data values, and thus the model can resemble a trend. If the argument \code{fixed.optima = TRUE}, the model will not estimate optima but constrain it to the first (ancestral) value in the sequence as is done in phylogenetic OU models} -#' -#' \item{Trend}{ Fits a Brownian motion model with a directional component. This model is also known as the General Random Walk (Hunt 2006). This model has three parameters: the ancestral state, the 'step-variance' (sigma-squared), and the positive or negative trend.} -#' -#' \item{Stasis}{ Fits a model in which traits evolve with variance (omega) around a mean (theta). This model is time-independent in that the model is guided only by the variance and attraction to the mean (Hunt 2006)} -#' -#' \item{EB}{ Early-Burst. Trait variance accumulates early in the evolution of a trait and decreases exponentially through time (Blomberg et al. 2003; Harmon et al. 2010). This model has three parameters: ancestral state, sigma-squared, and the exponential rate of decrease. Note this model expects the mean to remain unchanged through the model, so does not explicitly model a rapid change to a new mean or optimum value.} -#' -#' \item{multi.OU}{ Fits a model in which the value of the optima shifts at one or more time splits. The values of the 'step-variance' (sigma squared) and attraction to the optima (alpha) are shared across all the samples. This model can not be fit with other models - the multi.OU system can be fit to the model only} +#' \item BM: Fits a unbiased random walk model of Brownian motion evolution (Felsenstein 1973; 1985; Hunt 2006). The model optimises the ancestral state and the 'step-variance' (sigma-squared). +#' \item OU: The Ornstein-Uhlenbeck model of evolution in which the change in variance is constrained to an optimum value (Hansen 1997). In this model there are three parameters: optima, alpha, and ancestral state. The strength of attraction based on the parameter alpha and the ancestral state is estimated from the data. The optima value is estimated from the data, and this can lead to optima being found outside the known data values, and thus the model can resemble a trend. If the argument \code{fixed.optima = TRUE}, the model will not estimate optima but constrain it to the first (ancestral) value in the sequence as is done in phylogenetic OU models. +#' \item Trend: Fits a Brownian motion model with a directional component. This model is also known as the General Random Walk (Hunt 2006). This model has three parameters: the ancestral state, the 'step-variance' (sigma-squared), and the positive or negative trend. +#' \item Stasis: Fits a model in which traits evolve with variance (omega) around a mean (theta). This model is time-independent in that the model is guided only by the variance and attraction to the mean (Hunt 2006). +#' \item EB: Early-Burst, trait variance accumulates early in the evolution of a trait and decreases exponentially through time (Blomberg et al. 2003; Harmon et al. 2010). This model has three parameters: ancestral state, sigma-squared, and the exponential rate of decrease. Note this model expects the mean to remain unchanged through the model, so does not explicitly model a rapid change to a new mean or optimum value. +#' \item multi.OU: Fits a model in which the value of the optima shifts at one or more time splits. The values of the 'step-variance' (sigma squared) and attraction to the optima (alpha) are shared across all the samples. This model can not be fit with other models - the multi.OU system can be fit to the model only. #' } #' #' @return A list of class \code{dispRity} and \code{model.test} that can be plotted and summarised via \code{\link{summary.dispRity}} and \code{\link{plot.dispRity}}. #' The list is composed of: #' \itemize{ -#' \item{$aic.models}{ summary for each model's small sample Akaike Information Criterion (AICc), delta AICc, and AICc weight} -#' \item{$full.models}{ the list of the full models outputs from \code{\link{optim}} with the estimated parameters, log-likelihood, convergence statistics, and the split.time if applicable } -#' \item{$call}{ the model input} -#' \item{$models.data}{ input data used by the model(s)} -#' \item{$fixed.optima}{ Logical indicating whether a fixed optima was assumed for OU model(s)} +#' \item \code{$aic.models} summary for each model's small sample Akaike Information Criterion (AICc), delta AICc, and AICc weight +#' \item \code{$full.models} the list of the full models outputs from \code{\link{optim}} with the estimated parameters, log-likelihood, convergence statistics, and the split.time if applicable +#' \item \code{$call} the model input +#' \item \code{$models.data} input data used by the model(s) +#' \item \code{$fixed.optima} Logical indicating whether a fixed optima was assumed for OU model(s) #' } #' #' @examples diff --git a/R/model.test.sim.R b/R/model.test.sim.R index c03e19be..0ecaf5f4 100755 --- a/R/model.test.sim.R +++ b/R/model.test.sim.R @@ -208,7 +208,7 @@ model.test.sim <- function(sim = 1, model, model.rank = 1, alternative = "two-si model <- strsplit(model, ":")[[1]] # MP: necessary for the combination of 'multi.OU' and 'fixed.optima=TRUE' otherwise simulations take wrong optimum - if(fixed.optima && model == "multi.OU") parameters$optima.1 <- parameters$ancestral.state + if(fixed.optima && model[[1]] == "multi.OU") parameters$optima.1 <- parameters$ancestral.state if(model[1] == "Stasis") parameters$ancestral.state <- parameters$theta.1 diff --git a/R/model.test.wrapper.R b/R/model.test.wrapper.R index 88df7054..cc4bb7a9 100755 --- a/R/model.test.wrapper.R +++ b/R/model.test.wrapper.R @@ -87,7 +87,6 @@ model.test.wrapper <- function(data, model, pool.variance = NULL, time.split = N sim <- abs(sim) } ## The rest of model.test arguments are tested by the model.test functions - check.class(plot.sim, "logical") if(!missing(col.sim)) {check.class(col.sim, "character")} check.class(col.obs, "character") @@ -109,7 +108,7 @@ model.test.wrapper <- function(data, model, pool.variance = NULL, time.split = N summary.models <- summary(models.out) n.models <- dim(summary.models)[1] outputs <- suppressWarnings(lapply(1:n.models, function(x) model.test.sim(sim, model = models.out, model.rank = x))) - p.int <- t(sapply(outputs, function(u) c(u$p[[4]], u$p[[5]]))) + p.int <- t(sapply(outputs, function(u) return(c(attr(u$p.value, "p"), attr(u$p.value, "p_interval"))))) results <- cbind(summary.models) results <- results[order(results[, 2]), ] # MP: allow a single model to be used as input for model.test.wrapper (may be a bit pointless but prevents an error) @@ -161,7 +160,7 @@ model.test.wrapper <- function(data, model, pool.variance = NULL, time.split = N } if(show.p) { - legend("bottomleft", paste0("Rank Env. Test, p = ", round( p.int[one_model, 2], 3), ":", round( p.int[one_model, 3], 3)), cex = cex.p) + legend("bottomleft", paste0("Rank Env. Test, p = ", round( p.int[one_model, 1], 3), ":", round( p.int[one_model, 2], 3)), cex = cex.p) } } ## Reset default diff --git a/R/model.test_fun.R b/R/model.test_fun.R index e3ff3d04..64828a08 100755 --- a/R/model.test_fun.R +++ b/R/model.test_fun.R @@ -241,27 +241,28 @@ est.mean <- function(p, data.model.test.in, model.type, optima.level.ou, optima. alpha <- p[3] optima <- p[c(4, 9, 10)] - all.splits <- length(split.time) - start.split <- split.time[-all.splits] - start.split[-1] <- start.split[-1] + 1 - end.split <- split.time[-1] - - take.away <- start.split[1] - 1 - start.split <- start.split - take.away - end.split <- end.split - take.away - + all.splits <- length(split.time) + start.split <- split.time[-all.splits] + start.split[-1] <- start.split[-1] + 1 + end.split <- split.time[-1] + + take.away <- start.split[1] - 1 + start.split <- start.split - take.away + end.split <- end.split - take.away + n.optima <- length(split.time) - 1 ou.mean.fun <- function (anc.state, optima, alpha, time) optima * (1 - exp(-alpha * time)) + anc.state * exp(-alpha * time) ou.mean.splits <- sapply(1: n.optima, function(x) ou.mean.fun(anc.state, optima[x], alpha, data.model.test.in$subsets)) mean.ou <- c() - for(x in 1:n.optima) mean.ou <- c(mean.ou, ou.mean.splits[start.split[x] : end.split[x] , x]) + for(x in 1:n.optima) { + mean.ou <- c(mean.ou, ou.mean.splits[start.split[x] : end.split[x] , x]) + } return(mean.ou) } if(model.type == "Stasis") { - if(optima.level.stasis == 1) theta <- p[5] if(optima.level.stasis == 2) theta <- p[11] if(optima.level.stasis == 3) theta <- p[12] @@ -270,22 +271,21 @@ est.mean <- function(p, data.model.test.in, model.type, optima.level.ou, optima. } if(model.type == "Trend") { - if(est.anc) { anc.state <- p[1] } else { anc.state <- model.anc } - trend.param <- p[7] - sample.size <- length(data.model.test.in[[1]]) - mean.trend <- anc.state + trend.param * data.model.test.in$subsets - } + trend.param <- p[7] + sample.size <- length(data.model.test.in[[1]]) + mean.trend <- anc.state + trend.param * data.model.test.in$subsets + return(mean.trend) + } } est.VCV <- function(p, data.model.test, model.type, est.anc=TRUE, model.anc) { if(model.type == "BM" | model.type == "Trend") { - sigma.squared <- p[2] VCV <- sigma.squared * outer(data.model.test$subsets, data.model.test$subsets, FUN = pmin) diag(VCV) <- diag(VCV) + data.model.test$variance / data.model.test$sample_size @@ -293,7 +293,6 @@ est.VCV <- function(p, data.model.test, model.type, est.anc=TRUE, model.anc) { } if(model.type == "OU" || model.type == "multi.OU" ) { - alpha <- p[3] sigma.squared <- p[2] VCV <- outer(data.model.test$subsets, data.model.test$subsets, function(x, y) abs(x - y)) @@ -306,7 +305,6 @@ est.VCV <- function(p, data.model.test, model.type, est.anc=TRUE, model.anc) { } if(model.type == "Stasis") { - omega <- p[6] VCV <- diag(omega + data.model.test$variance / data.model.test$sample_size) return(VCV) @@ -355,9 +353,7 @@ model.test.lik <- function(model.test_input, model.type.in, time.split, control. upper.bounds <- c(NA, 100, 100, NA, NA, 20, 100, -1e-8, NA, NA, NA, NA) model.output <- stats::optim(par = p, fn = opt.mode, method = "L", control = control.list, model.type.in = model.type.in, time.split = time.split, data.model.test = model.test_input, lower = lower.bounds, upper = upper.bounds, fixed.optima = fixed.optima) - - model.type.in - + model.output.pars <- model.output[[1]] names(model.output.pars) <- c("ancestral state", "sigma squared", "alpha", "optima.1", "theta.1", "omega", "trend", "eb", "optima.2", "optima.3", "theta.2", "theta.3") model.output$par <- get.parameters(model.output.pars, model.type.in, time.split = time.split, fixed.optima = fixed.optima) @@ -371,21 +367,21 @@ opt.mode <- function(p, model.type.in, time.split, data.model.test, fixed.optima total.n <- length(data.model.test$subsets) sample.time <- 1:total.n - split.here.vcv <-c(1, time.split) - split.here.2.vcv <-c(time.split - 1, total.n) + split.here.vcv <- c(1, time.split) + split.here.2.vcv <- c(time.split - 1, total.n) any.model <- which(model.type.in == "multi.OU") - if(any(any.model, na.rm=T)) { - split.here.vcv <- split.here.2.vcv <- NULL - ou.mean <- c(1, time.split, length(data.model.test$subsets)) - split.here.vcv <- c(1, split.here.vcv) - split.here.2.vcv <- c(split.here.2.vcv, length(data.model.test$subsets)) - } + if(any(any.model, na.rm = TRUE)) { + split.here.vcv <- split.here.2.vcv <- NULL + ou.mean <- c(1, time.split, length(data.model.test$subsets)) + split.here.vcv <- c(1, split.here.vcv) + split.here.2.vcv <- c(split.here.2.vcv, length(data.model.test$subsets)) + } - total_VCV <- matrix(0, nrow=total.n, ncol=total.n) + total_VCV <- matrix(0, nrow = total.n, ncol = total.n) - total_mean <- c() + total_mean <- c() optima.level.ou <- optima.level.stasis <-1 model.anc <- model.alpha <- NULL time.int <- 1 @@ -426,7 +422,7 @@ opt.mode <- function(p, model.type.in, time.split, data.model.test, fixed.optima time.int <- time.x + 1 } - } else { + } else { data.model.test.int <- lapply(data.model.test, function(k) k[sort(sample.time[split.here.vcv[time.x] : (split.here.2.vcv[time.x])] )]) @@ -470,13 +466,13 @@ opt.mode <- function(p, model.type.in, time.split, data.model.test, fixed.optima est.anc <- FALSE time.int <- time.x + 1 } - - } + } - total_VCV[split.here.vcv[time.x] : (split.here.2.vcv[time.x]), split.here.vcv[time.x] : (split.here.2.vcv[time.x]) ] <- output.vcv - total_mean <- c(total_mean, output.mean) + total_VCV[split.here.vcv[time.x] : (split.here.2.vcv[time.x]), split.here.vcv[time.x] : (split.here.2.vcv[time.x]) ] <- output.vcv + total_mean <- c(total_mean, output.mean) } - mnormt::dmnorm(t(data.model.test$central_tendency), mean = total_mean, varcov = total_VCV, log = TRUE) + + return(mnormt::dmnorm(t(data.model.test$central_tendency), mean = total_mean, varcov = total_VCV, log = TRUE)) } @@ -513,9 +509,25 @@ rank_env_dtt <- function(x, alternative) { s1 <- sims[-c(1),] r <- as.vector(x$subsets[-c(1)]) obs <- as.vector(x$central_tendency)[-c(1)] + + ## Get the curve c1 <- list("r" = r, "obs" = obs, "sim_m" = s1) - c2 <- spptest::create_curve_set(c1) - res <- spptest::rank_envelope(c2, alternative = alternative) + c2 <- GET::create_curve_set(c1, verbose = FALSE) + + ## Get the alpha (scales with the size of c2, the bigger c2, the smaller alpha) + n_sim <- ncol(c2$funcs) + if(n_sim <= 10) { + alpha <- 0.05*n_sim + } else { + if(n_sim <= 100) { + alpha <- 1/(0.05*n_sim)/4 + } else { + alpha <- 0.05 + } + } + + ## Run the test + res <- GET::rank_envelope(c2, alternative = alternative, alpha = alpha) return(res) } diff --git a/R/multi.ace.R b/R/multi.ace.R index b6b0d172..afbe3d22 100755 --- a/R/multi.ace.R +++ b/R/multi.ace.R @@ -4,7 +4,7 @@ #' #' @param data A \code{matrix} or \code{list} with the characters for each taxa. #' @param tree A \code{phylo} or \code{mutiPhylo} object (if the \code{tree} argument contains node labels, they will be used to name the output). -#' @param models A \code{vector} of models to be passed to \code{\link[castor]{asr_mk_model}}. +#' @param models A \code{vector} of models to be passed to \code{castor::asr_mk_model}. #If left empty, the it will use the \code{\link{fit.ace.model}} function to find the best model using the first tree. See details. #' @param threshold either \code{logical} for applying a relative threshold (\code{TRUE} - default) or no threshold (\code{FALSE}) or a \code{numeric} value of the threshold (e.g. 0.95). See details. #' @param special.tokens optional, a named \code{vector} of special tokens to be passed to \code{\link[base]{grep}} (make sure to protect the character with \code{"\\\\"}). By default \code{special.tokens <- c(missing = "\\\\?", inapplicable = "\\\\-", polymorphism = "\\\\&", uncertainty = "\\\\/")}. Note that \code{NA} values are not compared and that the symbol "@" is reserved and cannot be used. @@ -12,14 +12,14 @@ #' @param brlen.multiplier optional, a vector of branch length modifiers (e.g. to convert time branch length in changes branch length) or a list of vectors (the same length as \code{tree}). #' @param verbose \code{logical}, whether to be verbose (\code{TRUE}) or not (\code{FALSE} - default). #' @param parallel \code{logical}, whether to use parallel algorithm (\code{TRUE}) or not (\code{FALSE} - default). -#' @param output optional, see Return section below. -#' @param castor.options optional, a named list of options to be passed to function called by \code{\link[castor]{asr_mk_model}}. -#' @param estimation.details optional, whether to also return the details for each estimation as returned by \code{\link[castor]{asr_mk_model}}. This argument can be left \code{NULL} (default) or be any combination of the elements returned by \code{\link[castor]{asr_mk_model}} (e.g. \code{c("loglikelihood", "transition_matrix")}). +#' @param output optional, see Value section below. +#' @param castor.options optional, a named list of options to be passed to function called by \code{castor::asr_mk_model}. +#' @param estimation.details optional, whether to also return the details for each estimation as returned by \code{castor::asr_mk_model}. This argument can be left \code{NULL} (default) or be any combination of the elements returned by \code{castor::asr_mk_model} (e.g. \code{c("loglikelihood", "transition_matrix")}). #' #' @details #' #' The \code{models} argument can be a single or a list of transition \code{matrix}, a single or a a vector of built-in model(s) (see below) or a list of both matrices and built-in models: -#' The available built-in models in \code{\link[castor]{asr_mk_model}} are: +#' The available built-in models in \code{castor::asr_mk_model} are: #' \itemize{ #' \item \code{"ER"} for all equal rates #' \item \code{"SYM"} for symmetric rates @@ -27,7 +27,7 @@ #' \item \code{"SUEDE"} equal stepwise transitions (e.g. for meristic/counting characters) #' \item \code{"SRD"} different stepwise transitions #' } -#' See directly \code{\link[castor]{asr_mk_model}} for more models. +#' See directly \code{castor::asr_mk_model} for more models. # TODO: add note about fit.ace.model #' #' The \code{threshold} option allows to convert ancestral states likelihoods into discrete states. When \code{threshold = FALSE}, the ancestral state estimated is the one with the highest likelihood (or at random if likelihoods are equal). When \code{threshold = TRUE}, the ancestral state estimated are all the ones that are have a scaled likelihood greater than the maximum observed scaled likelihood minus the inverse number of possible states (i.e. \code{select_state >= (max(likelihood) - 1/n_states)}). This option makes the threshold selection depend on the number of states (i.e. if there are more possible states, a lower scaled likelihood for the best state is expected). Finally using a numerical value for the threshold option (e.g. \code{threshold = 0.95}) will simply select only the ancestral states estimates with a scaled likelihood equal or greater than the designated value. This option makes the threshold selection absolute. Regardless, if more than one value is select, the uncertainty token (\code{special.tokens["uncertainty"]}) will be used to separate the states. If no value is selected, the uncertainty token will be use between all observed characters (\code{special.tokens["uncertainty"]}). @@ -42,7 +42,7 @@ #' #' Functions in the list must be named following the special token of concern (e.g. \code{missing}), have only \code{x, y} as inputs and a single output a single value (that gets coerced to \code{integer} automatically). For example, the special behaviour for the special token \code{"?"} can be coded as: \code{special.behaviours = list(missing = function(x, y) return(NA)} to make ignore the character for taxa containing \code{"?"}. #' -#' When using the parallel option (either through using \code{parallel = TRUE} by using the number of available cores minus on or manually setting the number of cores - e.g. \code{parallel = 5}), the \code{\link[castor]{asr_mk_model}} function will use the designated number of cores (using the option \code{Nthreads = }). Additionally, if the input \code{tree} is a \code{"multiPhylo"} object, the trees will be run in parallel for each number of cores, thus decreasing computation time accordingly (e.g. if 3 cores are requested and \code{tree} contains 12 \code{"phylo"} objects, 4 different \code{"phylo"} objects will be run in parallel on the 3 cores making the calculation around 3 times faster). +#' When using the parallel option (either through using \code{parallel = TRUE} by using the number of available cores minus on or manually setting the number of cores - e.g. \code{parallel = 5}), the \code{castor::asr_mk_model} function will use the designated number of cores (using the option \code{Nthreads = }). Additionally, if the input \code{tree} is a \code{"multiPhylo"} object, the trees will be run in parallel for each number of cores, thus decreasing computation time accordingly (e.g. if 3 cores are requested and \code{tree} contains 12 \code{"phylo"} objects, 4 different \code{"phylo"} objects will be run in parallel on the 3 cores making the calculation around 3 times faster). #' #' @return #' Returns a \code{"matrix"} or \code{"list"} of ancestral states. By default, the function returns the ancestral states in the same format as the input \code{matrix}. This can be changed using the option \code{output = "matrix"} or \code{"list"} to force the class of the output. @@ -120,7 +120,7 @@ #' parallel = TRUE) #' } #' @seealso -#' \code{\link[castor]{asr_mk_model}}, \code{char.diff} +#' \code{castor::asr_mk_model}, \code{char.diff} # \code{fit.ace.model}, #' #' @author Thomas Guillerme @@ -442,7 +442,7 @@ multi.ace <- function(data, tree, models = "ER", threshold = TRUE, special.token "invariant_characters_states") export_functions_list <- c("one.tree.ace", "castor.ace", - "update.tree.data", + "tree.data.update", "add.state.names", "translate.likelihood") diff --git a/R/multi.ace_fun.R b/R/multi.ace_fun.R index ec4c592c..3ab10e6c 100755 --- a/R/multi.ace_fun.R +++ b/R/multi.ace_fun.R @@ -65,7 +65,7 @@ make.args <- function(character, character_states, model, castor.options, cores, } ## Update the tree and data -update.tree.data <- function(castor_args) { +tree.data.update <- function(castor_args) { ## Find if any of the tips have NAs if(any(dropped <- apply(castor_args$tip_priors, 1, FUN = function(x) any(is.na(x))))) { ## Update the tip_priors @@ -87,7 +87,7 @@ castor.ace <- function(castor_args) { verboseplaceholder <- "silent" ## Drop the tips with no data if needed - dropped <- update.tree.data(castor_args) + dropped <- tree.data.update(castor_args) castor_args <- dropped[[1]] dropped <- dropped[[2]] diff --git a/R/null.test.R b/R/null.test.R index e78d236d..909c7dfd 100755 --- a/R/null.test.R +++ b/R/null.test.R @@ -18,7 +18,7 @@ #' ## Load the Beck & Lee 2014 data #' data(BeckLee_mat50) #' ## Calculating the disparity as the ellipsoid volume -#' obs_disparity <- dispRity(BeckLee_mat50, metric = ellipse.volume) +#' obs_disparity <- dispRity(BeckLee_mat50, metric = ellipsoid.volume) #' ## Testing against normal distribution #' results <- null.test(obs_disparity, replicates = 100, null.distrib = rnorm) #' results ; plot(results) diff --git a/R/pgls.dispRity.R b/R/pgls.dispRity.R new file mode 100644 index 00000000..4a3d8211 --- /dev/null +++ b/R/pgls.dispRity.R @@ -0,0 +1,251 @@ +#' @title phylolm dispRity (from \code{phylolm::phylolm}) +#' +#' @description Passing \code{dispRity} objects to the \code{\link[phylolm]{phylolm}} function from the \code{phylolm} package. Typically to run some PGLS. +#' +#' @param data A \code{dispRity} object with a metric of dimension level 2 at least +#' @param tree If \code{data} does not contain a tree component, a \code{"phylo"} or \code{"multiPhylo"} object to be used as the tree. If \code{data} already contains a tree component and the \code{tree} argument is not missing, the provided \code{tree} will replace any contained in \code{data}. +#' @param formula The PGLS formula. If left empty, runs either \code{disparity ~ 1} or \code{disparity ~ subsets} if \code{data} contains subsets. +#' @param model The covariance model (default is \code{"BM"}). For more details (including the models available) see the manual for \code{\link[phylolm]{phylolm}}. +#' @param ... Any optional arguments to be passed to \code{\link[phylolm]{phylolm}} +#' @param optim An optional named list of arguments to be passed to the function \code{optim} +#' +#' @details +#' The \code{formula} needs to be expressed by always naming the response variable \code{disparity} to use the calculated disparity data from \code{data}. +#' +#' Optional arguments \code{...} correspond to all the non-ambiguous named arguments from the \code{\link[phylolm]{phylolm}}. Optional arguments for the internal \code{optim} function can be passed as a named list to the \code{optim} argument. +#' +#' @seealso +#' \code{\link[phylolm]{phylolm}}, \code{\link{test.dispRity}}, \code{\link{custom.subsets}}, \code{\link{chrono.subsets}}. +#' +#' @examples +#' ## Simple example +#' data(BeckLee_mat50) +#' data(BeckLee_tree) +#' disparity <- dispRity(BeckLee_mat50, metric = centroids, tree = BeckLee_tree) +#' +#' ## Running a simple PGLS +#' model <- pgls.dispRity(disparity) +#' summary(model) +#' +#' ## More complex example running a PGLS +#' ## on multiple trees and using groups as a predictor +#' + +#' @author Thomas Guillerme +pgls.dispRity <- function(data, tree, formula, model = "BM", ..., optim = list()) { + + match_call <- match.call() + + ## Check data + check.class(data, "dispRity") + ## Check data level + disparity_list <- get.disparity(data) + checks <- unlist(lapply(disparity_list, check.dimension)) + if(any(!checks)) { + stop.call(msg.pre = "Impossible to run a univariate pgls on ", match_call$data, msg = " because doesn't contain a dimension level-2 metric. See ?dispRity.metric for more info.") + } + + ## Check tree in data + if(!missing(tree)) { + data <- add.tree(data, tree = tree, replace = TRUE) + } else { + if(is.null(get.tree(data))) { + stop("No tree was found in the provided data and none was provided through the tree argument.") + } + } + + ## Check the formula + if(missing(formula)) { + ## Select the formula + formula <- get.formula(data) + } + + ## Check if response is disparity + if(as.character(formula[[2]]) != "disparity") { + stop("The response term of the formula must be 'disparity'.", call. = FALSE) + } + + ## Get the pgls data + data_list <- get.pgls.data(data) + + ## Check model + check.method(model, all_arguments = eval(formals(phylolm::phylolm)$model), msg = "model") + + ## Set the phylolm optional args + phylolm_args <- as.list(c(..., optim)) + # warning("DEBUG"); phylolm_args <- as.list(c(optim)) + ## Add the main arguments + phylolm_args$formula <- formula + phylolm_args$model <- model + + ## Run all the models + models_out <- lapply(data_list, one.phylolm, phylolm_args) + + ## Handle the output + if(length(models_out) == 1) { + return(models_out[[1]]) + } else { + class(models_out) <- c("dispRity", "pgls.dispRity") + return(models_out) + } +} + +## Internals +## Check the dimension of the data +check.dimension <- function(one_disparity) { + ## Must have names, be numeric (or integer), no dimensions and length > 1 + return(!is.null(names(one_disparity)) && + (is.numeric(one_disparity) || is.integer(one_disparity)) && + is.null(dim(one_disparity)) && + length(one_disparity) > 1) +} + +## Outputs the formula depending on what's in the object +get.formula <- function(disparity) { + if(is.null(disparity$call$subsets)) { + return(disparity ~ 1) + } else { + ## Grouped data + ## Get the groups + group <- lapply(disparity$subsets, function(x) return(c(x$elements))) + ## Check overlap + if(any(table(unlist(group)) != 1)) { + stop("Some groups have overlapping elements.") + } + ## Return the correct formula + if(disparity$call$subsets[[1]] == "customised") { + return(disparity ~ group) + } else { + ## Warning for time auto-correlation + stop("It is currently not possible to apply an phylogenetic linear model on dispRity data with time series.") + # warning("Data contains time series: the default formula used is disparity ~ time but it does not take time autocorrelation into account.", call. = FALSE) + # colnames(group_table) <- "time" + # return(list(formula = disparity ~ time, group = NULL, time = group_table)) + } + } +} + +## Formats the data and trees for phylolm +get.pgls.data <- function(data) { + ## Extract the trees (as a list) + trees <- get.tree(data) + if(is(trees, "phylo")) { + trees <- list(trees) + } + + ## Extract the disparity results (as a list) + disparity <- get.disparity(data, observed = TRUE, concatenate = FALSE) + + ## Split the data per matrix and per group + split_data <- lapply(disparity, function(X) apply(X, 2, function(x) return(x), simplify = FALSE)) + + ## Get the rownames for all the data + #TG: expecting all the matrices to have the same rownames + row_names <- rownames(data$matrix[[1]])[unlist(lapply(data$subsets, function(x) return(x[["elements"]][,1])))] + + ## Split between matrix + data_list <- list() + while(length(split_data[[1]]) > 0) { + ## Get the disparity and group for one matrix + dispa <- unlist(lapply(split_data, `[[`, 1)) + group <- unlist(mapply(rep_len, as.list(names(split_data)), lapply(lapply(split_data, `[[`, 1), length))) + ## Populate the data list + if(!is.null(group)) { + data_list[[length(data_list)+1]] <- data.frame("disparity" = dispa, "group" = group, row.names = row_names) + } else { + data_list[[length(data_list)+1]] <- data.frame("disparity" = dispa, row.names = row_names) + } + ## Remove from the list + split_data <- lapply(split_data, function(x) {x[[1]] <- NULL; return(x)}) + } + + ## Combine both trees and matrices + if(length(trees) != length(data_list)) { + ## Check if feasible + multiple_trees <- (length(trees) > 1) + multiple_datas <- (length(data_list) > 1) + if(multiple_datas && multiple_trees) { + stop(paste0("Data must either same number of matrices (", length(data_list), ") and trees (", length(trees) , ") or just one tree or matrix combined with respectively with multiple matrices or trees.")) + } + ## Combine the data + if(multiple_datas) { + data_out <- lapply(data_list, function(data, tree) return(list(data = data, phy = trees[[1]])), trees) + } else { + data_out <- lapply(trees, function(tree, data) return(list(data = data[[1]], phy = tree)), data_list) + } + } else { + data_out <- mapply(function(data, tree) return(list(data = data, phy = tree)), data_list, trees, SIMPLIFY = FALSE) + } + return(data_out) +} + +## Run one phylolm +one.phylolm <- function(one_datas, args) { + ## Adding the tree and the data + args$phy <- one_datas$phy + args$data <- one_datas$data + + ## Run the phylolm + run_out <- do.call(phylolm, args) + ## Edit the call + run_out$call <- paste0(c("dispRity interface of phylolm using: formula = ", args$formula, " and model = ", args$model), collapse = "") + return(run_out) +} + +## Pooling output data together for plot and summary +pool.pgls.param <- function(x, param, fun = c(median = median, sd = sd)) { + ## Extract the parameters + param_values <- lapply(x, `[[`, param) + ## Make them into a table + param_values <- do.call(rbind, param_values) + ## Get param names + if(is.null(colnames(param_values))) { + param_names <- param + } else { + param_names <- colnames(param_values) + } + ## Output + return(matrix(c(apply(param_values, 2, fun[[1]]), apply(param_values, 2, fun[[2]])), ncol = length(fun), dimnames = list(c(param_names), names(fun)))) +} + +## Converting a list of phylolm to summary phylolm (median) +convert.to.summary.phylolm <- function(data) { + ## Get the standard error and the t statistic + se <- sqrt(apply(do.call(rbind, lapply(data, function(x) return(diag(x$vcov)))), 2, median)) + med_coefs <- pool.pgls.param(data, "coefficients")[,1] + tval <- med_coefs/se + + ## Get the results table + if(data[[1]]$boot == 0) { + results_table <- cbind(Estimate = med_coefs, StdErr = se, t.value = tval, + p.value = 2*pt(-abs(tval), df=data[[1]]$n - data[[1]]$d)) + } else { + ## Bootstrapped results + lower_bootCI <- apply(do.call(rbind, lapply(data, function(x, dim) return(x$bootconfint95[1, 1:dim]), dim = data[[1]]$d)), 2, median) + upper_bootCI <- apply(do.call(rbind, lapply(data, function(x, dim) return(x$bootconfint95[2, 1:dim]), dim = data[[1]]$d)), 2, median) + results_table <- cbind(Estimate = med_coefs, StdErr = se, t.value = tval, + lowerbootCI = lower_bootCI, upperbootCI = upper_bootCI, + p.value = 2*pt(-abs(tval), df=data[[1]]$n - data[[1]]$d)) + } + + ## Combine the results into a phylolm object + sum_phylolm <- list(call = c(data[[1]]$call, paste0("The statistics are calculated based on the median estimates of ", length(data), " models.")), + coefficients = results_table, + residuals = pool.pgls.param(data, "residuals")[,1], + sigma2 = pool.pgls.param(data, "sigma2")[,1], + optpar = if(is.null(data[[1]]$optpar)) {data[[1]]$optpar} else {pool.pgls.param(data, "optpar")[,1]}, + sigma2_error = if(is.null(data[[1]]$sigma2_error)) {data[[1]]$sigma2_error} else {pool.pgls.param(data, "sigma2_error")[,1]}, + logLik = pool.pgls.param(data, "logLik")[,1], + df = data[[1]]$p, + aic = pool.pgls.param(data, "aic")[,1], + model = data[[1]]$model, + mean.tip.height = pool.pgls.param(data, "mean.tip.height", fun = c(mean = mean, sd = sd))[,1], + bootNrep = ifelse(data[[1]]$boot>0, data[[1]]$boot - data[[1]]$bootnumFailed, 0), + + r.squared = pool.pgls.param(data, "r.squared")[,1], + adj.r.squared = pool.pgls.param(data, "adj.r.squared")[,1]) + + + class(sum_phylolm) <- "summary.phylolm" + return(sum_phylolm) +} diff --git a/R/plot.char.diff.R b/R/plot.char.diff.R index d7c281bb..75c80fce 100755 --- a/R/plot.char.diff.R +++ b/R/plot.char.diff.R @@ -153,6 +153,6 @@ plot.char.diff <- function(x, ..., type = "matrix", legend = TRUE, legend.title } } else { ## Plotting the density profile - plot.char.diff.density(matrix, main, legend, col, xlim, ylim, legend.pos, xlab, ylab) + do.plot.char.diff.density(matrix, main, legend, col, xlim, ylim, legend.pos, xlab, ylab) } } \ No newline at end of file diff --git a/R/plot.char.diff_fun.R b/R/plot.char.diff_fun.R index c0e6f85f..937fb48f 100755 --- a/R/plot.char.diff_fun.R +++ b/R/plot.char.diff_fun.R @@ -14,7 +14,7 @@ select.nas <- function(column) { } ## Plotting density -plot.char.diff.density <- function(matrix, main, legend, col, xlim, ylim, legend.pos, xlab, ylab) { +do.plot.char.diff.density <- function(matrix, main, legend, col, xlim, ylim, legend.pos, xlab, ylab) { ## Removing columns with NAs NA_columns <- which(apply(matrix, 2, select.nas) == TRUE) diff --git a/R/plot.dispRity.R b/R/plot.dispRity.R index a4e96e6e..86c1531e 100755 --- a/R/plot.dispRity.R +++ b/R/plot.dispRity.R @@ -138,7 +138,7 @@ plot.dispRity <- function(x, ..., type, quantiles = c(50, 95), cent.tend = media ## Select the right dataset plot_data$data_sub <- data[[1]] ## Run the plot - plot.randtest(plot_data) + do.plot.randtest(plot_data) } else { ## Set up multiple plot windows plot_size <- ifelse(length_data == 3, 4, length_data) @@ -149,10 +149,17 @@ plot.dispRity <- function(x, ..., type, quantiles = c(50, 95), cent.tend = media plot_data$data_sub <- data[[model]] ## Add the title (optional) if(is.null(dots$main)) { - plot_data$dots$main <- paste("MC test for subsets ", names(data)[[model]], sep = "") + if(length(grep("compared to", data[[model]]$call)) == 1) { + plot_data$dots$main <- gsub("dispRity.randtest: ", "", gsub("compared to ", "compared to\n", data[[model]]$call)) + + } else { + plot_data$dots$main <- paste("MC test for subsets ", names(data)[[model]], sep = "") + } + } else { + plot_data$dots$main <- dots$main[model] } ## Run the plot - plot.randtest(plot_data) + do.plot.randtest(plot_data) } par(op_tmp) } @@ -164,7 +171,7 @@ plot.dispRity <- function(x, ..., type, quantiles = c(50, 95), cent.tend = media if(is(data, c("dispRity")) && is(data, c("dtt"))) { ## Dtt style plots - plot.dtt(data, quantiles, cent.tend, density, ...) + do.plot.dtt(data, quantiles, cent.tend, density, ...) rm(data) return(invisible()) } @@ -173,7 +180,7 @@ plot.dispRity <- function(x, ..., type, quantiles = c(50, 95), cent.tend = media if(is(data, c("dispRity")) && is(data, c("model.test"))) { ## Plotting the model support - plot.model.test(data, ...) + do.plot.model.test(data, ...) rm(data) return(invisible()) } @@ -190,7 +197,7 @@ plot.dispRity <- function(x, ..., type, quantiles = c(50, 95), cent.tend = media check.length(density, 1, " must be a single numeric value.") } - plot.model.sim(data, add, density, quantiles, cent.tend, ...) + do.plot.model.sim(data, add, density, quantiles, cent.tend, ...) rm(data) return(invisible()) } @@ -199,7 +206,7 @@ plot.dispRity <- function(x, ..., type, quantiles = c(50, 95), cent.tend = media if(is(data, c("dispRity")) && is(data, c("test.metric"))) { ## Plotting the test.metric results - plot.test.metric(data, specific.args, ...) + do.plot.test.metric(data, specific.args, ...) rm(data) ## Exit subclass plots @@ -210,7 +217,7 @@ plot.dispRity <- function(x, ..., type, quantiles = c(50, 95), cent.tend = media if(is(data, c("dispRity")) && is(data, c("axes"))) { ## Plot the data - plot.axes(data, ...) + do.plot.axes(data, ...) rm(data) ## Exit subclass plots @@ -221,12 +228,36 @@ plot.dispRity <- function(x, ..., type, quantiles = c(50, 95), cent.tend = media if(is(data, c("dispRity")) && is(data, c("projection"))) { ## Plot the data - plot.projection(data, specific.args, cent.tend, ...) + do.plot.projection(data, specific.args, cent.tend, ...) rm(data) ## Exit subclass plots return(invisible()) - } + } + + ## pgls.dispRity plots + if(is(data, c("dispRity")) && is(data, c("pgls.dispRity"))) { + + ## Get all the y values + all_y <- unlist(lapply(data, `[[`, "y")) + ## Get all the fitted values + all_fitted <- unlist(lapply(data, fitted)) + + ## Default labels + plot_args <- list(...) + if(is.null(plot_args$xlab)) { + plot_args$xlab <- "Observed values" + } + if(is.null(plot_args$ylab)) { + plot_args$ylab <- "Fitted value" + } + plot_args$x <- all_y + plot_args$y <- all_fitted + + ## Plot + do.call(plot, plot_args) + return(invisible()) + } } ## ---- @@ -259,7 +290,7 @@ plot.dispRity <- function(x, ..., type, quantiles = c(50, 95), cent.tend = media ## Plot the matrix preview if(!missing(type) && type == "preview") { ## Plotting the matrix preview - plot.preview(data, specific.args, ...) + do.plot.preview(data, specific.args, ...) rm(data) return(invisible()) } @@ -392,13 +423,13 @@ plot.dispRity <- function(x, ..., type, quantiles = c(50, 95), cent.tend = media switch(plot_task, "rarefaction" = { - plot.rarefaction(plot_params, data_params, data) + do.plot.rarefaction(plot_params, data_params, data) }, "continuous" = { - plot.continuous(plot_params, data_params, add = add, density = density) + do.plot.continuous(plot_params, data_params, add = add, density = density) }, "polygon" = { - plot.discrete(plot_params, data_params, add = add, density = density, type = type) + do.plot.discrete(plot_params, data_params, add = add, density = density, type = type) }, "box" = { ## Set the box arguments @@ -412,12 +443,12 @@ plot.dispRity <- function(x, ..., type, quantiles = c(50, 95), cent.tend = media ## Add the observed if(plot_params$observed_args$observed) { - plot.observed(plot_params) + do.plot.observed(plot_params) } ## Add elements if(plot_params$elements_args$elements) { - plot.elements(plot_params, data_params, type) + do.plot.elements(plot_params, data_params, type) } rm(plot_params) diff --git a/R/plot.dispRity_fun.R b/R/plot.dispRity_fun.R index f74dbd34..576c57f0 100755 --- a/R/plot.dispRity_fun.R +++ b/R/plot.dispRity_fun.R @@ -26,7 +26,7 @@ get.plot.params <- function(data, data_params, cent.tend, quantiles, rarefaction summarised_data <- summary.dispRity(data, quantiles = quantiles, cent.tend = cent.tend, digits = 5, na.rm = TRUE) # } } else { - summarised_data <- summary.dispRity(data, quantiles = quantiles, cent.tend = cent.tend, digits = 5) + summarised_data <- summary.dispRity(data, quantiles = quantiles, cent.tend = cent.tend, digits = 5, na.rm = TRUE) ## Making a fake obs column colnames(summarised_data)[3] <- "obs" } @@ -311,7 +311,7 @@ get.quantile.col <- function(cent_tend_col, cis, n_quantiles) { } ## Observed points -plot.observed <- function(plot_params) { +do.plot.observed <- function(plot_params) { if(plot_params$observed_args$observed) { ## Set the observed arguments @@ -330,7 +330,7 @@ plot.observed <- function(plot_params) { } ## Plot elements -plot.elements <- function(plot_params, data_params, type) { +do.plot.elements <- function(plot_params, data_params, type) { ## Elements plots add_args <- plot_params$elements_args add_args$elements <- NULL @@ -406,7 +406,7 @@ plot.elements <- function(plot_params, data_params, type) { } ## discrete plotting -plot.discrete <- function(plot_params, data_params, add, density, type) { +do.plot.discrete <- function(plot_params, data_params, add, density, type) { ## Get the shifting argument shift <- get.shift(add, plot_params) @@ -515,7 +515,7 @@ plot.discrete <- function(plot_params, data_params, add, density, type) { } ## continuous plotting -plot.continuous <- function(plot_params, data_params, add, density) { +do.plot.continuous <- function(plot_params, data_params, add, density) { ## Get the shifting argument shift <- get.shift(add, plot_params) @@ -628,7 +628,7 @@ plot.continuous <- function(plot_params, data_params, add, density) { } ## Plot the rarefaction -plot.rarefaction <- function(plot_params, data_params, data) { +do.plot.rarefaction <- function(plot_params, data_params, data) { ## How many rarefaction plots? n_plots <- length(data$subsets) @@ -691,7 +691,7 @@ plot.rarefaction <- function(plot_params, data_params, data) { } ## Plotting a space preview -plot.preview <- function(data, specific.args, ...) { +do.plot.preview <- function(data, specific.args, ...) { dots <- list(...) @@ -876,7 +876,7 @@ plot.preview <- function(data, specific.args, ...) { } ## The following is a modified version of plot.randtest from ade4 v1.4-3 -plot.randtest <- function(plot_data) { +do.plot.randtest <- function(plot_data) { ## Extracting the elements dots <- plot_data$dots @@ -939,7 +939,7 @@ plot.randtest <- function(plot_data) { } ## The following is a modified version of dtt plots (from https://github.com/mwpennell/geiger-v2/blob/master/R/disparity.R) -plot.dtt <- function(data, quantiles, cent.tend, density, ...) { +do.plot.dtt <- function(data, quantiles, cent.tend, density, ...) { dots <- list(...) plot_args <- dots @@ -1022,7 +1022,7 @@ plot.dtt <- function(data, quantiles, cent.tend, density, ...) { } ## Plotting model tests results -plot.model.test <- function(data, ...) { +do.plot.model.test <- function(data, ...) { plot_args <- list(...) ## Set the default plotting arguments @@ -1040,7 +1040,7 @@ plot.model.test <- function(data, ...) { } ## Plotting the model simulation results -plot.model.sim <- function(data, add, density, quantiles, cent.tend, ...) { +do.plot.model.sim <- function(data, add, density, quantiles, cent.tend, ...) { dots <- list(...) @@ -1079,11 +1079,13 @@ plot.model.sim <- function(data, add, density, quantiles, cent.tend, ...) { options(warn = 0) ## Plot the simulated data - plot.continuous(plot_params, data_params, add = add, density = density) + do.plot.continuous(plot_params, data_params, add = add, density = density) } ## Plotting test metrics -plot.test.metric <- function(data, specific.args, ...) { +do.plot.test.metric <- function(data, specific.args, ...) { + + # specific.args <- list() ; warning("DEBUG do.plot.test.metric") ## Adding slopes add.slope <- function(model, col) { @@ -1098,22 +1100,65 @@ plot.test.metric <- function(data, specific.args, ...) { col = col) } } + ## Adding little stars for p-values for people that like that + p.stars <- function(x) { + if(x < 0.1) { + if(x < 0.05) { + if(x < 0.01) { + if(x < 0.001) { + return("***") + } + return("**") + } + return("*") + } + return(".") + } + return("") + } ## Adding fits add.fit <- function(model) { + has_fit <- has_coeff <- FALSE fit_param <- try.get.from.model(model, "r.squared") - if(!is.null(fit_param) || length(fit_param) != 0) { + coeff_param <- try.get.from.model(model, "coefficient") + ## Adjust the fitting + if(!is.null(fit_param) || length(fit_param) != 0) { + has_fit <- TRUE if(any(names(fit_param) == "adj.r.squared")) { fit_param <- fit_param$adj.r.squared is_adjusted <- TRUE } else { is_adjusted <- FALSE } + text_fit <- paste0(ifelse(is_adjusted, "Adj. R^2: ", "R^2: "), unlist(round(fit_param, 3))) + } + + ## Adjust the coefficient + if(!is.null(coeff_param) && is(coeff_param[[1]], "matrix")) { + has_coeff <- TRUE + slopes_coeffs <- coeff_param[[1]][-1, , drop = FALSE] + slopes_estimates <- slopes_coeffs[, "Estimate"] + ## Nice rounding + slopes_estimates <- round(slopes_estimates, nchar(format(slopes_estimates, scientific = FALSE)) - nchar(sub("0\\.0*", "", format(slopes_estimates, scientific = FALSE)))) + p_values <- sapply(slopes_coeffs[, "Pr(>|t|)"], p.stars) + slopes <- paste0(slopes_estimates, p_values) + text_slope <- paste0("Slope: ", slopes) + } - return(paste0(ifelse(is_adjusted, "Adj. R^2: ", "R^2: "), unlist(round(fit_param, 3)))) + if(has_fit && has_coeff) { + return(paste0(text_slope, "; ", text_fit)) } else { - return(NA) + if(has_fit && !has_coeff) { + return(text_fit) + } else { + if(!has_fit && has_coeff) { + return(text_slope) + } + } } + ## Return nothing + return(NA) } ## Detect whether to plot the shift steps or not @@ -1219,10 +1264,10 @@ plot.test.metric <- function(data, specific.args, ...) { if(length(legend_args == 2) && !is.null(names(legend_args[[1]])) && !is.null(names(legend_args[[2]]))) { if(names(legend_args[[1]]) == names(legend_args[[2]])) { legend_args_1 <- legend_args[[1]] - legend_args_2 <- legend_args_2_base <- legend_args[[2]] + legend_args_2 <- legend_args[[2]] } } else { - legend_args_1 <- legend_args_2 <- legend_args_2_base <- legend_args + legend_args_1 <- legend_args_2 <- legend_args } ## Separating the data @@ -1285,20 +1330,24 @@ plot.test.metric <- function(data, specific.args, ...) { ## Set up the legend arguments if(plot_legend) { - if(is.null(legend_args_1$x)) { - legend_args_1$x <- "bottomright" + leg_args_1 <- legend_args_1 + if(is.null(leg_args_1$x)) { + leg_args_1$x <- "bottomright" } - if(is.null(legend_args_1$legend)) { - legend_args_1$legend <- names(plot_data) + if(is.null(leg_args_1$cex)) { + leg_args_1$cex <- 2/3 } - if(is.null(legend_args_1$pch)) { - legend_args_1$pch <- plot_args$pch + if(is.null(leg_args_1$legend)) { + leg_args_1$legend <- names(plot_data) } - if(is.null(legend_args_1$col)) { - legend_args_1$col <- col_vector + if(is.null(leg_args_1$pch)) { + leg_args_1$pch <- plot_args$pch + } + if(is.null(leg_args_1$col)) { + leg_args_1$col <- col_vector } ## Plot the legend - do.call(legend, legend_args_1) + do.call(legend, leg_args_1) } } @@ -1307,11 +1356,12 @@ plot.test.metric <- function(data, specific.args, ...) { ## Get the fit of the first model fit <- add.fit(model_groups[[one_plot]][[1]]) + ## Slope for the first model add.slope(model_groups[[one_plot]][[1]], col = col_vector[1]) ## Get the eventual second fit - if(!is.null(model_groups[[one_plot]][[1]])) { + if(length(model_groups[[one_plot]]) > 1) { ## Fit for the second model fit <- c(fit, add.fit(model_groups[[one_plot]][[2]])) ## Slope for the second model @@ -1323,22 +1373,24 @@ plot.test.metric <- function(data, specific.args, ...) { ## Set up the legend arguments if(plot_legend) { - if(is.null(legend_args_2$x)) { - legend_args_2$x <- "topright" + leg_args_2 <- legend_args_2 + if(is.null(leg_args_2$x)) { + leg_args_2$x <- "topright" + } + if(is.null(leg_args_2$cex)) { + leg_args_2$cex <- 2/3 } - if(is.null(legend_args_2$legend)) { - legend_args_2$legend <- fit[!na_fit] + if(is.null(leg_args_2$legend)) { + leg_args_2$legend <- fit[!na_fit] } - if(is.null(legend_args_2$lty)) { - legend_args_2$lty <- c(1,1)[!na_fit] + if(is.null(leg_args_2$lty)) { + leg_args_2$lty <- c(1,1)[!na_fit] } - if(is.null(legend_args_2$col)) { - legend_args_2$col <- col_vector[!na_fit] + if(is.null(leg_args_2$col)) { + leg_args_2$col <- col_vector[!na_fit] } ## Plot the legend - do.call(legend, legend_args_2) - ## Reinitialise the legend - legend_args_2 <- legend_args_2_base + do.call(legend, leg_args_2) } } } @@ -1403,7 +1455,7 @@ plot.test.metric <- function(data, specific.args, ...) { } ## Plot axes -plot.axes <- function(data, ...) { +do.plot.axes <- function(data, ...) { ## Magic value for below transparency <- 0.5 @@ -1487,7 +1539,7 @@ plot.axes <- function(data, ...) { } ## Plot projections -plot.projection <- function(data, specific.args, cent.tend, ...) { +do.plot.projection <- function(data, specific.args, cent.tend, ...) { dots <- list(...) diff --git a/R/print.dispRity.R b/R/print.dispRity.R index 24f4b294..eab3272b 100755 --- a/R/print.dispRity.R +++ b/R/print.dispRity.R @@ -67,11 +67,12 @@ print.dispRity <- function(x, all = FALSE, ...) { randtest = { ## Remove the call (messy) remove.call <- function(element) { - if(element$call != "dispRity.randtest") { + if(length(grep("dispRity.randtest", element$call)) == 0) { element$call <- "dispRity::null.test" } return(element) } + x <- lapply(x, remove.call) if(length(x) == 1) { @@ -111,7 +112,16 @@ print.dispRity <- function(x, all = FALSE, ...) { cat("\n") if(!is.null(x$p.value)) { - print(x$p.value) + cat("Rank envelope test:\n") + cat(" p-value of the global test: ", attr(x$p.value, "p", exact = TRUE), sep="") + if(!is.null(attr(x$p.value, "ties"))) { + cat(" (ties method: ", attr(x$p.value, "ties"), ")\n", sep="") + } else { + cat("\n") + } + if(!is.null(attr(x$p.value, "p_interval"))) { + cat(" p-interval : (", attr(x$p.value, "p_interval")[1], ", ", attr(x$p.value, "p_interval")[2],")\n", sep="") + } } return(invisible()) @@ -193,6 +203,54 @@ print.dispRity <- function(x, all = FALSE, ...) { class(x) <- "list" print(x) return(invisible()) + }, + pgls.dispRity = { + + ## Modified from phylolm::print.phylolm + ## Print the general info + cat(paste0("phylolm test (pgls) applied to ", length(x), " disparity estimates\n")) + cat(paste0("using the formula: ", Reduce(paste, deparse(x[[1]]$formula))," and the model: ", x[[1]]$model,"\n\n")) + + ## Print fit + print(rbind(pool.pgls.param(x, "aic"), pool.pgls.param(x, "logLik")), ...) + + ## Print param + cat("\nParameter estimate(s) using ML:\n") + if(!is.null(x[[1]]$optpar)) { + opt_param <- pool.pgls.param(x, "optpar") + if (x[[1]]$model %in% c("OUrandomRoot","OUfixedRoot")) { + rownames(opt_param) <- "alpha" + print(opt_param, ...) + } + if (x[[1]]$model %in% c("lambda","kappa","delta")) { + cat(x[[1]]$model,":") + print(opt_param, ...) + } + if (x[[1]]$model=="EB") { + rownames(opt_param) <- "rate" + print(opt_param, ...) + } + cat("\n") + } + + ## Variance + print(pool.pgls.param(x, "sigma2"), ...) + if(x[[1]]$sigma2_error > 0) { + print(pool.pgls.param(x, "sigma2_error"), ...) + } + + ## Print the coefficients + cat("\nCoefficients:\n") + pool_coef <- pool.pgls.param(x, "coefficients") + # rownames(pool_coef) <- names(x[[1]]$coefficients) + print(pool_coef, ...) + + cat(paste0("\nYou can access individual models by using their index (e.g. x[[1]])\nor summarise and plot all models using summary(x) or plot(x).")) + return(invisible()) + }, + multi = { + print.dispRity(dispRity.multi.merge.data(x), ...) + return(invisible()) } ) } @@ -235,17 +293,21 @@ print.dispRity <- function(x, all = FALSE, ...) { "covar" = cat(paste(length(subsets), method[1], "subsets for", nrow(x$matrix[[1]]), "elements")) ) + ## Print the number of matrices if(length(x$matrix) > 1) { - cat(paste0(" in ", length(x$matrix), " matrices"), sep = "") + cat(paste0(" in ", length(x$matrix), ifelse((!is.null(x$call$dispRity.multi) && x$call$dispRity.multi), " separated", ""), " matrices"), sep = "") } else { cat(paste0(" in one matrix"), sep = "") } if(length(x$call$dimensions) != 0) cat(paste(" with", length(x$call$dimensions), "dimensions"), sep = "") + + ## Print the number of trees if(!is.null(x$tree[[1]])) { cat(" with ") ; print(x$tree) } else { cat(":\n") } + if(length(subsets) > 5) { cat(" ",paste(subsets[1:5], collapse=", "),"...\n") } else { @@ -258,10 +320,9 @@ print.dispRity <- function(x, all = FALSE, ...) { if(!is.null(x$call$subsets) && ("covar" %in% x$call$subsets)) { cat(paste0("One covar matrix (", names(x$subsets), ") with ")) } - cat(paste(nrow(x$matrix[[1]]), "elements")) if(length(x$matrix) > 1) { - cat(paste0(" in ", length(x$matrix), " matrices"), sep = "") + cat(paste0(" in ", length(x$matrix), ifelse((!is.null(x$call$dispRity.multi) && x$call$dispRity.multi), " separated", ""), " matrices"), sep = "") } else { cat(paste0(" in one matrix"), sep = "") } @@ -294,6 +355,15 @@ print.dispRity <- function(x, all = FALSE, ...) { if(x$call$disparity$metrics$between.groups) { cat(" between groups") } + + ## Print info from dispRitreats + if(!is.null(x$call$dispRitreats)) { + ## PLACEHOLDER FOR dispRitreats INFO + if(x$call$dispRitreats) { + cat(".\nDisparity was calculated from treats simulated data") + } + } + cat(".\n") } } diff --git a/R/randtest.dispRity.R b/R/randtest.dispRity.R index a18cfc65..367ee710 100755 --- a/R/randtest.dispRity.R +++ b/R/randtest.dispRity.R @@ -2,11 +2,11 @@ #' #' @description Performs a random test (aka permutation test) on a \code{matrix} or a \code{dispRity} object. #' -#' @param data The \code{matrix} to draw from. -#' @param subsets A \code{vector} of elements to test (or a \code{list} of \code{vectors}). +#' @param xtest The \code{matrix} or a \code{dispRity} object to draw from. +#' @param subsets A \code{vector} of elements to test (or a \code{list} of \code{vectors} - see details). #' @param replicates A \code{numeric} value for the number of replicates (\code{default = 100}). #' @param metric A \code{function} to be the statistic to apply to the subset. -#' @param resample \code{logical} whether to resample the full distribution (\code{TRUE}) or the distribution without the subset (\code{FALSE}). +#' @param resample \code{logical} whether to resample the full distribution (\code{TRUE}; default) or the distribution without the subset (\code{FALSE}). #' @param alter The alternative hypothesis. Can be \code{"two-sided"} (default), \code{"greater"} or \code{"lesser"}. #' @param ... optional arguments to be passed to \code{metric}. #' @@ -20,6 +20,8 @@ #' This algorithm is based on a similar procedure than in \code{link[ade4]{rantest}}. #' #' If \code{data} is a \code{dispRity} object, the \code{subsets}, \code{metric} and \code{replicates} can be left missing and are automatically inherited from the \code{dispRity} if it contains respectively subsets (from \code{\link{chrono.subsets}} or \code{\link{custom.subsets}}) a \code{metric} (from \code{\link{dispRity}}) and bootstrap draws (from \code{boot.matrix}). +#' +#' If \code{data} is a \code{dispRity} object subsets can be a list of subsets to compare for example \code{list(c("A", "B"), c("B", "A"))} will run two tests comparing respectively sample A to B and B to A. \emph{Note} that it will only compare these two samples and use their combined size as the population size, if you want to compare a subset to all the subsets you can use \code{list(c("A")} or write down the specific subsets to be used. #' #' @return #' This function returns a \code{"randtest"} object that can be passed to the generic S3 functions \code{\link[ade4]{print.randtest}} or \code{\link[ade4]{plot.randtest}}. @@ -47,28 +49,67 @@ #' ## Plotting the results #' plot(test_disparity) #' +#' ## Applying this on a dispRity object with specific subset comparisons +#' test_disparity2 <- randtest.dispRity(disparity, subsets = list( +#' ## Comparing subset 90 to the whole population (traitspace) +#' c(observed = "90"), +#' ## Comparing subset "70" to "90", "70" and "30" +#' c(observed = "70", random = c("90", "70", "30")))) +#' +#' ## Summarising and plotting the results +#' summary(test_disparity2) +#' plot(test_disparity2) +#' #' @seealso \code{\link[ade4]{randtest}} #' #' @author Thomas Guillerme #' @export -randtest.dispRity <- function(data, subsets, metric, replicates = 100, resample = TRUE, alter = "two-sided", ...) { +randtest.dispRity <- function(xtest, subsets, metric, replicates = 100, resample = TRUE, alter = "two-sided", ...) { match_call <- match.call() args <- list(...) + data <- xtest + names(match_call)[which(names(match_call) == "xtest")] <- "data" + ## Sanitizing ## Distribution and subset data_class <- check.class(data, c("matrix", "dispRity")) inherits_metrics <- inherits_subsets <- FALSE + subsets_names <- NULL if(data_class == "dispRity") { - pop_size <- nrow(data$matrix[[1]]) + + sample_pop <- 1:nrow(data$matrix[[1]]) + pop_names <- rownames(data) if(missing(subsets)) { ## Take subsets from the dispRity object subsets <- lapply(data$subsets, function(x) return(x$elements)) sub_names <- names(subsets) inherits_subsets <- TRUE + ## Set the sampling population list + sample_pop <- replicate(length(subsets), list(sample_pop)) + } else { + ## Get the subset names (if available) + if(!is.null(names(subsets))) { + subsets_names <- names(subsets) + } + + ## Split the subsets in observed and random + subsets_obs <- lapply(subsets, `[`, 1) + subsets_rand <- lapply(subsets, function(x) unname(x[-1])) + + ## Set the comparisons to match the subset list + sample_pop <- lapply(subsets_rand, get.sample.pop, data) + + ## Set the sample pop names + names(sample_pop) <- unlist(lapply(subsets_rand, get.sample.pop.name, data)) + + ## Get the subsets + subsets <- lapply(data$subsets[unlist(subsets_obs)], function(x) return(x$elements)) + sub_names <- names(subsets) + inherits_subsets <- TRUE } if(missing(metric)) { @@ -79,10 +120,10 @@ randtest.dispRity <- function(data, subsets, metric, replicates = 100, resample } else { ## Check the data - pop_size <- nrow(data) + sample_pop <- 1:nrow(data) pop_names <- rownames(data) if(is.null(pop_names)) { - pop_names <- rownames(data) <- 1:pop_size + pop_names <- rownames(data) <- sample_pop } ## Making the data into a dispRity like format @@ -99,16 +140,20 @@ randtest.dispRity <- function(data, subsets, metric, replicates = 100, resample ## Make the subsets into a matrix list subsets <- lapply(subsets, function(x) matrix(x, ncol = 1)) } - all.checks <- function(one_subset, pop_size, pop_names) { - return(((is(one_subset, "numeric") || is(one_subset, "integer") || is(one_subset, "character")) && length(one_subset) > pop_size) || all(one_subset %in% pop_names)) + all.checks <- function(one_subset, sample_pop, pop_names) { + return(((is(one_subset, "numeric") || is(one_subset, "integer") || is(one_subset, "character")) && length(one_subset) > sample_pop) || all(one_subset %in% pop_names)) } - if(any(!unlist(lapply(subsets, all.checks, pop_size, pop_names)))) { + if(any(!unlist(lapply(subsets, all.checks, sample_pop, pop_names)))) { stop("Subsets must be a vector or a list of vector of integers or numeric values that can not exceed the number of rows in data.", call. = FALSE) } + + ## Make the sample pop list + sample_pop <- replicate(length(subsets), list(sample_pop)) } ## Check the metric metrics_list <- get.dispRity.metric.handle(metric, match_call, data = data, ...)$levels + # warning("DEBUG randtest.dispRity") ; metrics_list <- get.dispRity.metric.handle(metric, match_call, data = data)$levels ## Replicates check.class(replicates, c("numeric", "integer")) @@ -149,10 +194,11 @@ randtest.dispRity <- function(data, subsets, metric, replicates = 100, resample verbose <- FALSE ## Make the lapply loop - lapply_loop <- lapply(subsets, make.lapply.loop, replicates, pop_size) + lapply_loop <- mapply(make.lapply.loop, subsets, sample_pop, MoreArgs = list(replicates = replicates), SIMPLIFY = FALSE) ## Calculate all the disparity values disparity <- lapply(lapply_loop, lapply.wrapper, metrics_list, data, matrix_decomposition, verbose, ...) + # warning("DEBUG randtest.dispRity") ; disparity <- lapply(lapply_loop, lapply.wrapper, metrics_list, data, matrix_decomposition, verbose) ## Get the observed values results <- lapply(disparity, one.randtest, replicates, resample, alter, get.p.value, match_call) @@ -166,13 +212,41 @@ randtest.dispRity <- function(data, subsets, metric, replicates = 100, resample if(length(results) == 1) { return(results[[1]]) } else { + ## Get the list of comparisons + if(is.null(names(sample_pop))) { + comparisons <- replicate(n = length(results), "the whole space", simplify = FALSE) + } else { + comparisons <- names(sample_pop) + } + ## Get the call for each test + make.call <- function(subset_name, comparison, resample) { + return(paste0("dispRity.randtest: Subset ", subset_name, " (observed) compared to ", comparison, " (random) ", ifelse(resample, "with", "without"), " resampling.")) + } + + if(is.null(names(results))) { + results_names <- as.list(1:length(results)) + } else { + results_names <- as.list(names(results)) + } + + call_summary <- mapply(make.call, results_names, comparisons, MoreArgs = list(resample = resample), SIMPLIFY = FALSE) + ## For match_call - results <- lapply(results, function(x) {x$call <- "dispRity.randtest"; x}) + results <- mapply(function(results, call_summary) {results$call <- call_summary; return(results)}, results, call_summary, SIMPLIFY = FALSE) + ## Add names to the results (if any) - if(!is.null(sub_names)) { - names(results) <- sub_names + if(!is.null(subsets_names)) { + ## Input names + names(results) <- subsets_names + } else { + ## Auto naming (if possible) + if(!is.null(sub_names)) { + names(results) <- sub_names + } } + class(results) <- c("dispRity", "randtest") + return(results) } } \ No newline at end of file diff --git a/R/randtest.dispRity_fun.R b/R/randtest.dispRity_fun.R index 32e08748..5b227455 100755 --- a/R/randtest.dispRity_fun.R +++ b/R/randtest.dispRity_fun.R @@ -1,9 +1,15 @@ -make.lapply.loop.resample <- function(one_subset, replicates, pop_size) { - return(list("elements" = one_subset, replicate(replicates, sample(1:pop_size, length(one_subset), replace = TRUE)))) +make.lapply.loop.resample <- function(one_subset, one_sample_pop, replicates) { + return(list("elements" = one_subset, replicate(replicates, sample(one_sample_pop, length(one_subset), replace = TRUE)))) } -make.lapply.loop.nosample <- function(one_subset, replicates, pop_size) { - return(list("elements" = one_subset, replicate(replicates, sample((1:pop_size)[-one_subset], length(one_subset), replace = TRUE)))) +make.lapply.loop.nosample <- function(one_subset, one_sample_pop, replicates) { + ## Check if the subset is not the whole population + if(all(c(one_subset) %in% one_sample_pop)) { + ## If it is, use make.lapply.loop.resample + return(make.lapply.loop.resample(one_subset, one_sample_pop, replicates)) + } else { + return(list("elements" = one_subset, replicate(replicates, sample((one_sample_pop)[-one_subset], length(one_subset), replace = TRUE)))) + } } one.randtest <- function(results, replicates, resample, alternative, get.p.value, match_call) { @@ -44,4 +50,29 @@ one.randtest <- function(results, replicates, resample, alternative, get.p.value class(res) <- "randtest" return(res) +} + + +## Get the comparisons to match the subset list +get.sample.pop <- function(one_rand, data) { + if(length(one_rand) == 0) { + return(1:nrow(data$matrix[[1]])) + } else { + return(sort(unique(unlist(lapply(data$subsets[unlist(one_rand)], function(x) return(x$elements)))))) + } +} + +get.sample.pop.name <- function(one_rand, data) { + if(length(one_rand) == 0) { + return("the whole space") + } else { + + orthograph <- paste0("subset", ifelse(length(one_rand) == 1, " ", "s ")) + + if(length(one_rand) <= 2) { + return(paste0(orthograph, paste(one_rand, collapse = " and "))) + } else { + return(paste0(orthograph, paste(c(paste(one_rand[-length(one_rand)], collapse = ", "), one_rand[length(one_rand)]), collapse = " and "))) + } + } } \ No newline at end of file diff --git a/R/reduce.space.R b/R/reduce.space.R index 1259990d..f95c8ec3 100755 --- a/R/reduce.space.R +++ b/R/reduce.space.R @@ -156,7 +156,6 @@ reduce.space <- function(space, type, remove, parameters, tuning, verbose = FALS check.class(verbose, "logical") check.class(return.optim, "logical") - ## Select the reduction type algorithm switch(type, random = { @@ -238,23 +237,32 @@ reduce.space <- function(space, type, remove, parameters, tuning, verbose = FALS ## List of arguments args <- list("space" = space, "parameters" = parameters) ## Run the complex removal - to_remove <- do.call(fun, args) + to_remove <- list(remove = do.call(fun, args)) ## Optimise the function (if necessary) if(!missing(remove)) { ## Get out of the corner case of all being TRUE or FALSE - if(all(to_remove) || all(!to_remove)) { + if(all(to_remove$remove) || all(!to_remove$remove)) { args$parameters$optimise <- runif(1) - to_remove <- do.call(fun, args) + to_remove <- list(remove = do.call(fun, args)) } ## Optimise - to_remove <- optimise.results(to_remove, fun = fun, remove = remove, args = args, tuning = tuning, verbose = verbose, space = space, return.optim = return.optim) + to_remove <- optimise.results(to_remove$remove, fun = fun, remove = remove, args = args, tuning = tuning, verbose = verbose, space = space, return.optim = return.optim) + + ## Try 25 more times if necessary + counter <- 0 + while(all(to_remove$remove) || all(!to_remove$remove) && counter != 26) { + args$parameters$optimise <- runif(1) + to_remove <- list(remove = do.call(fun, args)) + to_remove <- optimise.results(to_remove$remove, fun = fun, remove = remove, args = args, tuning = tuning, verbose = verbose, space = space, return.optim = return.optim) + counter <- counter + 1 + } } if(!return.optim) { - return(to_remove) + return(to_remove$remove) } else { return(list(remove = to_remove$remove, optim = to_remove$optim)) } diff --git a/R/reduce.space_fun.R b/R/reduce.space_fun.R index 6b4c8f26..e66ab63f 100755 --- a/R/reduce.space_fun.R +++ b/R/reduce.space_fun.R @@ -95,7 +95,7 @@ optimise.results <- function(to_remove, fun, remove, args, tuning, verbose = FAL } if(!return.optim) { - return(to_remove) + return(list(remove = to_remove)) } else { return(list(remove = to_remove, optim = args$parameters$optimise)) } diff --git a/R/remove.zero.brlen.R b/R/remove.zero.brlen.R index 31c7b268..735b2f12 100755 --- a/R/remove.zero.brlen.R +++ b/R/remove.zero.brlen.R @@ -1,14 +1,14 @@ #' @title Remove zero branch length #' -#' @description Remove zero branch lengths on trees by sliding nodes randomly in a postorder traversal based on \code{\link{slide.nodes}}. +#' @description Remove zero or negative branch lengths on trees by sliding nodes randomly in a postorder traversal based on \code{\link{slide.nodes}}. #' -#' @param tree A \code{"phylo"} object with edge lengths +#' @param tree A \code{"phylo"} or \code{"multiPhylo"} object with edge lengths #' @param slide An optional sliding \code{numeric} values. If left empty, 1\% of the shortest branch length is used. #' @param verbose A \code{logical} value indicating whether to be verbose or not. #' #' @details #' The sliding value will be used to slide the nodes up and down to remove zero branch lengths by minimising the amount of branch changes. -#' The algorithm slides the nodes up and down (when possible) on each node in a recursive way while there is still zero branch lengths. +#' The algorithm slides the nodes up and down (when possible) on each node in a recursive way while there is still zero or negative branch lengths. #' If two recursions produce the same series of zero branches (e.g. by sliding node A towards node B equally so that the distance A:B becomes 0), the sliding value is divided by two until the next slide. #' #' @return A \code{"phylo"} object with a postorder edge table and no zero branch lengths. @@ -34,6 +34,17 @@ #' plot(tree_no_zero, main = "no zero branch length") #' plot(tree_exaggerated, main = "exaggerated slidding") #' +#' ## Removing negative branch lengths +#' ## Generating a tree with negative branch length +#' set.seed(3) +#' tree_negative <- chronoMPL(rtree(10)) +#' ## Removing the negative branch length (and make it non-zero) +#' tree_positive <- remove.zero.brlen(tree_negative) +#' ## Plot the differences +#' par(mfrow = c(2, 1)) +#' plot(tree_negative, main = "Negative branch lengths") +#' plot(tree_positive, main = "Positive branch lengths") +#' #' @seealso #' \code{\link{slide.nodes}} #' @@ -44,20 +55,27 @@ remove.zero.brlen <- function(tree, slide, verbose = FALSE) { match_call <- match.call() ## Tree class - check.class(tree, "phylo") + tree_class <- check.class(tree, c("phylo", "multiPhylo")) + + ## multiPhylo version + if(tree_class == "multiPhylo") { + out <- lapply(tree, remove.zero.brlen, slide, verbose) + class(out) <- "multiPhylo" + return(out) + } ## Reorder in postorder tree_bkp <- tree tree <- reorder(tree, "postorder") ## Return the tree if no zero branch lengths - if(length(which(tree$edge.length == 0)) == 0) { + if(length(which(tree$edge.length <= 0)) == 0) { return(tree) } ## Configure sliding if(missing(slide)) { - slide <- 0.01 * min(tree$edge.length[-which(tree$edge.length == 0)]) + slide <- 0.01 * min(tree$edge.length[-which(tree$edge.length <= 0)]) } else { check.class(slide, c("numeric", "integer")) check.length(slide, 1, " must be a single numeric value.") @@ -87,16 +105,25 @@ remove.zero.brlen <- function(tree, slide, verbose = FALSE) { direction <- 2 } } else { - ## Get a random direction - direction <- sample(c(1, 2), 1) + ## Is it a negative edge? + edge <- which((tree$edge[,1] == node_pair[1]) & (tree$edge[,2] == node_pair[2])) + if(tree$edge.length[edge] < 0) { + ## Go to the right + direction <- 2 + ## Slide is bigger + slide <- slide - tree$edge.length[edge] + } else { + ## Get a random direction + direction <- sample(c(1, 2), 1) + } } ## Slide the node - tree_slided <- slide.nodes.internal(tree, node_pair[direction], ifelse(direction == 1, -slide, slide)) + tree_slided <- slide.nodes.internal(tree, node_pair[direction], ifelse(direction == 1, -slide, slide), allow.negative.root = FALSE) ## Try reversing the slide if(is.null(tree_slided) && !any(is.na(node_pair))) { - tree_slided <- slide.nodes.internal(tree, node_pair[-direction], ifelse(direction == 1, slide, -slide)) + tree_slided <- slide.nodes.internal(tree, node_pair[-direction], ifelse(direction == 1, slide, -slide), allow.negative.root = FALSE) } return(tree_slided) } @@ -105,7 +132,7 @@ remove.zero.brlen <- function(tree, slide, verbose = FALSE) { recursive.remove.zero.brlen <- function(tree, slide, max.it, verbose, zero_brlen_tracker) { ## Get the zero branch length - zero_brlen <- which(tree$edge.length == 0) + zero_brlen <- which(tree$edge.length <= 0) ## Get the zero nodes zero_nodes <- unique(c(tree$edge[(1:nrow(tree$edge) %in% zero_brlen), 1], tree$edge[(1:nrow(tree$edge) %in% zero_brlen), 2])) @@ -179,14 +206,14 @@ remove.zero.brlen <- function(tree, slide, verbose = FALSE) { ## Verbose - if(verbose) cat(paste0("Changing ", length(which(tree$edge.length == 0)), " branch lengths:")) + if(verbose) cat(paste0("Changing ", length(which(tree$edge.length <= 0)), " branch lengths:")) ## Initialising the tracker - zero_brlen_tracker <- list("current" = which(tree$edge.length == 0)) + zero_brlen_tracker <- list("current" = which(tree$edge.length <= 0)) ## Placeholder for a max.it option max.it <- 1000000 - ## Test + ## Remove zeros and negatives tree <- recursive.remove.zero.brlen(tree, slide, max.it, verbose, zero_brlen_tracker) if(verbose) cat("Done.") diff --git a/R/sanitizing.R b/R/sanitizing.R index f898577f..55bb8834 100755 --- a/R/sanitizing.R +++ b/R/sanitizing.R @@ -146,9 +146,13 @@ add.rownames <- function(x) { rownames(x) <- seq(1:nrow(x)) return(x) } + ## Checks whether the data is a matrix or a list -check.dispRity.data <- function(data) { - match_call <- match.call() +check.data <- function(data, match_call) { + + ## Multi toggle + is_multi <- FALSE + ## Check class data_class <- check.class(data, c("matrix", "data.frame", "list")) @@ -169,9 +173,8 @@ check.dispRity.data <- function(data) { if(!all(all_classes %in% c("matrix", "array"))) { stop.call(match_call$data, is_error) } - ## Check the dimensions - all_dim <- unique(unlist(lapply(data, dim))) - if(!(length(all_dim) %in% c(1,2))) { + ## Check the columns + if(length(unique(unlist(lapply(data, ncol)))) > 1) { stop.call(match_call$data, is_error) } ## Check the rownames @@ -179,15 +182,18 @@ check.dispRity.data <- function(data) { ## If no rownames, add them data <- lapply(data, add.rownames) warning(row_warning) + } + ## Find the rows that are not in common between matrices + rows <- unique(unlist(lapply(data, rownames))) + unmatch <- unlist(lapply(data, function(x, rows) return(rows[!rows %in% rownames(x)]), rows = rows)) + if(length(unmatch) > 0) { + ## Warning rows + warning(paste0("The following elements are not present in all matrices: ", paste0(unmatch, collapse = ", "), ". The matrices will be treated as separate trait-spaces."), call. = FALSE) + ## Toggle multi + is_multi <- TRUE } else { - ## Check the rownames - check_rows <- unique(unlist(lapply(data, rownames))) - if(length(check_rows) != all_dim[1]) { - stop.call(match_call$data, is_error) - } - ## Sort the rownames softly (i.e. in the order of the first matrix) + ## Reorder the rows to match the first matrix data <- lapply(data, function(x, order) x[order, , drop = FALSE], order = rownames(data[[1]])) - # data <- lapply(data, function(x) x[order(rownames(x)), exact = TRUE, drop = FALSE]) } } else { ## Eventually add rownames @@ -197,12 +203,14 @@ check.dispRity.data <- function(data) { } data <- list(data) } - return(data) + return(list(matrix = data, multi = is_multi)) } - ## Checks whether the tree is in the correct format -check.dispRity.tree <- function(tree, data, bind.trees = FALSE) { +check.tree <- function(tree, data, bind.trees = FALSE, match_call) { + ## multi toggle + is_multi <- FALSE + ## Check class tree_class <- check.class(tree, c("phylo", "multiPhylo")) ## Convert into a list (not multiPhylo if it's a single tree) @@ -211,14 +219,24 @@ check.dispRity.tree <- function(tree, data, bind.trees = FALSE) { class(tree) <- "multiPhylo" } + ## Bind tree auto-toggle + if(length(tree) != 1 && (length(tree) == length(data$matrix))) { + bind.trees <- TRUE + } + ## Inc.nodes toggle inc.nodes <- unique(unlist(lapply(tree, function(x) !is.null(x$node.label)))) if(length(inc.nodes) > 1) { stop("All trees should have node labels or no node labels.", call. = FALSE) } + ## Double check the inc.nodes + if(inc.nodes) { + ## ignore node labels if no overlap + inc.nodes <- any(unlist(mapply(function(x, y) any(rownames(x) %in% y$node.label), data$matrix, tree, SIMPLIFY = FALSE))) + } ## Make the data into "dispRity" format for testing - if(!missing(data) && is.array(data)) { + if(!missing(data) && is.array(data) || is.null(names(data))) { if(!is(data, "dispRity")) { data <- list(matrix = list(data)) } @@ -229,12 +247,30 @@ check.dispRity.tree <- function(tree, data, bind.trees = FALSE) { ## Match the data and the trees? pass.fun <- function(cleaned) return(!all(is.na(cleaned$dropped_tips), is.na(cleaned$dropped_rows))) if(!bind.trees) { - cleanings <- lapply(data$matrix, clean.data, tree, inc.nodes = inc.nodes) + if(length(tree) != length(data$matrix)) { + ## Check multi trees + dropped <- check.multi.tree(tree, data, inc.nodes) + + ## Toggle multi + is_multi <- !all(unlist(lapply(tree, function(x, y) all(c(x$node.label, x$tip.label) %in% c(y$node.label, y$tip.label)), y = tree[[1]]))) + if(length(dropped) > 0) { + ## Warning + warning(paste0("The following elements are not present in all trees: ", paste0(dropped, collapse = ", "), ". Some analyses downstream might not work because of this (you can use ?clean.data to match both data and tree if needed)."), call. = FALSE) + } + ## Done for here + return(list(tree = tree, multi = is_multi)) + } else { + ## Normal cleaning check + options(warn = -1) + cleanings <- lapply(data$matrix, clean.data, tree, inc.nodes = inc.nodes) + options(warn = 0) + } } else { if(length(tree) != length(data$matrix)) { stop("The number of matrices and trees must be the same to bind them.", call. = FALSE) } - cleanings <- mapply(clean.data, data$matrix, tree, MoreArgs = c(inc.nodes = inc.nodes), SIMPLIFY = FALSE) + ## Clean + cleanings <- mapply(clean.data, data$matrix, tree, MoreArgs = list(inc.nodes = inc.nodes), SIMPLIFY = FALSE) } if(any(not_pass <- unlist(lapply(cleanings, pass.fun)))) { ## Stop! @@ -242,5 +278,57 @@ check.dispRity.tree <- function(tree, data, bind.trees = FALSE) { } } - return(tree) + return(list(tree = tree, multi = is_multi)) +} +## Check the match between multiple trees and data +check.multi.tree <- function(tree, data, inc.nodes) { + ## Get all the labels from the matrices and make a dummy one + all_elements <- unique(unlist(lapply(data$matrix, rownames))) + dummy_data <- matrix(1, nrow = length(all_elements), dimnames = list(all_elements)) + ## Make a dummy matrix with these + cleanings <- lapply(tree, function(tree, data, inc.nodes) clean.data(data, tree, inc.nodes), data = dummy_data, inc.nodes = inc.nodes) + dropped <- unique(unlist(lapply(cleanings, function(x) return(c(x$dropped_rows, x$dropped_tips))))) + if(any(is.na(dropped))) { + dropped <- dropped[-which(is.na(dropped))] + } + return(dropped) +} + +## Wrapper function for checking the data +check.dispRity.data <- function(data = NULL, tree = NULL, bind.trees = FALSE, returns = c("matrix", "tree", "multi")) { + + match_call <- match.call() + is_multi <- FALSE + + ## Checking the data + if(!is.null(data) && !is(data, "dispRity")) { + data <- check.data(data, match_call) + is_multi <- any(is_multi, data$multi) + data$multi <- NULL + } + + ## Checking the tree + if(!is.null(tree)) { + tree <- check.tree(tree, data, bind.trees, match_call) + is_multi <- any(is_multi, tree$multi) + } + + ## Sort the output + output <- list() + if("matrix" %in% returns) { + output$matrix <- data$matrix + } + if("tree" %in% returns) { + output$tree <- tree$tree + } + if("multi" %in% returns) { + output$multi <- is_multi + } + + ## Output + if(length(returns) == 1) { + return(output[[1]]) + } else { + return(output) + } } diff --git a/R/slice.tree.R b/R/slice.tree.R index 01e73643..fe40e6b1 100755 --- a/R/slice.tree.R +++ b/R/slice.tree.R @@ -1,6 +1,6 @@ #' @title Time slicing a tree. #' -#' @usage slice.tree(tree, age, model, FAD, LAD) +#' @usage slice.tree(tree, age, model, FAD, LAD, keep.all.ancestors = FALSE) #' #' @description Time slicing through a phylogenetic tree. #' @@ -8,13 +8,14 @@ #' @param age A single \code{numeric} value indicating where to perform the slice. #' @param model One of the following models: \code{"acctran"}, \code{"deltran"}, \code{"random"}, \code{"proximity"}, \code{"equal.split"} or \code{"gradual.split"}. Is ignored if \code{method = "discrete"}. See \code{\link{chrono.subsets}} for the models description. #' @param FAD,LAD The first and last occurrence data. +#' @param keep.all.ancestors Optional, whether to also include the ancestors of the tree slice (\code{TRUE}) or just the ones linking the elements present at the slice (\code{FALSE}; default) #' #' @seealso \code{paleotree::timeSliceTree}, \code{\link{chrono.subsets}}. #' #' @examples #' set.seed(1) #' ## Generate a random ultrametric tree -#' tree <- rcoal(20) +#' tree <- rtree(20) #' #' ## Add some node labels #' tree$node.label <- letters[1:19] @@ -22,8 +23,21 @@ #' ## Add its root time #' tree$root.time <- max(tree.age(tree)$ages) #' -#' ## Slice the tree at age 0.75 -#' tree_75 <- slice.tree(tree, age = 0.75, "deltran") +#' ## Slice the tree at age 1.5 +#' tree_slice <- slice.tree(tree, age = 1.5, "deltran") +#' +#' ## The slice at age 0.5 but keeping all the ancestors +#' deep_slice <- slice.tree(tree, age = 0.5, "deltran", +#' keep.all.ancestors = TRUE) +#' +#' ## Visualising the trees +#' old_par <- par(mfrow = c(2,2)) +#' plot(ladderize(tree), main = "full tree"); axisPhylo() +#' abline(v = tree$root.time - 1.5) +#' plot(ladderize(tree_slice), main = "tree slice"); axisPhylo() +#' plot(ladderize(deep_slice), main = "slice with ancestors"); axisPhylo() +#' +#' par(old_par) #' #' @author Thomas Guillerme # @export @@ -37,7 +51,7 @@ # source("sanitizing.R") #Function modified from paleotree::timeSliceTree -slice.tree <- function(tree, age, model, FAD, LAD) { +slice.tree <- function(tree, age, model, FAD, LAD, keep.all.ancestors = FALSE) { #For adding modules (i.e. models) follow the format # tree_slice<-timeSliceTree(tree, age, drop.extinct = TRUE, plot = FALSE) @@ -88,7 +102,7 @@ slice.tree <- function(tree, age, model, FAD, LAD) { ## Don't slice the tree if age is too old return(NA) } else { - tree_slice <- slice.tree.sharp(tree, age) + tree_slice <- slice.tree.sharp(tree, age, keep.all.ancestors = keep.all.ancestors) if(is.null(tree_slice)) { return(slice.edge(tree, age, model)) } diff --git a/R/slice.tree_fun.R b/R/slice.tree_fun.R index 7595d2cf..bd738828 100755 --- a/R/slice.tree_fun.R +++ b/R/slice.tree_fun.R @@ -2,7 +2,7 @@ ## This function is modified from Dave Bapst paleotree::timeSliceTree (2019/06/19) ## (returns null when failure) -slice.tree.sharp <- function(tree, slice) { +slice.tree.sharp <- function(tree, slice, keep.all.ancestors = FALSE) { ## Get slice time slice_time <- tree$root.time - slice @@ -48,6 +48,12 @@ slice.tree.sharp <- function(tree, slice) { ## Get the node tips depth n_tips_sliced <- Ntip(tree_sliced) tips_depth <- castor::get_all_pairwise_distances(tree_sliced)[n_tips_sliced + 1, 1:n_tips_sliced] + + ## Return the tree with ancestors (if needed) + if(keep.all.ancestors) { + return(tree_sliced) + } + ## Find tips that do not have the slice age #slice_age <- max(tips_depth) tips_at_slice <- (tips_depth == slice_time) diff --git a/R/slide.nodes.R b/R/slide.nodes.R index 054ddbff..b74cd7b3 100755 --- a/R/slide.nodes.R +++ b/R/slide.nodes.R @@ -2,9 +2,10 @@ #' #' @description Stretches a phylogenetic tree at a particular node #' -#' @param nodes A list of the ID nodes to slide (\code{"integer"}). The first node is \code{ape::Ntip(tree) + 1}, etc. +#' @param nodes A list of the ID nodes to slide (\code{"integer"}) or names (\code{"character"}). The first node is \code{ape::Ntip(tree) + 1}, etc. #' @param tree a \code{"phylo"} object. #' @param slide the sliding value. +#' @param allow.negative.root logical, whether to allow negative branch lengths and moving the root node (\code{TRUE}) or not (\code{FALSE}; default). #' #' @details #' The sliding works by subtracting the slide value to the branch leading to the node and adding it to the descendant branches. @@ -48,16 +49,27 @@ #' @author Thomas Guillerme #' @export -slide.nodes <- function(nodes, tree, slide) { +slide.nodes <- function(nodes, tree, slide, allow.negative.root = FALSE) { ## Sanitizing - check.class(nodes, c("integer", "numeric")) + check.class(allow.negative.root, "logical") + node_class <- check.class(nodes, c("integer", "numeric", "character")) + + ## Getting the node IDs (if character) + if(node_class == "character") { + if(is.null(tree$node.label)) { + stop("The tree has no node labels, provide the nodes as integers.") + } + nodes <- which(tree$node.label %in% nodes) + Ntip(tree) + } nodes <- as.integer(nodes) check.class(tree, "phylo") ## Check whether nodes exist in the tree if(any(nodes > (Nnode(tree)+Ntip(tree)))) stop("node(s) not found in tree.") if(any(nodes < Nnode(tree))) stop("node(s) not found in tree.") - if(any(nodes == (Ntip(tree)+1))) warning(paste0("The parent of the root node (", (Ntip(tree) + 1), ") cannot be slideed.")) + if(!allow.negative.root) { + if(any(nodes == (Ntip(tree)+1))) warning(paste0("The parent of the root node (", (Ntip(tree) + 1), ") cannot be slid.")) + } ## Check whether the tree has edge lengths if(is.null(tree$edge.length)) stop("The tree has no edge lengths.") @@ -66,7 +78,7 @@ slide.nodes <- function(nodes, tree, slide) { check.class(slide, c("numeric", "integer")) ## Slide the nodes - tree <- slide.nodes.internal(tree, nodes, slide) + tree <- slide.nodes.internal(tree, nodes, slide, allow.negative.root) ## Catch eventual errors if(is.null(tree)) { diff --git a/R/slide.nodes_fun.R b/R/slide.nodes_fun.R index 1b0d5fdd..343846e0 100755 --- a/R/slide.nodes_fun.R +++ b/R/slide.nodes_fun.R @@ -1,5 +1,5 @@ ## Internal for slide.nodes -slide.nodes.internal <- function(tree, nodes, slide) { +slide.nodes.internal <- function(tree, nodes, slide, allow.negative.root) { ## Find the parent and descendants parent_edge <- which(tree$edge[,2] %in% nodes) descendant_edge <- which(tree$edge[,1] %in% nodes) @@ -14,7 +14,7 @@ slide.nodes.internal <- function(tree, nodes, slide) { } tree$edge.length[descendant_edge] <- tree$edge.length[descendant_edge] - slide ## Check for negatives - if(any(tree$edge.length[descendant_edge] < 0)) { + if(!allow.negative.root && any(tree$edge.length[descendant_edge] < 0)) { return(NULL) } return(tree) diff --git a/R/space.maker.R b/R/space.maker.R index 87c00775..6b539ad3 100755 --- a/R/space.maker.R +++ b/R/space.maker.R @@ -10,6 +10,8 @@ #' @param arguments Optional \code{list} of arguments to be passed to the distributions functions in the order they appear (\code{default = NULL}, see details). #' @param cor.matrix An optional correlation \code{matrix} of size \code{dimensions * dimensions} (\code{default = NULL}, see details). #' @param scree An optional proportional \code{numeric} vector for approximating the \code{dimensions} variance (\code{default = NULL}, see details). +#' @param elements.names Optional, a \code{character} or \code{integer} string for naming the elements in the matrix. +#' @param replicates Optional, an \code{integer} to replicate the simulations and generating multiple spaces. #' #' @details #' When passing additional arguments to different distributions, these must be given as a \code{list} to each function in the order they appear. @@ -45,7 +47,10 @@ #' space <- space.maker(10000, 3, rnorm, scree = c(0.6, 0.3, 0.1)) #' ## The resulting screeplot #' barplot(apply(space, 2, var)) -#' +#' +#' ## Generate 3 2D normal spaces with rownames +#' space.maker(10, 2, rnorm, elements.names = letters[1:10], replicates = 3) +#' #' \dontrun{ #' require(scatterplot3d) #' ## A cube space @@ -102,9 +107,8 @@ # scatterplot3d(space, pch = 20) -space.maker <- function(elements, dimensions, distribution, arguments = NULL, cor.matrix = NULL, scree = NULL) { +space.maker <- function(elements, dimensions, distribution, arguments = NULL, cor.matrix = NULL, scree = NULL, elements.names = NULL, replicates = NULL) { ## SANITZING - match_call <- match.call() ## elements @@ -181,6 +185,20 @@ space.maker <- function(elements, dimensions, distribution, arguments = NULL, co # } } + ## element names + if(!is.null(elements.names)) { + check.class(elements.names, c("character", "integer", "numeric")) + check.length(elements.names, elements, msg = " must be the same as the number of elements", errorif = FALSE) + } + + ## replicates + if(!is.null(replicates)) { + check.class(replicates, c("integer", "numeric")) + return(replicate(replicates, + space.maker(elements, dimensions, distribution, arguments, cor.matrix, scree, elements.names, replicates = NULL) + , simplify = FALSE)) + } + ## CREATE THE SPACE ## with only one distribution if(uni_distribution) { @@ -234,6 +252,11 @@ space.maker <- function(elements, dimensions, distribution, arguments = NULL, co space <- space %*% diag(scree) } + ## Add the row names if needed + if(!is.null(elements.names)) { + rownames(space) <- elements.names + } + #output return(space) } diff --git a/R/summary.dispRity.R b/R/summary.dispRity.R index 57590585..c1c42f22 100755 --- a/R/summary.dispRity.R +++ b/R/summary.dispRity.R @@ -244,6 +244,12 @@ summary.dispRity <- function(object, ..., quantiles = c(50, 95), cent.tend = med return(lapply(data, summary.dispRity, quantiles = quantiles, cent.tend = cent.tend, recall = FALSE)) } + if(is(data, "pgls.dispRity")) { + + ## SUmmarise pgls.dispRity output into phylolm + return(convert.to.summary.phylolm(data)) + } + ## No dual class summary available stop.call("", paste0("No specific summary for combined class \"dispRity\" and \"", class(data)[2], "\".")) } diff --git a/R/summary.dispRity_fun.R b/R/summary.dispRity_fun.R index 18694063..f475c56f 100755 --- a/R/summary.dispRity_fun.R +++ b/R/summary.dispRity_fun.R @@ -9,7 +9,7 @@ get.summary <- function(disparity_subsets_rare, cent.tend, quantiles, ...) { output <- list() ## Summarising NA - if(is.na(disparity_subsets_rare[[1]])) { + if(all(is.na(disparity_subsets_rare))) { if(!missing(cent.tend)) { output$cent.tend <- NA } @@ -71,7 +71,7 @@ get.digit <- function(column) { } -round.column <- function(column, digits) { +column.round <- function(column, digits) { ## Get the digits value digits <- ifelse(digits != "default", digits, get.digit(as.numeric(column))) ## Round the table @@ -91,7 +91,7 @@ digits.fun <- function(results_table, digits, model.test = FALSE) { ## Apply the digits rounded_table <- as.matrix(results_table[,c(start_column:ncol(results_table))]) - rounded_table <- apply(rounded_table, 2, round.column, digits) + rounded_table <- apply(rounded_table, 2, column.round, digits) results_table[,c(start_column:ncol(results_table))] <- rounded_table return(results_table) diff --git a/R/test.dispRity.R b/R/test.dispRity.R index 26e48f20..b6e26f20 100755 --- a/R/test.dispRity.R +++ b/R/test.dispRity.R @@ -57,7 +57,7 @@ #' concatenate = FALSE, correction = "bonferroni", #' conc.quantiles = c(mean, c(95, 5))) #' -#' @seealso \code{\link{dispRity}}, \code{\link{null.test}}, \code{\link{bhatt.coeff}}, \code{\link{pair.plot}}, \code{\link{adonis.dispRity}}, \code{\link{randtest.dispRity}} +#' @seealso \code{\link{dispRity}}, \code{\link{null.test}}, \code{\link{bhatt.coeff}}, \code{\link{pair.plot}}, \code{\link{adonis.dispRity}}, \code{\link{randtest.dispRity}}, \code{\link{test.dispRity}} # \code{\link{sequential.test}} #' #' @author Thomas Guillerme @@ -102,6 +102,13 @@ test.dispRity <- function(data, test, comparisons = "pairwise", rarefaction = NU if(is.null(data$call$disparity)) { stop.call("", "Disparity has not been calculated yet.\nUse the dispRity() function to do so.\n") } + + ## Overriding for pgls + if(is(test, "function") && as.character(match_call$test)[[1]] == "pgls.dispRity") { + ## Attempt to run the pgls + return(pgls.dispRity(data = data, ...)) + } + ## ...and must have more than one subsets if(length(data$subsets) == 1){ stop.call(match_call$data, " must have more than one subset.") diff --git a/R/test.dispRity_fun.R b/R/test.dispRity_fun.R index 7fb200b3..2b3d25dc 100755 --- a/R/test.dispRity_fun.R +++ b/R/test.dispRity_fun.R @@ -27,19 +27,19 @@ convert.to.numeric <- function(list, object) { } ## Getting the names (character) (convert.to.character internal) -names.fun <- function(list, object) { +name.fun <- function(list, object) { return(names(object[c(list)])) } ## convert a list from numeric to character convert.to.character <- function(list, object) { ## Applying to the list - return(lapply(list, names.fun, object)) + return(lapply(list, name.fun, object)) } ## function for repeating the extracted_data names (list to table internal) -rep.names <- function(name, subsets) { +repeat.names <- function(name, subsets) { return(rep(name, subsets)) } @@ -56,7 +56,7 @@ list.to.table <- function(extracted_data) { subsets_length <- unlist(lapply(extracted_data, length), recursive = FALSE) ## Create the data.frame - output <- data.frame("data" = unlist(extracted_data), row.names = NULL, "subsets" = unlist(mapply(rep.names, names_list, subsets_length, SIMPLIFY = FALSE))) + output <- data.frame("data" = unlist(extracted_data), row.names = NULL, "subsets" = unlist(mapply(repeat.names, names_list, subsets_length, SIMPLIFY = FALSE))) ## Transform groups to numeric # if(style == "binomial") { diff --git a/R/test.metric.R b/R/test.metric.R index d9d98080..b9e0c7fd 100755 --- a/R/test.metric.R +++ b/R/test.metric.R @@ -90,8 +90,6 @@ #' #' @references #' Guillerme T, Puttick MN, Marcy AE, Weisbecker V. \bold{2020} Shifting spaces: Which disparity or dissimilarity measurement best summarize occupancy in multidimensional spaces?. Ecol Evol. 2020;00:1-16. (doi:10.1002/ece3.6452) - - test.metric <- function(data, metric, ..., shifts, shift.options, model, replicates = 3, steps = 10, dimensions, verbose = FALSE, save.steps = FALSE) { ## Saving the call @@ -104,7 +102,7 @@ test.metric <- function(data, metric, ..., shifts, shift.options, model, replica metric_name <- NULL if(!is(data, "dispRity")) { options(warn = -1) - data <- check.dispRity.data(data) + data <- check.dispRity.data(data, returns = "matrix") options(warn = 0) } else { ## See if the metric needs to be recycled diff --git a/R/test.metric_fun.R b/R/test.metric_fun.R index bf56f47f..a8864817 100755 --- a/R/test.metric_fun.R +++ b/R/test.metric_fun.R @@ -51,7 +51,7 @@ reduce.space.one.type <- function(type, data, steps, shift.options, verbose) { names(output[[1]]) <- as.character(steps*100) names(output[[2]]) <- rev(as.character(steps*100)) switch(type, - "size" = {names(output) <- c("size.inner", "size.outer")}, + "size" = {names(output) <- c("size.increase", "size.hollowness")}, "density" = {names(output) <- c("density.higher", "density.lower")}, "position" = {names(output) <- c("position.top", "position.bottom")}, "evenness" = {names(output) <- c("evenness.flattened", "evenness.compacted")}, diff --git a/README.md b/README.md index da060ced..efc302f8 100755 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ Release: [![R-CMD-check](https://github.com/TGuillerme/dispRity/workflows/R-CMD-check/badge.svg)](https://github.com/TGuillerme/dispRity/actions) [![codecov](https://codecov.io/gh/TGuillerme/dispRity/branch/release/graph/badge.svg)](https://codecov.io/gh/TGuillerme/dispRity) [![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) -[![develVersion](https://img.shields.io/badge/devel%20version-1.7.0-green.svg?style=flat)](https://github.com/TGuillerme/dispRity) +[![develVersion](https://img.shields.io/badge/devel%20version-1.8.0-green.svg?style=flat)](https://github.com/TGuillerme/dispRity) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1186467.svg)](https://doi.org/10.5281/zenodo.1186467) Development (master): @@ -11,7 +11,7 @@ Development (master): [![R-CMD-check](https://github.com/TGuillerme/dispRity/workflows/R-CMD-check/badge.svg)](https://github.com/TGuillerme/dispRity/actions) [![codecov](https://codecov.io/gh/TGuillerme/dispRity/branch/master/graph/badge.svg)](https://codecov.io/gh/TGuillerme/dispRity) [![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) -[![develVersion](https://img.shields.io/badge/devel%20version-1.7.0-green.svg?style=flat)](https://github.com/TGuillerme/dispRity) +[![dispRity status badge](https://phylotastic.r-universe.dev/badges/dispRity)](https://phylotastic.r-universe.dev) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1186467.svg)](https://doi.org/10.5281/zenodo.1186467) CRAN: @@ -40,7 +40,7 @@ install.packages("dispRity") library(dispRity) ``` -The package is also available in the [phylotastic r-universe](https://phylotastic.r-universe.dev/ui#packages). [![dispRity status badge](https://phylotastic.r-universe.dev/badges/dispRity)](https://phylotastic.r-universe.dev) +The package is also available in the [phylotastic r-universe](https://phylotastic.r-universe.dev/ui#packages) [![dispRity status badge](https://phylotastic.r-universe.dev/badges/dispRity)](https://phylotastic.r-universe.dev) or through the [phylogenetics CRAN Task View](https://cran.r-project.org/web/views/Phylogenetics.html). You can also install the piping hot development version (not always recommended!) by installing the package directly through github: @@ -74,54 +74,16 @@ Not sure what disparity metric to use? Not sure what a disparity metric is in the first place? Check out this paper on selecting the best metric for your specific question in [Ecology and Evolution](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.6452) or the [`moms` shiny app](https://tguillerme.shinyapps.io/moms/). You can also find more information in the [`dispRity` manual](https://rawcdn.githack.com/TGuillerme/dispRity/c94452e6877fbb274eb0a4ff1363272a6297a9ee/inst/gitbook/_book/details-of-specific-functions.html#disparity-metrics). - - - -## Latest major patch notes -* 2022/08/08 v1.7 *MacMacGlimm* - - * *New* data function: `select.axes` for selecting and analysing the number of axes required to contain an arbitrary amount of variance. - * *New* utility function: `randtest.dist` for measuring the distance between the observed statistic and a specific quantile of the simulated statistic (thanks to [Frane Babarovic](https://twitter.com/FBabarovic) for the inspiration). - * `dispRity` objects can now contain covariance matrices as a `$covar` object. The `covar` part can be directly used for some specific metrics (usually `my_metric.covar`) and are handled by the `dispRity` function (and `plot`, `summary`, etc...) in a specific way. `$covar` contains a list of two elements `VCV` the variance covariance matrix and `loc` the coordinates of the centre of the `VCV` in space (can be left empty). `$covar` matrices are effectively treated as bootstraps. - * *New function* `covar.plot` for plotting the `covar` content of `dispRity` objects (this is separated from `plot.dispRity` because of the many different options). - * *New function*: `MCMCglmm.subsets` is a function that allows to convert a `MCMCglmm` object into a `dispRity` object. - * *New metric*: `projections.between` a between group metric for applying the `projections` metric between the major covariance axis of two matrices. - * *New metric*: `disalignment`: the (dis)alignment of a group compared to another one (i.e. the rejection from group B's centre on the group A's major axis). - a between group metric for applying the `projections` metric between the major covariance axis of two matrices. - * New `dispRity.fast` function for the fastest disparity calculations at the expanses of pretty much everything this package does. This is a really situational function. - * *New utility functions* for manipulating `MCMCglmm` objects: `MCMCglmm.traits` for extracting the number of traits, `MCMCglmm.levels` for extracting the level names, `MCMCglmm.sample` for sampling posterior IDs and `MCMCglmm.covars` for extracting variance-covariance matrices - * *New utility functions* for `dispRity` objects with `covar` matrices: `get.covar` to extract the VCV matrices (or a subsample of them); `axes.covar` to extract the major axes of the VCV matrices and `as.covar` to transform `dispRity` metric function to use a covar object. - * *New utility function* `match.tip.edge` for matching tip labels as factors/characters/integer to tree edges. - * *New wrapper function* `dispRity.covar.projections` for covariance projections analyses (with its associated S3 sub-class). - * One new demo datasets: `charadriiformes`, a `data.frame` and a `phylo` object of 359 _Charadriiformes_ species (gulls, plovers and sandpipers) from [Cooney et al 2017](https://www.nature.com/articles/d41586-021-02480-z) along with a `MCMCglmm` model with each clade as a random term. - * Additional plot arguments `...` in all the `dispRity` plotting functions can now be targeted to a specific plotting element. When multiple elements are plot by default (e.g. lines, legend, points, etc...) it is now possible to pass a specific `...` argument to the specific plotted element using the syntax `.` (e.g. `points.col = "blue"` will only apply the argument `col = "blue"` to the points). - * **Changed default arguments** for `projections` and `projections.tree` metrics: the default `"position"` output is now scaled, centred and absolute (see `?projections` for details). - * Formalised the grouping logic for `custom.subsets` and `select.axes`. This can create some minor user level changes namely: warning messages for empty subsets now correctly mentions "subsets" (rather than subsamples); groups with incorrect elements are now always flagged as errors (rather than just ignored). The changes at the developer level is that the logic is now made smoother and exported in `custom.subsets_fun.R`. - * Added a `function.index.csv` list (and updater) to help developers find internal functions locations easily. - * Restricted the type-I error inflation warning message in `test.dispRity` to only occur when using a test of class `"htest"`. - * Continuous Integration has been moved from Travis-CI to GitHub Actions. - * `custom.subsets` can now group elements using a `"factor"` vector. - * Utility functions manuals are now grouped by topic (e.g. utilities related to `MCMCglmm` objects, `dispRity` objects in general, `dispRity` objects with subsets, ect...). It should now be much easier to find these sometimes overlooked functions. - * Many updates and new entries in the `dispRity` manual, including a section on `covar` and `between.groups` specific analyses. - * Improving speed for the `test.metric` (using the new official `dispRity.fast` function). - * Most core functions in the package now have a garbage memory cleaning component. This improves the speed and the memory footprint when handling very large datasets. - * Disparity results stored in `data$disparity` now don't have dimension names anymore (significantly reducing the size of `disparity` objects). However, you can always retrieve the dimensions names using `get.disparity`. - * Updated the calculation options for `ellipse.volume`, you can now directly specify one of the following methods: `"pca"` to calculate the eigen values from the ordinated matrix; `"eigen"` to directly do an eigen decomposition of the matrix (new); or `"axes"` to directly measure the axes (new); or directly provide the eigen values. - * The interval function `check.subsets` now handles the checking of `"dispRity"` objects much faster making most functions relying on it slightly faster (this function is typically not called more than once per function). - * Updated `adonis.dispRity` to the newest `vegan::adonis2` code (thanks to Jari Oksanen for the notification). - * Removed dependency to `geiger` for `dtt.dispRity` to avoid package maintenance errors. This leads to no changes at the user level and `geiger::dtt` is still acknowledged in the manual. - * `tree.age` function's manual now makes it clear it does not estimate tree ages. - * When using `plot.dispRity(..., type = "preview")`, group's colour attribution and plotting is now made so that the groups larger groups are plotted in the background and the smaller in the foreground. - * `NA`s are now better handled in internal checking functions. - * Removed warning in `dispRity` when selecting a specific number of dimensions (old warning artefact). - * Fixed bug in `plot.dispRity` when using `type = "preview"` on bootstrapped data and for `type = "box"` when the subsets to plot are from different sizes (now plots all the data correctly). - * Fixed bug when using `chrono.subsets` with `"continuous"` method a `FADLAD` data containing only node values (now correctly taken into account; thanks to [Peng-Wei Li](https://www.researchgate.net/profile/Peng-Wei-Li) for noticing it) and when using `chrono.subsets` with `"gradual.*"` models on empty subsets. - * `standardGeneric` functions are now correctly interpreted as functions throughout the package. - * Fixed bug when plotting level 1 disparity metric results without bootstrapped (`observed = TRUE` is now used as the default). - * Fixed bug when plotting `test.metric` plots with `save.steps` options with more than two types of shifts. - * Fixed bug with `null.test` which is now correctly managing the number of dimensions inherited from `dispRity` objects (thanks to [Alex Slavenko](https://alexslavenko.weebly.com/) for spotting this one and the two above). - * Fixed bug when using level 2 dimension metrics on unidimensional data (the metric is now detected as a level 2 correctly; thanks to [Catherine Klein](https://www.researchgate.net/profile/Catherine-Klein) and [Rachel Warnock](https://www.gzn.nat.fau.de/palaeontologie/team/professors/rachel-warnock/) for noticing that one). - * Update internal use of `is(data, c("array", "matrix"))` to `is.array(data)` for R 4.1.2. + +## Latest major patch highlights +### dispRity v1.8 (2023-12-11) *dispRity.multi* +[Read the full patch note here](https://github.com/TGuillerme/dispRity/blob/master/NEWS.md). + + * Added the _dispRity.multi_ internal architecture now allowing users to work with different matrices **and** different trees as inputs for `custom.subsets`, `chrono.subsets`, `boot.matrix` and `dispRity`. This change is not affecting the user level appart from now allowing to bypass some error messages (thanks to Mario Corio for that obvious suggestion). + * *New* statistical test: `pgls.dispRity` to run PGLS test on a `dispRity` object with a level-2 metric and a tree (using excellent [`phylolm`](https://CRAN.R-project.org/package=phylolm) algorithm). The new test comes with its own S3 print, summary and plot functions if the input `dispRity` data contains multiple trees or multiple matrices (running and handling the output of multiple `phylolm`). + * *New vignette* compiling resources for developers to help people (and future me) to edit the package. + * And many more new additions, improvements and couple of bug fixes! + * **NOTE** there are now changes in the following function names: `ellipse.volume` is now `ellipsoid.volume`; `rescale.dispRity` is now `scale.dispRity` and `randtest.dist` is now `distance.randtest` (the old aliases still work). Previous patch notes and notes for the *next version* can be seen [here](https://github.com/TGuillerme/dispRity/blob/master/NEWS.md). @@ -131,6 +93,7 @@ Authors * [Thomas Guillerme](http://tguillerme.github.io) * [Natalie Cooper](http://nhcooper123.github.io) * [Mark Puttick](https://puttickbiology.wordpress.com/) +* [Jack Hatfield](https://www.york.ac.uk/anthropocene-biodiversity/people/jack-hatfield/) #### Contributors (bug fixes, pull requests and suggestions) @@ -191,7 +154,7 @@ Schaeffer J, Benton MJ, Rayfield EJ, Stubbs TL. Morphological disparity in thero * Heggli OA, Cabral J, Konvalinka I, Vuust P, Kringelbach ML. A Kuramoto model of self-other integration across interpersonal synchronization strategies. *PLoS computational biology*. **2019** [DOI:10.1371/journal.pcbi.1007422](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007422) -##### Comparisons between groups using `custom.subsets` and `test.dispRity`: +#### Comparisons between groups using `custom.subsets` and `test.dispRity`: * Esquerré D, Donnellan S, Brennan IG, Lemmon AR, Lemmon EM, Zaher H, Grazziotin FG, Keogh JS. Phylogenomics, biogeography and morphometrics reveal rapid phenotypic evolution in pythons after crossing Wallace’s line. *Systematic Biology*. **2019** [DOI: 10.1093/sysbio/syaa024](https://doi.org/10.1093/sysbio/syaa024) diff --git a/TODO-MCMCglmm.md b/TODO-MCMCglmm.md deleted file mode 100755 index 93e3e8e5..00000000 --- a/TODO-MCMCglmm.md +++ /dev/null @@ -1,3 +0,0 @@ -# TODO list for MCMCglmm branch? - -Thanks a lot to Andrew Beckerman, Natalie Cooper and Gavin Thomas for supporting the development of this version. diff --git a/castor_change.md b/castor_change.md deleted file mode 100755 index 9bc2f8ea..00000000 --- a/castor_change.md +++ /dev/null @@ -1,3 +0,0 @@ -ape::dist.nodes <-> castor::get_all_pairwise_distances -ape::node.depth.edgelength >-> castor::get_all_distances_to_root -ape::drop.tips() <-> castor::get_subtree_with_tips( omit_tips =)[[1]] \ No newline at end of file diff --git a/codecov.yml b/codecov.yml index 8c512ae5..358a0861 100755 --- a/codecov.yml +++ b/codecov.yml @@ -1,2 +1,2 @@ codecov: - token: 5f041826-63f1-47fa-b4a8-9a32633f47fa + token: 14042dcc-32e4-418a-b5ca-fa368414b775 diff --git a/covar_model_list.rda b/covar_model_list.rda deleted file mode 100755 index 087653e8..00000000 Binary files a/covar_model_list.rda and /dev/null differ diff --git a/desiderata.md b/desiderata.md deleted file mode 100755 index 52fea147..00000000 --- a/desiderata.md +++ /dev/null @@ -1,8 +0,0 @@ -# Package wish list -* Add the possibility to use a `multiPhylo` object in `time.series`. -* Improve `test.dispRity` architecture. -* Change the structure of the `dispRity` calculation loop to be run in `C`. `make.metric` would then be a compiler function. -* Add "clever" rarefaction algorithms: 1) http://www.bioone.org/doi/full/10.1017/pab.2014.5 and 2) with rarefaction as a function of sampling or occurrences. -* Add correlation with sample size for disparity metrics (calculate Spearman’s rho for the correlation between the metric and the sample size). - - * Add distributions for FADLADs + FADLADs of nodes \ No newline at end of file diff --git a/disparity_internal_logic.md b/disparity_internal_logic.md deleted file mode 100755 index d4b68cbc..00000000 --- a/disparity_internal_logic.md +++ /dev/null @@ -1,48 +0,0 @@ -# `dispRity` function internal logic explained - -This is a note for understanding the logic at the base of the package within the `dispRity` function (i.e. to calculate disparity metrics). - -The disparity calculations are handled by two main functions `mapply.wrapper` or `lapply.wrapper` depending if the metric is applied between groups or not (respectively). Here we are going to focus on `lapply.wrapper` but `mapply.wrapper` uses the same logic but intakes two lists instead of one. - -## The inputs - -The `lapply.wrapper` function intakes the following mandatory arguments: - - * `lapply_loop` (`"list"`) that contains the row names to analyse for each subsest (each element of the list is divided into different lists containing the observed rows as a n x 1 matrix and the bootstrapped rows as a n x bootstraps matrix as well as the rarefaction levels (see [`disparity_object.md`](https://github.com/TGuillerme/dispRity/blob/master/disparity_object.md) for details). If disparity was already previously calculated, `lapply_loop` contains disparity values (`"matrix"` or `"array"`) rather than row names. - * `metrics_list` (`"list"` of length 3) the list of the three levels of metrics to apply (some levels can be NULL). - * `data` (`"dispRity"`) the data that must contain at least the matrix or the disparity data and the number of dimensions (and also a tree if `metric_is_tree`). - * `matrix_decomposition` (`"logical"` of length 1) whether to decompose the matrix (see later) - * `verbose` (`"logical"` of length 1) whether to be verbose - * `metric_is_tree` (`"logical"` of length 3) whether each metric needs a tree - * `...` any arguments to be handled by the functions in `metrics_list` - - * `metric_is_between.groups` which is a deprecated placeholder for between groups analyses -TODO: remove that! - -## The pipeline - -These arguments are passed to `lapply.wrapper` (or `mapply.wrapper`) which first toggles the functions to be verbose or not and then handles in turn the following (in a nested way): - -### 1 - Passing one subset (e.g. `lapply_loop[[1]]`) to `disparity.bootstraps` - -The `disparity.bootstraps` function handles subsets one by one, (e.g. `lapply_loop[[1]][[1]]`, etc...) and calculates disparity on them using the different metrics from `metrics_list`. - -This function is composed of two elements: firstly decomposing the matrix if necessary (see point 2 below) and secondly applying each metric depending on there level. The application of each metric is done simply by using an apply loop like `apply(data, margin, metric)` where `data` is already calculated disparity data (either from previous calculations or from decomposing the matrix), `margin` is detected automatically and `metric` comes from `metrics_list` according to the correct level. - -### 2 - Wrapping the matrix decomposition for one subset with `decompose.matrix.wrapper` - -This part transforms the raw data from `"dispRity"` (i.e. the row IDs from the subset, the requested number of dimensions and the original matrix) into the first requested disparity metric (with the appropriate requested level, e.g. if the metric contains `var`, the matrix decomposition transforms the `"dispRity"` data into a var/covar `"matrix"`). The metric to use to decompose the matrix (the one with the highest level) is detected with the `get.first.metric` function. Then both the `"dispRity"` data and the first metric are passed to `decompose.matrix.wrapper`. - -This function basically feeds the number of subsets (i.e. either elements (n x 1) or the bootstraps/rarefaction (n x bootstraps) - see [`disparity_object.md`](https://github.com/TGuillerme/dispRity/blob/master/disparity_object.md) for details) to the `decompose.matrix` function along with the first metric. -The wrapper does also a bit of data/format wrangling I'm not going to detail here (and can hopefully be understood reading the code). - -### 3 - Decomposing the matrix with `decompose.matrix` - -This function intakes: - - * `one_subsets_bootstraps` that is one row from one element of the subset list (e.g. `lapply_loop[[1]][[1]][, 1]`). Effectively, these are the row IDs to be considered when calculating the disparity metric. - * `fun` the first disparity metric - * `data` the `"dispRity"` object - * `nrow` an option for between groups calculation. Is `NULL` when the decomposition is for a normal metric (but is an indicator of the pairs of row to consider if the metric is `between.groups = TRUE`). - -And then applies the `decompose` function to each matrix in `data$matrix`. Which simply applies the metric to the matrix in the following format (i.e. `fun(matrix[rows, columns])`). \ No newline at end of file diff --git a/disparity_object.md b/disparity_object.md deleted file mode 100755 index deb148e9..00000000 --- a/disparity_object.md +++ /dev/null @@ -1,109 +0,0 @@ -### Structure of `dispRity` objects (lite version) - -``` -object - | - \---$matrix* = class:"list" (a list containing the orginal matrix/matrices) - | | - | \---[[1]]* = class:"matrix" (the matrix (trait-space)) - | | - | \---[[...]] = class:"matrix" (any additional matrices) - | - \---$tree* = class:"multiPhylo" (a list containing the attached tree(s) or NULL) - | | - | \---[[1]] = class:"phylo" (the first tree) - | | - | \---[[...]] = class:"phylo" (any additional trees) - | - \---$call* = class:"list" (details of the methods used) - | | - | \---$subsets = class:"character" - | | - | \---$bootstrap = class:"character" - | | - | \---$dimensions = class:"numeric" - | | - | \---$metric = class:"list" (details about the metric(s) used) - | | - | \---$name = class:"character" - | | - | \---$fun = class:"list" (elements of class "function") - | | - | \---$arg = class:"list" - | - | - \---$subsets* = class:"list" (subsets as a list) - | | - | \---[[1]]* = class:"list" (first item in subsets list) - | | | - | | \---$elements* = class:"matrix" (one column matrix containing the elements within the first subset) - | | | - | | \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data) - | | | - | | \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level) - | | | - | | \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.) - | | - | \---[[2]] = class:"list" (second item in subsets list) - | | | - | | \---$elements* = class:"matrix" (one column matrix containing the elements within the second subset) - | | | - | | \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data) - | | | - | | \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level) - | | | - | | \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.) - | | - | \---[[...]] = class:"list" (the following subsets) - | | - | \---$elements* = class:"matrix" (a one column matrix containing the elements within this subset) - | | - | \---[[...]] = class:"matrix" (the rarefactions) - | - \---$covar = class:"list" (a list of subsets containing covar matrices; is the same length as $subsets) - | | - | \---[[1]] = class:"list" (first item in subsets list) - | | | - | | \---$matrices = class:"list" (the list of covar matrices) - | | | | - | | | \[[1]] = class:"matrix" (the first covar matrix) - | | | | - | | | \[[...]] = class:"matrix" (the subsequent covar matrices) - | | | - | | \---$centre = class:"list" (optional, the list of centres for the matrices) - | | | - | | \[[1]] = class:"numeric" (the coordinates for the centre of the first matrix) - | | | - | | \[[...]] = class:"numeric" (the coordinates for the centre of the subsequent covar matrices) - | | - | \---[[...]] = class:"list" (the following subsets) - | - \---$disparity - | - \---[[2]] = class:"list" (the first subsets) - | | - | \---$observed* = class:"numeric" (vector containing the observed disparity within the subsets) - | | - | \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data) - | | - | \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level) - | | - | \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.) - | - \---[[2]] = class:"list" (the first subsets) - | | - | \---$observed* = class:"numeric" (vector containing the observed disparity within the subsets) - | | - | \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data) - | | - | \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level) - | | - | \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.) - | - \---[[...]] = class:"list" (the following subsets) - | - \---$observed* = class:"numeric" (the vector containing the observed disparity within this subsets) - | - \---[[...]] = class:"matrix" (the rarefactions) -``` -The elements marked with an asterisk (*) are mandatory. diff --git a/export.cran.sh b/export.cran.sh index 36608120..39027ecb 100755 --- a/export.cran.sh +++ b/export.cran.sh @@ -25,7 +25,11 @@ # shift # done +## Update the function index +sh update.function.index.sh + ## Create the temporary CRAN folder +rm -R cran_tmp/ mkdir cran_tmp mkdir cran_tmp/dispRity TMPPATH="cran_tmp/dispRity" @@ -69,34 +73,34 @@ rm src/*.rds version_number=$(grep "Version:" DESCRIPTION | sed 's/Version: //g') ## CHANGE THE WARNING zzz.R -sed 's/# //g' R/zzz.R | sed 's/GitHub release./This is the CRAN release version ('"$version_number"') of the package.\\nFor news, vignettes and future releases,\\nvisit https:\/\/github.com\/TGuillerme\/dispRity/g' > ${TMPPATH}/R/zzz.R - -## Add ssptest.support and remove dependencies -cp ~/Packaging/CRAN/Support/ssptest.support.R ${TMPPATH}/R/ - -## Remove spptest from model.test.fun.R -sed 's/spptest::create_curve_set/create_curve_set/g' ${TMPPATH}/R/model.test_fun.R | sed -e 's/spptest::rank_envelope/rank_envelope/g' > export.cran.tmp -mv export.cran.tmp ${TMPPATH}/R/model.test_fun.R - -## Remove remotes from DESCRIPTION -line_remove=$(grep -n "Remotes" ${TMPPATH}/DESCRIPTION | sed -e 's/:Remotes://g') -sed ''"${line_remove}"'d' ${TMPPATH}/DESCRIPTION > export.cran.tmp -line_remove=$(grep -n "github::myllym/spptest@no_fastdepth" export.cran.tmp | sed -e 's/: github::myllym\/spptest@no_fastdepth//g') -sed ''"${line_remove}"'d' export.cran.tmp > export.cran.tmp2 -## Remove imports -line_remove=$(grep -n "spptest" export.cran.tmp2 | sed -e 's/:[[:space:]]spptest//g' | sed -e 's/,//g') -sed ''"${line_remove}"'d' export.cran.tmp2 > export.cran.tmp -## Remove coma to last import -let "line_remove -= 1" -sed ''"${line_remove}"'s/,//' export.cran.tmp > ${TMPPATH}/DESCRIPTION - -## Remove spptest from NAMESPACE -line_remove=$(grep -n "importFrom(\"spptest\"," ${TMPPATH}/NAMESPACE | sed -e 's/:importFrom("spptest", "create_curve_set", "rank_envelope")//g') -sed ''"${line_remove}"'d' ${TMPPATH}/NAMESPACE > export.cran.tmp -## Add "is" to methods in NAMESPACE -sed 's/importFrom("methods", "hasArg"/importFrom("methods", "hasArg", "is"/g' export.cran.tmp > ${TMPPATH}/NAMESPACE -rm export.cran.tmp -rm export.cran.tmp2 +# sed 's/# //g' R/zzz.R | sed 's/GitHub release./This is the CRAN released version of the package ('"$version_number"').\\nFor news, vignettes and future releases, visit\\nhttps:\/\/github.com\/TGuillerme\/dispRity\\nTo cite the package use\\ncitation("dispRity")/g' > ${TMPPATH}/R/zzz.R + +# ## Add ssptest.support and remove dependencies +# cp ~/Packaging/CRAN/Support/ssptest.support.R ${TMPPATH}/R/ + +# ## Remove spptest from model.test.fun.R +# sed 's/spptest::create_curve_set/create_curve_set/g' ${TMPPATH}/R/model.test_fun.R | sed -e 's/spptest::rank_envelope/rank_envelope/g' > export.cran.tmp +# mv export.cran.tmp ${TMPPATH}/R/model.test_fun.R + +# ## Remove remotes from DESCRIPTION +# line_remove=$(grep -n "Remotes" ${TMPPATH}/DESCRIPTION | sed -e 's/:Remotes://g') +# sed ''"${line_remove}"'d' ${TMPPATH}/DESCRIPTION > export.cran.tmp +# line_remove=$(grep -n "github::myllym/spptest@no_fastdepth" export.cran.tmp | sed -e 's/: github::myllym\/spptest@no_fastdepth//g') +# sed ''"${line_remove}"'d' export.cran.tmp > export.cran.tmp2 +# ## Remove imports +# line_remove=$(grep -n "spptest" export.cran.tmp2 | sed -e 's/:[[:space:]]spptest//g' | sed -e 's/,//g') +# sed ''"${line_remove}"'d' export.cran.tmp2 > export.cran.tmp +# ## Remove coma to last import +# let "line_remove -= 1" +# sed ''"${line_remove}"'s/,//' export.cran.tmp > ${TMPPATH}/DESCRIPTION + +# ## Remove spptest from NAMESPACE +# line_remove=$(grep -n "importFrom(\"spptest\"," ${TMPPATH}/NAMESPACE | sed -e 's/:importFrom("spptest", "create_curve_set", "rank_envelope")//g') +# sed ''"${line_remove}"'d' ${TMPPATH}/NAMESPACE > export.cran.tmp +# ## Add "is" to methods in NAMESPACE +# sed 's/importFrom("methods", "hasArg"/importFrom("methods", "hasArg", "is"/g' export.cran.tmp > ${TMPPATH}/NAMESPACE +# rm export.cran.tmp +# rm export.cran.tmp2 ## Compile the package cd cran_tmp/ diff --git a/function.index.csv b/function.index.csv old mode 100755 new mode 100644 index 7e4736dc..5aeab027 --- a/function.index.csv +++ b/function.index.csv @@ -1,49 +1,53 @@ file,line,function Claddis.ordination.R,55,Claddis.ordination Claddis.ordination_fun.R,2,convert.to.Claddis -MCMCglmm.subsets.R,42,MCMCglmm.subsets +MCMCglmm.subsets.R,43,MCMCglmm.subsets MCMCglmm.subsets_fun.R,2,get.one.group -MCMCglmm.subsets_fun.R,29,split.term.name -MCMCglmm.utilities.R,55,MCMCglmm.levels -MCMCglmm.utilities.R,56,convert.term.name -MCMCglmm.utilities.R,122,MCMCglmm.traits -MCMCglmm.utilities.R,143,MCMCglmm.sample -MCMCglmm.utilities.R,157,MCMCglmm.covars +MCMCglmm.subsets_fun.R,29,term.name.split +MCMCglmm.subsets_fun.R,48,location.update +MCMCglmm.utilities.R,63,MCMCglmm.levels +MCMCglmm.utilities.R,64,convert.term.name +MCMCglmm.utilities.R,134,MCMCglmm.traits +MCMCglmm.utilities.R,158,MCMCglmm.sample +MCMCglmm.utilities.R,177,MCMCglmm.covars +MCMCglmm.utilities.R,224,MCMCglmm.variance MCMCglmm.utilities_fun.R,2,make.covar MCMCglmm.utilities_fun.R,7,make.sol MCMCglmm.utilities_fun.R,16,make.matrix MCMCglmm.utilities_fun.R,22,get.sample.covar -adonis.dispRity.R,84,adonis.dispRity -adonis.dispRity.R,143,split.variables +adonis.dispRity.R,83,adonis.dispRity +adonis.dispRity.R,142,split.variables adonis.dispRity_fun.R,2,make.factors adonis.dispRity_fun.R,7,get.group.factors adonis.dispRity_fun.R,15,output.factor adonis.dispRity_fun.R,30,make.time.factor adonis.dispRity_fun.R,42,make.time.series adonis.dispRity_fun.R,97,lapply.subsets -as.covar.R,71,as.covar -as.covar.R,80,fun.covar2 -as.covar.R,88,fun.covar2 -as.covar.R,96,fun.covar2 -as.covar.R,110,fun.covar -as.covar.R,117,fun.covar -as.covar.R,124,fun.covar +as.covar.R,72,as.covar +as.covar.R,90,fun.covar2 +as.covar.R,102,fun.covar2 +as.covar.R,113,fun.covar2 +as.covar.R,124,fun.covar2 +as.covar.R,156,fun.covar +as.covar.R,163,fun.covar +as.covar.R,170,fun.covar as.covar_fun.R,2,eval.covar bhatt.coeff.R,28,bhatt.coeff -boot.matrix.R,89,boot.matrix -boot.matrix.R,217,add.prob +boot.matrix.R,90,boot.matrix +boot.matrix.R,247,add.prob boot.matrix_fun.R,2,elements.sampler boot.matrix_fun.R,4,sampler -boot.matrix_fun.R,21,boot.full -boot.matrix_fun.R,26,boot.full.proba -boot.matrix_fun.R,38,boot.single -boot.matrix_fun.R,50,boot.single.proba -boot.matrix_fun.R,68,replicate.bootstraps -boot.matrix_fun.R,85,bootstrap.wrapper -boot.matrix_fun.R,94,select.rarefaction -boot.matrix_fun.R,99,combine.bootstraps -boot.matrix_fun.R,104,split.subsets -boot.matrix_fun.R,115,merge.to.list +boot.matrix_fun.R,20,boot.null +boot.matrix_fun.R,25,boot.full +boot.matrix_fun.R,30,boot.full.proba +boot.matrix_fun.R,42,boot.single +boot.matrix_fun.R,54,boot.single.proba +boot.matrix_fun.R,72,replicate.bootstraps +boot.matrix_fun.R,89,bootstrap.wrapper +boot.matrix_fun.R,98,select.rarefaction +boot.matrix_fun.R,103,combine.bootstraps +boot.matrix_fun.R,108,do.split.subsets +boot.matrix_fun.R,119,merge.to.list char.diff.R,118,char.diff char.diff.R,157,not.exist char.diff.R,197,special.behaviours$missing @@ -57,10 +61,10 @@ char.diff_fun.R,56,binary char.diff_fun.R,61,convert.bitwise check.morpho.R,52,check.morpho chrono.subsets.R,98,chrono.subsets -chrono.subsets.R,150,stretch.tree -chrono.subsets.R,310,reverse.time -chrono.subsets.R,353,make.fadlad -chrono.subsets.R,433,combine.args +chrono.subsets.R,205,stretch.tree +chrono.subsets.R,356,reverse.time +chrono.subsets.R,399,make.fadlad +chrono.subsets.R,475,combine.args chrono.subsets_fun.R,2,get.percent.age chrono.subsets_fun.R,18,adjust.age chrono.subsets_fun.R,24,adjust.FADLAD @@ -68,7 +72,7 @@ chrono.subsets_fun.R,49,chrono.subsets.discrete chrono.subsets_fun.R,55,get.interval chrono.subsets_fun.R,93,chrono.subsets.continuous chrono.subsets_fun.R,113,make.origin.subsets -chrono.subsets_fun.R,120,cbind.fill +chrono.subsets_fun.R,120,do.cbind.fill chrono.subsets_fun.R,146,recursive.combine.list chrono.subsets_fun.R,163,fast.slice.table chrono.subsets_fun.R,190,get.sliced.edge @@ -89,7 +93,7 @@ covar.plot_fun.R,28,level.ellipses covar.plot_fun.R,38,replace.intercept covar.plot_fun.R,44,recentre.levels covar.plot_fun.R,63,VCV.cent.tend -covar.plot_fun.R,71,scale.VCV +covar.plot_fun.R,71,VCV.scale covar.utilities.R,43,get.covar covar.utilities.R,100,axis.covar covar.utilities_fun.R,4,redimension @@ -104,84 +108,101 @@ custom.subsets_fun.R,40,set.group.list custom.subsets_fun.R,69,check.elements custom.subsets_fun.R,118,check.group.list dispRity.R,126,dispRity -dispRity.covar.projections.R,66,dispRity.covar.projections -dispRity.covar.projections.R,165,get.call -dispRity.covar.projections.R,227,get.call +dispRity.R,220,dispRity.int.call +dispRity.covar.projections.R,73,dispRity.covar.projections +dispRity.covar.projections.R,174,get.call +dispRity.covar.projections.R,236,get.call dispRity.covar.projections_fun.R,2,projections.fast -dispRity.covar.projections_fun.R,123,fun.proj -dispRity.covar.projections_fun.R,128,apply.proj +dispRity.covar.projections_fun.R,69,fun.proj +dispRity.covar.projections_fun.R,74,apply.proj dispRity.fast.R,33,dispRity.fast -dispRity.metric.R,345,dimension.level3.fun -dispRity.metric.R,350,dimension.level2.fun -dispRity.metric.R,369,dimension.level1.fun -dispRity.metric.R,382,between.groups.fun -dispRity.metric.R,390,k.root -dispRity.metric.R,395,variances -dispRity.metric.R,404,ranges -dispRity.metric.R,413,quantiles -dispRity.metric.R,422,fun.dist.euclidean -dispRity.metric.R,426,fun.dist.manhattan -dispRity.metric.R,430,select.method -dispRity.metric.R,444,centroids -dispRity.metric.R,463,displacements -dispRity.metric.R,469,neighbours -dispRity.metric.R,480,mode.val -dispRity.metric.R,485,ellipse.volume -dispRity.metric.R,522,convhull.surface -dispRity.metric.R,528,convhull.volume -dispRity.metric.R,554,diagonal -dispRity.metric.R,560,get.ancestor.dist -dispRity.metric.R,566,get.root.dist -dispRity.metric.R,570,ancestral.dist -dispRity.metric.R,589,span.tree.length -dispRity.metric.R,597,pairwise.dist -dispRity.metric.R,605,radius -dispRity.metric.R,614,n.ball.volume -dispRity.metric.R,630,func.eve -dispRity.metric.R,646,func.div -dispRity.metric.R,659,get.slope.significant -dispRity.metric.R,663,get.slope.nonsignificant -dispRity.metric.R,667,angles -dispRity.metric.R,705,deviations -dispRity.metric.R,748,distance -dispRity.metric.R,758,edge.length.tree -dispRity.metric.R,771,centre.matrix -dispRity.metric.R,776,get.proj.length -dispRity.metric.R,780,quantiles.per.groups -dispRity.metric.R,784,group.dist -dispRity.metric.R,814,point.dist -dispRity.metric.R,826,vector.angle -dispRity.metric.R,830,get.rotation.matrix -dispRity.metric.R,845,projections -dispRity.metric.R,942,projections.between -dispRity.metric.R,967,get.root -dispRity.metric.R,971,get.ancestor -dispRity.metric.R,983,get.tips -dispRity.metric.R,987,get.nodes -dispRity.metric.R,991,get.livings -dispRity.metric.R,996,get.fossils -dispRity.metric.R,1001,sapply.projections -dispRity.metric.R,1007,projections.tree -dispRity.metric.R,1035,from_to[[i]] -dispRity.utilities.R,31,make.dispRity -dispRity.utilities.R,74,fill.dispRity -dispRity.utilities.R,149,get.matrix -dispRity.utilities.R,169,get.disparity -dispRity.utilities.R,214,lapply.observed -dispRity.utilities.R,234,matrix.dispRity -dispRity.utilities.R,238,extract.dispRity -dispRity.utilities.R,305,get.subsets -dispRity.utilities.R,328,combine.subsets -dispRity.utilities.R,331,select.elements -dispRity.utilities.R,438,size.subsets -dispRity.utilities.R,442,n.subsets -dispRity.utilities.R,493,add.tree -dispRity.utilities.R,502,get.tree -dispRity.utilities.R,511,remove.tree -dispRity.utilities.R,564,rescale.dispRity -dispRity.utilities.R,601,lapply.scale -dispRity.utilities.R,651,sort.dispRity -dispRity.utilities.R,737,extinction.subsets +dispRity.metric.R,371,dimension.level3.fun +dispRity.metric.R,376,dimension.level2.fun +dispRity.metric.R,395,dimension.level1.fun +dispRity.metric.R,409,between.groups.fun +dispRity.metric.R,418,k.root +dispRity.metric.R,424,variances +dispRity.metric.R,433,ranges +dispRity.metric.R,442,quantiles +dispRity.metric.R,451,fun.dist.euclidean +dispRity.metric.R,456,fun.dist.manhattan +dispRity.metric.R,462,select.method +dispRity.metric.R,477,centroids +dispRity.metric.R,496,displacements +dispRity.metric.R,502,neighbours +dispRity.metric.R,512,mode.val +dispRity.metric.R,518,ellipsoid.volume +dispRity.metric.R,554,ellipse.volume +dispRity.metric.R,559,convhull.surface +dispRity.metric.R,565,convhull.volume +dispRity.metric.R,571,diagonal +dispRity.metric.R,577,get.ancestor.dist +dispRity.metric.R,583,get.root.dist +dispRity.metric.R,587,ancestral.dist +dispRity.metric.R,598,span.tree.length +dispRity.metric.R,606,pairwise.dist +dispRity.metric.R,614,radius +dispRity.metric.R,623,n.ball.volume +dispRity.metric.R,639,func.eve +dispRity.metric.R,655,func.div +dispRity.metric.R,667,get.slope.significant +dispRity.metric.R,671,get.slope.nonsignificant +dispRity.metric.R,676,angles +dispRity.metric.R,714,deviations +dispRity.metric.R,757,distance +dispRity.metric.R,767,edge.length.tree +dispRity.metric.R,780,centre.matrix +dispRity.metric.R,785,get.proj.length +dispRity.metric.R,790,quantiles.per.groups +dispRity.metric.R,795,group.dist +dispRity.metric.R,825,point.dist +dispRity.metric.R,837,vector.angle +dispRity.metric.R,842,get.rotation.matrix +dispRity.metric.R,857,orthogonise +dispRity.metric.R,876,linear.algebra.projection +dispRity.metric.R,932,correct.position +dispRity.metric.R,945,get.distance +dispRity.metric.R,951,projections +dispRity.metric.R,993,projections.between +dispRity.metric.R,1018,disalignment +dispRity.metric.R,1038,get.root +dispRity.metric.R,1042,get.ancestor +dispRity.metric.R,1054,get.tips +dispRity.metric.R,1058,get.nodes +dispRity.metric.R,1062,get.livings +dispRity.metric.R,1067,get.fossils +dispRity.metric.R,1072,sapply.projections +dispRity.metric.R,1078,projections.tree +dispRity.metric.R,1106,from_to[[i]] +dispRity.metric.R,1133,roundness +dispRity.multi.R,29,dispRity.multi.split +dispRity.multi.R,109,lapply.clean.data +dispRity.multi.R,118,dispRity.multi.apply +dispRity.multi.R,161,dispRity.multi.merge +dispRity.multi.R,180,dispRity.multi.merge.data +dispRity.multi.R,206,dispRity.multi.merge.disparity +dispRity.multi.R,207,merge.subset.pair +dispRity.utilities.R,32,make.dispRity +dispRity.utilities.R,75,fill.dispRity +dispRity.utilities.R,156,get.matrix +dispRity.utilities.R,176,get.disparity +dispRity.utilities.R,221,lapply.observed +dispRity.utilities.R,245,matrix.dispRity +dispRity.utilities.R,249,extract.dispRity +dispRity.utilities.R,320,get.subsets +dispRity.utilities.R,342,combine.subsets +dispRity.utilities.R,345,select.elements +dispRity.utilities.R,452,size.subsets +dispRity.utilities.R,456,n.subsets +dispRity.utilities.R,460,name.subsets +dispRity.utilities.R,524,add.tree +dispRity.utilities.R,539,get.tree +dispRity.utilities.R,614,remove.tree +dispRity.utilities.R,669,scale.dispRity +dispRity.utilities.R,714,lapply.scale +dispRity.utilities.R,720,rescale.dispRity +dispRity.utilities.R,766,sort.dispRity +dispRity.utilities.R,852,extinction.subsets dispRity.utilities_fun.R,2,extract.disparity.values dispRity.utilities_fun.R,21,clean.list dispRity.utilities_fun.R,27,recursive.sort @@ -190,6 +211,15 @@ dispRity.utilities_fun.R,45,check.subsets dispRity.utilities_fun.R,104,detect.bin.age dispRity.utilities_fun.R,109,detect.bin.ages.lapply dispRity.utilities_fun.R,121,add.dimnames +dispRity.utilities_fun.R,138,detect.edges +dispRity.utilities_fun.R,183,get.new.tree +dispRity.utilities_fun.R,247,toggle.multiphylo.list +dispRity.utilities_fun.R,266,get.one.tree.subset +dispRity.utilities_fun.R,280,slide.node.root +dispRity.utilities_fun.R,330,get.interval.subtrees +dispRity.utilities_fun.R,332,slice.one.tree +dispRity.utilities_fun.R,348,get.slice.subsets +dispRity.utilities_fun.R,355,sample.x dispRity.wrapper.R,40,dispRity.through.time dispRity.wrapper.R,96,dispRity.per.group dispRity_fun.R,1,check.covar @@ -205,27 +235,34 @@ dispRity_fun.R,278,decompose.matrix.wrapper dispRity_fun.R,322,disparity.bootstraps dispRity_fun.R,381,lapply.wrapper dispRity_fun.R,388,mapply.wrapper -dispRity_fun.R,393,split.lapply_loop +dispRity_fun.R,393,lapply_loop.split dispRity_fun.R,395,split.matrix -dispRity_fun.R,406,split.data +dispRity_fun.R,406,bound.data.split dispRity_fun.R,425,recursive.merge dispRity_fun.R,434,combine.pairs -dtt.dispRity.R,73,dtt.dispRity -dtt.dispRity.R,111,get.p.value +distance.randtest.R,35,distance.randtest +dtt.dispRity.R,67,dtt.dispRity +dtt.dispRity.R,105,get.p.value +dtt.dispRity.R,114,get.p.value dtt.dispRity.R,120,get.p.value -dtt.dispRity.R,126,get.p.value -dtt.dispRity_fun.R,3,.dtt.dispRity -dtt.dispRity_fun.R,107,.area.between.curves +dtt.dispRity_fun.R,3,geiger.dtt.dispRity +dtt.dispRity_fun.R,107,geiger.area.between.curves +dtt.dispRity_fun.R,137,geiger.ratematrix +dtt.dispRity_fun.R,148,f +dtt.dispRity_fun.R,157,geiger.sim.char +dtt.dispRity_fun.R,216,geiger.make.modelmatrix +dtt.dispRity_fun.R,239,geiger.get.simulation.matrix +dtt.dispRity_fun.R,176,simulate geomorph.ordination.R,57,geomorph.ordination geomorph.ordination_fun.R,2,make.groups.factors get.bin.ages.R,30,get.bin.ages get.bin.ages.R,62,num.decimals make.metric.R,54,make.metric make.metric_fun.R,2,check.metric -match.tip.edge.R,31,match.tip.edge -model.test.R,102,model.test -model.test.sim.R,107,model.test.sim -model.test.sim.R,142,check.arg.inherit +match.tip.edge.R,43,match.tip.edge +model.test.R,97,model.test +model.test.sim.R,109,model.test.sim +model.test.sim.R,144,check.arg.inherit model.test.wrapper.R,77,model.test.wrapper model.test_fun.R,4,select.model.list model.test_fun.R,72,get.parameters @@ -235,11 +272,11 @@ model.test_fun.R,171,eb.parameters model.test_fun.R,183,trend.parameters model.test_fun.R,199,est.mean model.test_fun.R,255,ou.mean.fun -model.test_fun.R,285,est.VCV -model.test_fun.R,335,model.test.lik -model.test_fun.R,367,opt.mode -model.test_fun.R,487,pooled.variance -model.test_fun.R,510,rank_env_dtt +model.test_fun.R,286,est.VCV +model.test_fun.R,333,model.test.lik +model.test_fun.R,363,opt.mode +model.test_fun.R,483,pooled.variance +model.test_fun.R,506,rank_env_dtt morpho.utilities.R,52,get.contrast.matrix morpho.utilities.R,122,apply.NA morpho.utilities_fun.R,2,inap.character @@ -264,7 +301,7 @@ multi.ace_fun.R,12,check.model.class multi.ace_fun.R,21,convert.char.table multi.ace_fun.R,22,convert.one.taxon multi.ace_fun.R,50,make.args -multi.ace_fun.R,68,update.tree.data +multi.ace_fun.R,68,tree.data.update multi.ace_fun.R,86,castor.ace multi.ace_fun.R,152,add.state.names multi.ace_fun.R,169,translate.likelihood @@ -281,50 +318,60 @@ null.test_fun.R,12,make.null.model pair.plot.R,46,pair.plot pair.plot_fun.R,2,find.num.elements pair.plot_fun.R,7,solve.eq +pgls.dispRity.R,35,pgls.dispRity +pgls.dispRity.R,95,check.dimension +pgls.dispRity.R,104,get.formula +pgls.dispRity.R,129,get.pgls.data +pgls.dispRity.R,183,one.phylolm +pgls.dispRity.R,196,pool.pgls.param +pgls.dispRity.R,212,convert.to.summary.phylolm plot.char.diff.R,40,plot.char.diff plot.char.diff_fun.R,2,get.max.x plot.char.diff_fun.R,3,get.max.y plot.char.diff_fun.R,4,get.min.x plot.char.diff_fun.R,5,get.min.y plot.char.diff_fun.R,8,select.nas -plot.char.diff_fun.R,17,plot.char.diff.density +plot.char.diff_fun.R,17,do.plot.char.diff.density plot.dispRity.R,107,plot.dispRity plot.dispRity_fun.R,2,get.data.params plot.dispRity_fun.R,18,get.plot.params plot.dispRity_fun.R,260,get.dots plot.dispRity_fun.R,288,get.shift plot.dispRity_fun.R,304,get.quantile.col -plot.dispRity_fun.R,314,plot.observed -plot.dispRity_fun.R,333,plot.elements +plot.dispRity_fun.R,314,do.plot.observed +plot.dispRity_fun.R,333,do.plot.elements plot.dispRity_fun.R,395,seq.along.range -plot.dispRity_fun.R,409,plot.discrete -plot.dispRity_fun.R,518,plot.continuous +plot.dispRity_fun.R,409,do.plot.discrete +plot.dispRity_fun.R,518,do.plot.continuous plot.dispRity_fun.R,598,split.combine.data plot.dispRity_fun.R,607,combined.poly.args -plot.dispRity_fun.R,631,plot.rarefaction -plot.dispRity_fun.R,694,plot.preview +plot.dispRity_fun.R,631,do.plot.rarefaction +plot.dispRity_fun.R,694,do.plot.preview plot.dispRity_fun.R,699,gg.color.hue plot.dispRity_fun.R,702,make.transparent plot.dispRity_fun.R,808,plot.edge -plot.dispRity_fun.R,877,plot.randtest -plot.dispRity_fun.R,940,plot.dtt -plot.dispRity_fun.R,1023,plot.model.test -plot.dispRity_fun.R,1041,plot.model.sim -plot.dispRity_fun.R,1084,plot.test.metric -plot.dispRity_fun.R,1087,add.slope -plot.dispRity_fun.R,1100,add.fit -plot.dispRity_fun.R,1401,plot.axes -plot.dispRity_fun.R,1485,plot.projection -plot.dispRity_fun.R,1499,shmart.plot +plot.dispRity_fun.R,879,do.plot.randtest +plot.dispRity_fun.R,942,do.plot.dtt +plot.dispRity_fun.R,1025,do.plot.model.test +plot.dispRity_fun.R,1043,do.plot.model.sim +plot.dispRity_fun.R,1086,do.plot.test.metric +plot.dispRity_fun.R,1091,add.slope +plot.dispRity_fun.R,1104,p.stars +plot.dispRity_fun.R,1120,add.fit +plot.dispRity_fun.R,1458,do.plot.axes +plot.dispRity_fun.R,1542,do.plot.projection +plot.dispRity_fun.R,1556,shmart.plot print.dispRity.R,46,print.dispRity print.dispRity.R,69,remove.call -randtest.dispRity.R,55,randtest.dispRity -randtest.dispRity.R,102,all.checks -randtest.dispRity.R,159,add.n +randtest.dispRity.R,68,randtest.dispRity +randtest.dispRity.R,143,all.checks +randtest.dispRity.R,205,add.n +randtest.dispRity.R,222,make.call randtest.dispRity_fun.R,1,make.lapply.loop.resample randtest.dispRity_fun.R,5,make.lapply.loop.nosample -randtest.dispRity_fun.R,9,one.randtest -randtest.dist.R,35,randtest.dist +randtest.dispRity_fun.R,15,one.randtest +randtest.dispRity_fun.R,57,get.sample.pop +randtest.dispRity_fun.R,65,get.sample.pop.name reduce.matrix.R,33,reduce.matrix reduce.matrix.R,88,remove.one.by.one reduce.space.R,89,reduce.space @@ -337,9 +384,9 @@ reduce.space_fun.R,138,get.neigbhours reduce.space_fun.R,153,get.prob.vector reduce.space_fun.R,155,get.dimension.correction reduce.space_fun.R,161,get.prob.axis -remove.zero.brlen.R,43,remove.zero.brlen -remove.zero.brlen.R,80,slide.one.node -remove.zero.brlen.R,105,recursive.remove.zero.brlen +remove.zero.brlen.R,54,remove.zero.brlen +remove.zero.brlen.R,98,slide.one.node +remove.zero.brlen.R,132,recursive.remove.zero.brlen sanitizing.R,3,check.class sanitizing.R,68,check.length sanitizing.R,84,check.method @@ -348,9 +395,11 @@ sanitizing.R,123,stop.call sanitizing.R,128,check.list sanitizing.R,140,expect_equal_round sanitizing.R,145,add.rownames -sanitizing.R,150,check.dispRity.data -sanitizing.R,204,check.dispRity.tree -sanitizing.R,230,pass.fun +sanitizing.R,151,check.data +sanitizing.R,209,check.tree +sanitizing.R,248,pass.fun +sanitizing.R,284,check.multi.tree +sanitizing.R,298,check.dispRity.data select.axes.R,70,select.axes sim.morpho.R,66,sim.morpho sim.morpho_fun.R,4,sample.distribution @@ -362,30 +411,30 @@ sim.morpho_fun.R,60,k.sampler sim.morpho_fun.R,70,rTraitDisc.mk sim.morpho_fun.R,78,is.invariant sim.morpho_fun.R,88,MIXED.model -slice.tree.R,40,slice.tree -slice.tree.R,108,adjust.prob +slice.tree.R,54,slice.tree +slice.tree.R,122,adjust.prob slice.tree_fun.R,5,slice.tree.sharp slice.tree_fun.R,19,get.crossings -slice.tree_fun.R,64,get.node.ID -slice.tree_fun.R,72,get.node.ID.expand -slice.tree_fun.R,85,get.branch.length -slice.tree_fun.R,134,slice.tree_parent.node -slice.tree_fun.R,148,slice.tree_offspring.node -slice.tree_fun.R,190,num.decimals -slice.tree_fun.R,198,slice.tree_DELTRAN -slice.tree_fun.R,242,slice.tree_ACCTRAN -slice.tree_fun.R,249,slice.tree_PROXIMITY -slice.tree_fun.R,279,slice.edge -slide.nodes.R,51,slide.nodes +slice.tree_fun.R,70,get.node.ID +slice.tree_fun.R,78,get.node.ID.expand +slice.tree_fun.R,91,get.branch.length +slice.tree_fun.R,140,slice.tree_parent.node +slice.tree_fun.R,154,slice.tree_offspring.node +slice.tree_fun.R,196,num.decimals +slice.tree_fun.R,204,slice.tree_DELTRAN +slice.tree_fun.R,248,slice.tree_ACCTRAN +slice.tree_fun.R,255,slice.tree_PROXIMITY +slice.tree_fun.R,285,slice.edge +slide.nodes.R,52,slide.nodes slide.nodes_fun.R,2,slide.nodes.internal -space.maker.R,105,space.maker -space.maker.R,265,random.circle +space.maker.R,110,space.maker +space.maker.R,288,random.circle space.maker_fun.R,2,sample.distribution space.maker_fun.R,9,rand.circle summary.dispRity.R,55,summary.dispRity summary.dispRity.R,166,get.cent.tends summary.dispRity.R,175,get.model.summary -summary.dispRity.R,358,check.elements.NA +summary.dispRity.R,359,check.elements.NA summary.dispRity_fun.R,2,CI.converter summary.dispRity_fun.R,7,get.summary summary.dispRity_fun.R,33,lapply.summary @@ -393,7 +442,7 @@ summary.dispRity_fun.R,38,lapply.get.elements summary.dispRity_fun.R,47,lapply.observed summary.dispRity_fun.R,56,mapply.observed summary.dispRity_fun.R,65,get.digit -summary.dispRity_fun.R,74,round.column +summary.dispRity_fun.R,74,column.round summary.dispRity_fun.R,87,digits.fun summary.dispRity_fun.R,102,match.parameters summary.dispRity_fun.R,117,try.get.from.model @@ -403,9 +452,9 @@ test.dispRity_fun.R,2,test.mapply test.dispRity_fun.R,7,test.list.lapply.distributions test.dispRity_fun.R,13,set.sequence test.dispRity_fun.R,25,convert.to.numeric -test.dispRity_fun.R,30,names.fun +test.dispRity_fun.R,30,name.fun test.dispRity_fun.R,35,convert.to.character -test.dispRity_fun.R,42,rep.names +test.dispRity_fun.R,42,repeat.names test.dispRity_fun.R,48,list.to.table test.dispRity_fun.R,70,get.element test.dispRity_fun.R,75,htest.to.vector @@ -416,10 +465,10 @@ test.dispRity_fun.R,130,get.quantiles.from.table test.dispRity_fun.R,135,output.numeric.results test.dispRity_fun.R,164,lapply.output.test.elements test.dispRity_fun.R,173,output.htest.results -test.metric.R,95,test.metric -test.metric.R,143,model -test.metric.R,213,count.fails -test.metric.R,217,check.content +test.metric.R,93,test.metric +test.metric.R,141,model +test.metric.R,211,count.fails +test.metric.R,215,check.content test.metric_fun.R,2,make.reduce.space.args test.metric_fun.R,22,add.steps.to.args test.metric_fun.R,27,transform.to.dispRity diff --git a/inst/CITATION b/inst/CITATION index 421ccf1e..8508e927 100755 --- a/inst/CITATION +++ b/inst/CITATION @@ -1,8 +1,8 @@ citHeader("To cite the dispRity package (1), the dispRity manual (2), the time-slicing method (3), or the test.metric() procedure (4) please use the respective citations. You can also use the references for specific functions by looking at the function manual.") -citEntry(entry = "Article", +bibentry(bibtype = "Article", title = "dispRity: A modular R package for measuring disparity", - author = personList(as.person("Thomas Guillerme")), + author = c(as.person("Thomas Guillerme")), journal = "Methods in Ecology and Evolution", year = "2018", volume = "9", @@ -11,9 +11,9 @@ citEntry(entry = "Article", textVersion = "(1) Guillerme T. dispRity: A modular R package for measuring disparity. Methods Ecol Evol. 2018;00:1-9. https://doi.org/10.1111/2041-210X.13022" ) -citEntry(entry = "Article", +bibentry(bibtype = "Article", title = "dispRity manual", - author = personList(as.person("Thomas Guillerme"), as.person("Natalie Cooper")), + author = c(as.person("Thomas Guillerme"), as.person("Natalie Cooper")), journal = "figshare", year = "2018", volume = "preprint", @@ -21,9 +21,9 @@ citEntry(entry = "Article", textVersion = "(2) Guillerme, T. & Cooper, N. (2018): dispRity manual. figshare. Preprint. 10.6084/m9.figshare.6187337.v1" ) -citEntry(entry = "Article", +bibentry(bibtype = "Article", title = "Time for a rethink: time sub-sampling methods in disparity-through-time analyses", - author = personList(as.person("Thomas Guillerme"), as.person("Natalie Cooper")), + author = c(as.person("Thomas Guillerme"), as.person("Natalie Cooper")), journal = "Paleontology", year = "2018", volume = "61", @@ -32,9 +32,9 @@ citEntry(entry = "Article", textVersion = "(3) Guillerme, T. and Cooper, N. (2018), Time for a rethink: time sub-sampling methods in disparity-through-time analyses. Palaeontology, 61: 481-493. doi:10.1111/pala.12364" ) -citEntry(entry = "Article", +bibentry(bibtype = "Article", title = "Shifting spaces: Which disparity or dissimilarity measurement best summarize occupancy in multidimensional spaces?", - author = personList(as.person("Thomas Guillerme"), as.person("Mark N Puttick"), as.person("Ariel E Marcy"), as.person("Vera Weisbecker")), + author = c(as.person("Thomas Guillerme"), as.person("Mark N Puttick"), as.person("Ariel E Marcy"), as.person("Vera Weisbecker")), journal = "Ecology and Evolution", year = "2020", volume = "10", diff --git a/inst/gitbook/03_specific-tutorials.Rmd b/inst/gitbook/03_specific-tutorials.Rmd index a98599d3..7d7220b7 100755 --- a/inst/gitbook/03_specific-tutorials.Rmd +++ b/inst/gitbook/03_specific-tutorials.Rmd @@ -172,8 +172,9 @@ The number of bootstrap replicates can be defined using the `bootstraps` option. The method can be modified by controlling which bootstrap algorithm to use through the `boot.type` argument. Currently two algorithms are implemented: - * `full` where the bootstrapping is entirely stochastic (*n* elements are replaced by any *m* elements drawn from the data) - * `single` where only one random element is replaced by one other random element for each pseudo-replicate + * `"full"` where the bootstrapping is entirely stochastic (*n* elements are replaced by any *m* elements drawn from the data) + * `"single"` where only one random element is replaced by one other random element for each pseudo-replicate + * `"null"` where every element is resampled across the whole matrix (not just the subsets). I.e. for each subset of *n* elements, this algorithm resamples *n* elements across *ALL* subsets (not just the current one). If only one subset (or none) is used, this does the same as the `"full"` algorithm. ```{r, eval=TRUE} ## Bootstrapping with the single bootstrap method @@ -496,7 +497,7 @@ Level | Name | Description | Source | 1 | `disalignment` | The rejection of the centroid of a matrix from the major axis of another (typically an `"as.covar"` metric) | `dispRity` | 2 | `displacements` | The ratio between the distance from a reference and the distance from the centroid | `dispRity` | 1 | `edge.length.tree` | The edge lengths of the elements on a tree | `ape` | -1 | `ellipse.volume`1 | The volume of the ellipsoid of the space | Donohue _et al._ (2013) | +1 | `ellipsoid.volume`1 | The volume of the ellipsoid of the space | Donohue _et al._ (2013) | 1 | `func.div` | The functional divergence (the ratio of deviation from the centroid) | `dispRity` (similar to [`FD`](https://cran.r-project.org/web/packages/FD/index.html)`::dbFD$FDiv` but without abundance)| 1 | `func.eve` | The functional evenness (the minimal spanning tree distances evenness) | `dispRity` (similar to [`FD`](https://cran.r-project.org/web/packages/FD/index.html)`::dbFD$FEve` but without abundance)| 1 | `group.dist` | The distance between two groups | `dispRity` | @@ -511,6 +512,7 @@ Level | Name | Description | Source | 2 | `quantiles` | The *n*th quantile range per axis | `dispRity` | 2 | `radius` | The radius of each dimensions | `dispRity` | 2 | `ranges` | The range of each dimension | `dispRity` | +1 | `roundness` | The integral of the ranked scaled eigenvalues of a variance-covariance matrix | `dispRity` 2 | `span.tree.length` | The minimal spanning tree length | [`vegan`](https://cran.r-project.org/web/packages/vegan/index.html)`::spantree` | 2 | `variances` | The variance of each dimension | `dispRity` | @@ -519,8 +521,7 @@ It can, however, be fixed to a different value by using the `centroid` argument 2: This function uses an estimation of the eigenvalue that only works for MDS or PCoA ordinations (*not* PCA). -You can find more informations on the vast variety of metrics that you can use in your analysis in this [preprint](https://www.biorxiv.org/content/10.1101/801571v1). - +You can find more informations on the vast variety of metrics that you can use in your analysis in this [paper](https://onlinelibrary.wiley.com/doi/10.1002/ece3.6452). ### Equations and implementations Some of the functions described below are implemented in the `dispRity` package and do not require any other packages to calculate ([see implementation here](https://github.com/TGuillerme/dispRity/blob/master/R/dispRity.metric.R)). @@ -546,7 +547,7 @@ Some of the functions described below are implemented in the `dispRity` package \end{equation} \begin{equation} - ellipse.volume = \frac{\pi^{d/2}}{\Gamma(\frac{d}{2}+1)}\displaystyle\prod_{i=1}^{d} (\lambda_{i}^{0.5}) + ellipsoid.volume = \frac{\pi^{d/2}}{\Gamma(\frac{d}{2}+1)}\displaystyle\prod_{i=1}^{d} (\lambda_{i}^{0.5}) \end{equation} \begin{equation} @@ -568,6 +569,10 @@ Some of the functions described below are implemented in the `dispRity` package ranges = |max(d_i) - min(d_i)| \end{equation} +\begin{equation} + roundness = \int_{i = 1}^{n}{\frac{\lambda_{i}}{\text{max}(\lambda)}} +\end{equation} + \begin{equation} variances = \sigma^{2}{d_i} \end{equation} @@ -604,15 +609,15 @@ rownames(dummy_space) <- 1:10 We will use this simulated space to demonstrate the different metrics. #### Volumes and surface metrics -The functions `ellipse.volume`, `convhull.surface`, `convhull.volume` and `n.ball.volume` all measure the surface or the volume of the ordinated space occupied: +The functions `ellipsoid.volume`, `convhull.surface`, `convhull.volume` and `n.ball.volume` all measure the surface or the volume of the ordinated space occupied: Because there is only one subset (i.e. one matrix) in the dispRity object, the operations below are the equivalent of `metric(dummy_space)` (with rounding). ```{r} ## Calculating the ellipsoid volume -summary(dispRity(dummy_space, metric = ellipse.volume)) +summary(dispRity(dummy_space, metric = ellipsoid.volume)) ``` -> WARNING: in such dummy space, this gives the estimation of the ellipsoid volume, not the real ellipsoid volume! See the cautionary note in `?ellipse.volume`. +> WARNING: in such dummy space, this gives the estimation of the ellipsoid volume, not the real ellipsoid volume! See the cautionary note in `?ellipsoid.volume`. ```{r} ## Calculating the convex hull surface @@ -943,6 +948,51 @@ summary(dispRity(tree_space, metric = projections.tree, tree_space[1, ]))) ``` +#### Roundness + +The roundness coefficient (or metric) ranges between 0 and 1 and expresses the distribution of and ellipse' major axis ranging from 1, a totally round ellipse (i.e. a circle) to 0 a totally flat ellipse (i.e. a line). A value of $0.5$ represents a regular ellipse where each major axis is half the size of the previous major axis. A value $> 0.5$ describes a pancake where the major axis distribution is convex (values close to 1 can be pictured in 3D as a cr\`{e}pes with the first two axis being rather big - a circle - and the third axis being particularly thin; values closer to $0.5$ can be pictured as flying saucers). Conversely, a value $< 0.5$ describes a cigar where the major axis distribution is concave (values close to 0 can be pictured in 3D as a spaghetti with the first axis rather big and the two next ones being small; values closer to $0.5$ can be pictured in 3D as a fat cigar). + +This is what it looks for example for three simulated variance-covariance matrices in 3D: + +```{r, eval = TRUE, echo = FALSE, message = FALSE} +## Note that the plotting functions requires the ellipse, rgl and VCVtools packages (but not need to load them) +require("ellipse") +require("rgl") +require("VCVtools") +## To install them +# install.packages(c("ellipse", "rgl")) +# devtools::install_github("TGuillerme/VCVtools") + +## knitr RGL setup +# knitr::knit_hooks$set(webgl = hook_webgl) +options(rgl.useNULL = TRUE) # Suppress the separate window. + +## A 3D sphere +sphere <- VCVtools::make.VCV(shape = 1, dimensions = 3, min.thick = 0.01) +pancake <- VCVtools::make.VCV(shape = 0.75, dimensions = 3, min.thick = 0.01) +cigar <- VCVtools::make.VCV(shape = 0.25, dimensions = 3, min.thick = 0.01) +``` + +```{r, fig.height = 4, fig.width = 4, echo = FALSE} +# options(rgl.printRglwidget = TRUE) +VCVtools::plot.VCV(sphere, dimensions = c(1,2,3), col.major.axes = c("orange", "blue", "darkgreen"), main = "Sphere's roundness: 1", lwd.major.axes = 3) +rglwidget() +``` +```{r, fig.height = 4, fig.width = 4, echo = FALSE} +VCVtools::plot.VCV(pancake, dimensions = c(1,2,3), col.major.axes = c("orange", "blue", "darkgreen"), main = "Pancake's roundness: ~0.70", lwd.major.axes = 3) +rglwidget() +``` +```{r, fig.height = 4, fig.width = 4, echo = FALSE} +VCVtools::plot.VCV(cigar, dimensions = c(1,2,3), col.major.axes = c("orange", "blue", "darkgreen"), main = "Cigar's roundness: ~0.25", lwd.major.axes = 3) +rglwidget() +``` +```{r, eval = TRUE, fig.height = 4, fig.width = 12, echo = FALSE} +par(mfrow = c(1,3)) +VCVtools::plot.roundness(sphere, dimensions = c(1,2,3), col.lines = c("orange", "blue", "darkgreen"), main = "Sphere's roundness: 1") +VCVtools::plot.roundness(pancake, dimensions = c(1,2,3), col.lines = c("orange", "blue", "darkgreen"), main = "Pancake's roundness: ~0.70") +VCVtools::plot.roundness(cigar, dimensions = c(1,2,3), col.lines = c("orange", "blue", "darkgreen"), main = "Cigar's roundness: ~0.25") +``` + #### Between group metrics {#betweengroupmetricslist} You can find detailed explanation on how between group metrics work [here](#betweengroupmetricsexplain). @@ -1079,7 +1129,7 @@ This function allows to test whether a metric picks different changes in dispari Basically this is a type of biased data rarefaction (or non-biased for `"random"`) to see how the metric reacts to specific changes in trait space. ```{r} -# Creating a 2D uniform space +## Creating a 2D uniform space example_space <- space.maker(300, 2, runif) ## Testing the product of ranges metric on the example space @@ -2174,6 +2224,9 @@ Note here that the results are again rather different: with the bound data, the With the unbound data, the slices are done across the three trees and applied to the three matrices (resulting in 9 observations). As we've seen before, this is incorrect in this case since the trees don't have the same topology (so the nodes selected by a slice through the second tree are not equivalent to the nodes in the first matrix) but it can be useful if the topology is fixed to integrate both uncertainty in branch length (slicing through different trees) and uncertainty from, say, ancestral states estimations (applying the slices on different matrices). +Note that since the version `1.8` the trees and the matrices don't have to match allowing to run disparity analyses with variable matrices and trees. +This can be useful when running ancestral states estimations from a tree distribution where not all trees have the same topology. + ## Disparity with trees: *dispRitree!* {#dispRitree} @@ -2226,8 +2279,6 @@ plot(sum_edge_length) Of course this can be done with multiple trees and be combined with an approach using multiple matrices (see [here](#multi.input))! - - ## Disparity of variance-covariance matrices (covar) {#covar} Variance-covariance matrices are sometimes a useful way to summarise multidimensional data. diff --git a/inst/gitbook/05_other-functionalities.Rmd b/inst/gitbook/05_other-functionalities.Rmd index 818f2ad6..01d9d148 100755 --- a/inst/gitbook/05_other-functionalities.Rmd +++ b/inst/gitbook/05_other-functionalities.Rmd @@ -18,7 +18,7 @@ The `dispRity` package also contains several other functions that are not specif However, we decided to make these functions also available at a user level since they can be handy for certain specific operations! You'll find a brief description of each of them (alphabetically) here: -## `char.diff` +## `char.diff` {#char.diff} This is yet another function for calculating distance matrices. There are many functions for calculating pairwise distance matrices in `R` (`stats::dist`, `vegan::vegdist`, `cluster::daisy` or `Claddis::calculate_morphological_distances`) but this one is the `dispRity` one. @@ -212,7 +212,7 @@ plot(ladderize(my_tree_modif), show.tip.label = FALSE, Since version `1.7`, the `dispRity` package contains several utility functions for manipulating `"MCMCglmm"` (that is, objects returned by the function `MCMCglmm::MCMCglmm`). These objects are a modification of the `mcmc` object (from the package `coda`) and can be sometimes cumbersome to manipulate because of the huge amount of data in it. -You can use the functions `MCMCglmm.traits` for extracting the number of traits, `MCMCglmm.levels` for extracting the level names, `MCMCglmm.sample` for sampling posterior IDs and `MCMCglmm.covars` for extracting variance-covariance matrices. +You can use the functions `MCMCglmm.traits` for extracting the number of traits, `MCMCglmm.levels` for extracting the level names, `MCMCglmm.sample` for sampling posterior IDs and `MCMCglmm.covars` for extracting variance-covariance matrices. You can also quickly calculate the variance (or relative variance) for each terms in the model using `MCMCglmm.variance` (the variance is calculated as the sum of the diagonal of each variance-covariance matrix for each term). ```{r} ## Loading the charadriiformes data that contains a MCMCglmm object @@ -232,8 +232,13 @@ MCMCglmm.levels(my_MCMCglmm, convert = FALSE) ## Sampling 2 random posteriors samples IDs (random_samples <- MCMCglmm.sample(my_MCMCglmm, n = 2)) -## Extracting these two random amples +## Extracting these two random samples my_covars <- MCMCglmm.covars(my_MCMCglmm, sample = random_samples) + +## Plotting the variance for each term in the model +boxplot(MCMCglmm.variance(my_MCMCglmm), horizontal = TRUE, las = 1, + xlab = "Relative variance", + main = "Variance explained by each term") ``` See more in the [`$covar` section](#covar) on what to do with these `"MCMCglmm"` objects. @@ -484,7 +489,6 @@ plot(tree_no_zero, main = "without zero edges!") plot(tree_exaggerated, main = "with longer edges") ``` - ## `tree.age` This function allows to quickly calculate the ages of each tips and nodes present in a tree. @@ -509,3 +513,149 @@ Usually tree age is calculated from the present to the past (e.g. in million yea ## The ages in terms of tip/node height tree.age(tree, order = "present") ``` + +## `multi.ace` + +This function allows to run the `ape::ace` function (ancestral characters estimations) on multiple trees. +In it's most basic structure (e.g. using all default arguments) this function is using a mix of `ape::ace` and `castor::asr_mk_model` depending on the data and the situation and is generally faster than both functions when applied to a list of trees. +However, this function provides also some more complex and modular functionalities, especially appropriate when using discrete morphological character data. + +### Using different character tokens in different situations + +This data can be often coded in non-standard way with different character tokens having different meanings. +For example, in some datasets the token `-` can mean "the trait is inapplicable" but this can be also coded by the more conventional `NA` or can mean "this trait is missing" (often coded `?`). +This makes the meaning of specific tokens idiosyncratic to different matrices. +For example we can have the following discrete morphological matrix with all the data encoded: + +```{r} +set.seed(42) +## A random tree with 10 tips +tree <- rcoal(10) +## Setting up the parameters +my_rates = c(rgamma, rate = 10, shape = 5) + +## Generating a bunch of trees +multiple_trees <- rmtree(5, 10) + +## A random Mk matrix (10*50) +matrix_simple <- sim.morpho(tree, characters = 50, model = "ER", rates = my_rates, + invariant = FALSE) +matrix_simple[1:10, 1:10] +``` + +But of course, as mentioned above, in practice, such matrices have more nuance and can including missing characters, ambiguous characters, multi-state characters, inapplicable characters, etc... +All these coded and defined by different authors using different tokens (or symbols). +Let's give it a go and transform this simple data to something more messy: + + +```{r} +## Modify the matrix to contain missing and special data +matrix_complex <- matrix_simple +## Adding 50 random "-" tokens +matrix_complex[sample(1:length(matrix_complex), 50)] <- "-" +## Adding 50 random "?" tokens +matrix_complex[sample(1:length(matrix_complex), 50)] <- "?" +## Adding 50 random "0%2" tokens +matrix_complex[sample(1:length(matrix_complex), 50)] <- "0%2" +matrix_complex[1:10,1:10] +``` + +In `multi.ace` you can specify what all these tokens actually mean and how the code should interpret them. +For example, `-` often means inapplicable data (i.e. the specimen does not have the coded feature, for example, the colour of the tail of a tailless bird); or `?` that often means missing data (i.e. it is unknown if the specimen has a tail or not since only the head was available). +And more than the differences in meaning between these characters, different people treat these characters differently even if they have the same meaning for the token. +For example, one might want to treat `-` as meaning "we don't know" (which will be treated by the algorithm as "any possible trait value") or "we know, and it's no possible" (which will be treated by the algorithm as `NA`). +Because of this situation, `multi.ace` allows combining any special case marked with a special token to a special behaviour. +For example we might want to create a special case called `"missing"` (i.e. the data is missing) that we want to denote using the token `"?"` and we can specify the algorithm to treat this `"missing"` cases (`"?"`) as treating the character token value as "any possible values". +This behaviour can be hard coded by providing a function with the name of the behaviour. +For example: + +```{r} +## The specific token for the missing cases (note the "\\" for protecting the value) +special.tokens <- c("missing" = "\\?") + +## The behaviour for the missing cases (?) +special.behaviour <- list(missing <- function(x, y) return(y)) +## Where x is the input value (here "?") and y is all the possible normal values for the character +``` + +This example shows a very common case (and is actually used by default, more on that below) but this architecture allows for very modular combination of tokens and behaviours. +For example, in our code above we introduced the token `"%"` which is very odd (to my knowledge) and might mean something very specific in our case. +Say we want to call this case `"weirdtoken"` and mean that whenever this token is encountered in a character, it should be interpreted by the algorithm as the values 1 and 2, no matter what: + +```{r} +## Set a list of extra special tokens +my_spec_tokens <- c("weirdtoken" = "\\%") + +## Weird tokens are considered as state 0 and 3 +my_spec_behaviours <- list() +my_spec_behaviours$weirdtoken <- function(x,y) return(c(1,2)) +``` + +If you don't need/don't have any of this specific tokens, don't worry, most special but common tokens are handled by default as such: + +```{r} +## The token for missing values: +default_tokens <- c("missing" = "\\?", +## The token for inapplicable values: + "inapplicable" = "\\-", +## The token for polymorphisms: + "polymorphism" = "\\&", +## The token for uncertainties: + "uncertanity" = "\\/") +``` + +With the following associated default behaviours + +```{r} +## Treating missing data as all data values +default_behaviour <- list(missing <- function(x,y) y, +## Treating inapplicable data as all data values (like missing) + inapplicable <- function(x, y) y, +## Treating polymorphisms as all values present: + polymorphism <- function(x,y) strsplit(x, split = "\\&")[[1]], +## Treating uncertainties as all values present (like polymorphisms): + uncertanity <- function(x,y) strsplit(x, split = "\\&")[[1]]) +``` + +We can then use these token description along with our complex matrix and our list of trees to run the ancestral states estimations as follows: + +```{r} +## Running ancestral states +ancestral_states <- multi.ace(matrix_complex, multiple_trees, + special.tokens = my_spec_tokens, + special.behaviours = my_spec_behaviours, + verbose = TRUE) + +## This outputs a list of ancestral parts of the matrices for each tree +## For example, here's the first one: +ancestral_states[[1]][1:9, 1:10] +``` + +Note that there are many different options that are not covered here. +For example, you can use different models for each character via the `models` argument, you can specify how to handle uncertainties via the `threshold` argument, use a branch length modifier (`brlen.multiplier`), specify the type of output, etc... + +### Feeding the results to `char.diff` to get distance matrices + +Finally, after running your ancestral states estimations, it is not uncommon to then use these resulting data to calculate the distances between taxa and then ordinate the results to measure disparity. +You can do that using the `char.diff` function [described above](#char.diff) but instead of measuring the distances between characters (columns) you can measure the distances between species (rows). +You might notice that this function uses the same modular token and behaviour descriptions. +That makes sense because they're using the same core C functions implemented in dispRity that greatly speed up distance calculations. + +```{r} +## Running ancestral states +## and outputing a list of combined matrices (tips and nodes) +ancestral_states <- multi.ace(matrix_complex, multiple_trees, + special.tokens = my_spec_tokens, + special.behaviours = my_spec_behaviours, + output = "combined.matrix", + verbose = TRUE) +``` + +We can then feed these matrices directly to `char.diff`, say for calculating the "MORD" distance: + +```{r} +## Measuring the distances between rows using the MORD distance +distances <- lapply(ancestral_states, char.diff, method = "mord", by.col = FALSE) +``` + +And we now have a list of distances matrices with ancestral states estimated! \ No newline at end of file diff --git a/inst/gitbook/06_guts.Rmd b/inst/gitbook/06_guts.Rmd index e83fca97..1b2120b6 100755 --- a/inst/gitbook/06_guts.Rmd +++ b/inst/gitbook/06_guts.Rmd @@ -104,6 +104,16 @@ This function simply counts the number of subsets in a `dispRity` object. n.subsets(disparity) ``` +#### `name.subsets` + +This function gets you the names of the subsets in a `dispRity` object as a vector. + + +```{r} +## What are they called? +name.subsets(disparity) +``` + #### `size.subsets` This function tells the number of elements in each subsets of a `dispRity` object. @@ -150,7 +160,7 @@ get.disparity(disparity) get.disparity(disparity, observed = FALSE, subsets = 2, rarefaction = 10) ``` -#### `rescale.dispRity` +#### `scale.dispRity` This is the modified S3 method for `scale` (scaling and/or centring) that can be applied to the disparity data of a `dispRity` object and can take optional arguments (for example the rescaling by dividing by a maximum value). @@ -159,13 +169,13 @@ This is the modified S3 method for `scale` (scaling and/or centring) that can be head(summary(disparity)) ## Scaling the same disparity values -head(summary(rescale.dispRity(disparity, scale = TRUE))) +head(summary(scale.dispRity(disparity, scale = TRUE))) ## Scaling and centering: -head(summary(rescale.dispRity(disparity, scale = TRUE, center = TRUE))) +head(summary(scale.dispRity(disparity, scale = TRUE, center = TRUE))) ## Rescaling the value by dividing by a maximum value -head(summary(rescale.dispRity(disparity, max = 10))) +head(summary(scale.dispRity(disparity, max = 10))) ``` #### `sort.dispRity` @@ -196,6 +206,61 @@ remove.tree(disparity) add.tree(disparity, tree = BeckLee_tree) ``` +Note that `get.tree` can also be used to extract trees from different subsets (custom or continuous/discrete subsets). + +For example, if we have three time bins like in the example below we have three time bins and we can extract the subtrees for these three time bins in different ways using the option `subsets` and `to.root`: + +```{r} +## Load the Beck & Lee 2014 data +data(BeckLee_tree) ; data(BeckLee_mat99) ; data(BeckLee_ages) + +## Time binning (discrete method) +## Generate two discrete time bins from 120 to 40 Ma every 20 Ma +time_bins <- chrono.subsets(data = BeckLee_mat99, tree = BeckLee_tree, + method = "discrete", time = c(120, 100, 80, 60), + inc.nodes = TRUE, FADLAD = BeckLee_ages) + +## Getting the subtrees all the way to the root +root_subsets <- get.tree(time_bins, subsets = TRUE) + +## Plotting the bin contents +old_par <- par(mfrow = c(2,2)) +plot(BeckLee_tree, main = "original tree", show.tip.label = FALSE) +axisPhylo() +abline(v = BeckLee_tree$root.time - c(120, 100, 80, 60)) +for(i in 1:3) { + plot(root_subsets[[i]], main = names(root_subsets)[i], + show.tip.label = FALSE) + axisPhylo() +} +par(old_par) +``` + +But we can also extract the subtrees containing only branch lengths for the actual bins using `to.root = FALSE`: + +```{r} +## Getting the subtrees all the way to the root +bin_subsets <- get.tree(time_bins, subsets = TRUE, to.root = FALSE) + +## Plotting the bin contents +old_par <- par(mfrow = c(2,2)) +plot(BeckLee_tree, main = "original tree", show.tip.label = FALSE) +axisPhylo() +abline(v = BeckLee_tree$root.time - c(120, 100, 80, 60)) +for(i in 1:3) { + plot(bin_subsets[[i]], main = names(bin_subsets)[i], + show.tip.label = FALSE) + axisPhylo() +} +par(old_par) +``` + +This can be useful for example for calculating the branch lengths in each bin: + +```{r} +## How many cumulated phylogenetic diversity in each bin? +lapply(bin_subsets, function(tree) sum(tree$edge.length)) +``` ## The `dispRity` object content {#disprity-object} diff --git a/inst/gitbook/07_ecology-demo.Rmd b/inst/gitbook/07_ecology-demo.Rmd index a4b6bcfe..1a1da1e3 100755 --- a/inst/gitbook/07_ecology-demo.Rmd +++ b/inst/gitbook/07_ecology-demo.Rmd @@ -11,8 +11,7 @@ vignette: > \usepackage[utf8]{inputenc} --- - -# Ecology demo +# dispRity ecology demo This is an example of typical disparity analysis that can be performed in ecology. diff --git a/inst/gitbook/08_palaeo-demo.Rmd b/inst/gitbook/08_palaeo-demo.Rmd index 64a348cd..c7f98256 100755 --- a/inst/gitbook/08_palaeo-demo.Rmd +++ b/inst/gitbook/08_palaeo-demo.Rmd @@ -8,7 +8,6 @@ output: pdf_document: default --- - # Palaeobiology demo: disparity-through-time and within groups This demo aims to give quick overview of the `dispRity` package (v.`r version_release`) for palaeobiology analyses of disparity, including disparity through time analyses. diff --git a/inst/gitbook/09_gmm-demo.Rmd b/inst/gitbook/09_gmm-demo.Rmd index a0c7bfc5..a59fed49 100755 --- a/inst/gitbook/09_gmm-demo.Rmd +++ b/inst/gitbook/09_gmm-demo.Rmd @@ -8,7 +8,6 @@ output: pdf_document: default --- - # Morphometric geometric demo: a between group analysis This demo aims to give quick overview of the `dispRity` package (v.`r version_release`) for palaeobiology analyses of disparity, including disparity through time analyses. diff --git a/inst/gitbook/10_references.Rmd b/inst/gitbook/10_references.Rmd index 0e3987ae..16da0a2e 100755 --- a/inst/gitbook/10_references.Rmd +++ b/inst/gitbook/10_references.Rmd @@ -1,5 +1,19 @@ +--- +title: "dispRity R package manual" +author: "Thomas Guillerme (guillert@tcd.ie)" +date: "`r Sys.Date()`" +bibliography: [../References.bib, ../packages.bib] +output: + html_document: default + pdf_document: default +--- + +# dispRity R package manual + `r if (knitr:::is_html_output()) '# References {-}'` +# References + ```{r include=FALSE} # generate a BibTeX database automatically for some R packages knitr::write_bib(c( diff --git a/inst/gitbook/_book/404.html b/inst/gitbook/_book/404.html index 6b31ec57..00c4f115 100644 --- a/inst/gitbook/_book/404.html +++ b/inst/gitbook/_book/404.html @@ -4,26 +4,26 @@ - Page not found | Morphometric geometric demo: a between group analysis - - + Page not found | dispRity R package manual + + - + - + - + - + - + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -397,7 +430,7 @@

    Page not found

    var script = document.createElement("script"); script.type = "text/javascript"; var src = "true"; - if (src === "" || src === "true") src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-MML-AM_CHTML"; + if (src === "" || src === "true") src = "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.9/latest.js?config=TeX-MML-AM_CHTML"; if (location.protocol !== "file:") if (/^https?:/.test(src)) src = src.replace(/^https?:/, ''); diff --git a/inst/gitbook/_book/details-of-specific-functions.html b/inst/gitbook/_book/details-of-specific-functions.html index bb95426f..b3e67015 100644 --- a/inst/gitbook/_book/details-of-specific-functions.html +++ b/inst/gitbook/_book/details-of-specific-functions.html @@ -4,26 +4,26 @@ - 4 Details of specific functions | Morphometric geometric demo: a between group analysis - - + 4 Details of specific functions | dispRity R package manual + + - + - + - + - + - + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -480,8 +513,9 @@

    4.3 Bootstraps and rarefactionsboot.type argument. Currently two algorithms are implemented:

      -
    • full where the bootstrapping is entirely stochastic (n elements are replaced by any m elements drawn from the data)
      • -
    • single where only one random element is replaced by one other random element for each pseudo-replicate
      • +
    • "full" where the bootstrapping is entirely stochastic (n elements are replaced by any m elements drawn from the data)
      • +
    • "single" where only one random element is replaced by one other random element for each pseudo-replicate
      • +
    • "null" where every element is resampled across the whole matrix (not just the subsets). I.e. for each subset of n elements, this algorithm resamples n elements across ALL subsets (not just the current one). If only one subset (or none) is used, this does the same as the "full" algorithm.
    ## Bootstrapping with the single bootstrap method
 boot.matrix(BeckLee_mat50, boot.type = "single")
    @@ -863,7 +897,7 @@

    4.4.5 Metrics implemented in 1 -ellipse.volume1 +ellipsoid.volume1 The volume of the ellipsoid of the space Donohue et al. (2013) @@ -952,12 +986,18 @@

    4.4.5 Metrics implemented in dispRity +1 +roundness +The integral of the ranked scaled eigenvalues of a variance-covariance matrix +dispRity + + 2 span.tree.length The minimal spanning tree length vegan::spantree - + 2 variances The variance of each dimension @@ -968,8 +1008,7 @@

    4.4.5 Metrics implemented in 1: Note that by default, the centroid is the centroid of the elements. It can, however, be fixed to a different value by using the centroid argument centroids(space, centroid = rep(0, ncol(space))), for example the origin of the ordinated space.

    2: This function uses an estimation of the eigenvalue that only works for MDS or PCoA ordinations (not PCA).

    -

    You can find more informations on the vast variety of metrics that you can use in your analysis in this preprint. -

    +

    You can find more informations on the vast variety of metrics that you can use in your analysis in this paper.

    4.4.6 Equations and implementations

    @@ -990,7 +1029,7 @@

    4.4.6 Equations and implementatio displacements = \frac{\sqrt{\sum_{i=1}^{n}{({d}_{n}-Reference_{d})^2}}}{\sqrt{\sum_{i=1}^{n}{({d}_{n}-Centroid_{k})^2}}} \end{equation}\]

    \[\begin{equation} - ellipse.volume = \frac{\pi^{d/2}}{\Gamma(\frac{d}{2}+1)}\displaystyle\prod_{i=1}^{d} (\lambda_{i}^{0.5}) + ellipsoid.volume = \frac{\pi^{d/2}}{\Gamma(\frac{d}{2}+1)}\displaystyle\prod_{i=1}^{d} (\lambda_{i}^{0.5}) \end{equation}\]

    \[\begin{equation} n.ball.volume = \frac{\pi^{d/2}}{\Gamma(\frac{d}{2}+1)}\displaystyle\prod_{i=1}^{d} R @@ -1007,6 +1046,9 @@

    4.4.6 Equations and implementatio

    \[\begin{equation} ranges = |max(d_i) - min(d_i)| \end{equation}\]

    +

    \[\begin{equation} + roundness = \int_{i = 1}^{n}{\frac{\lambda_{i}}{\text{max}(\lambda)}} +\end{equation}\]

    \[\begin{equation} variances = \sigma^{2}{d_i} \end{equation}\]

    @@ -1037,14 +1079,14 @@

    4.4.7 Using the different dispari

    We will use this simulated space to demonstrate the different metrics.

    4.4.7.1 Volumes and surface metrics

    -

    The functions ellipse.volume, convhull.surface, convhull.volume and n.ball.volume all measure the surface or the volume of the ordinated space occupied:

    +

    The functions ellipsoid.volume, convhull.surface, convhull.volume and n.ball.volume all measure the surface or the volume of the ordinated space occupied:

    Because there is only one subset (i.e. one matrix) in the dispRity object, the operations below are the equivalent of metric(dummy_space) (with rounding).

    ## Calculating the ellipsoid volume
-summary(dispRity(dummy_space, metric = ellipse.volume))
    +summary(dispRity(dummy_space, metric = ellipsoid.volume))
    ##   subsets  n   obs
 ## 1       1 10 1.061
    -

    WARNING: in such dummy space, this gives the estimation of the ellipsoid volume, not the real ellipsoid volume! See the cautionary note in ?ellipse.volume.

    +

    WARNING: in such dummy space, this gives the estimation of the ellipsoid volume, not the real ellipsoid volume! See the cautionary note in ?ellipsoid.volume.

    ## Calculating the convex hull surface
 summary(dispRity(dummy_space, metric = convhull.surface))
    @@ -1302,14 +1344,14 @@

    4.4.7.7 Projections and phylo pro point1 = dummy_space[1,], point2 = dummy_space[2,]))

    ##   subsets  n obs.median  2.5%   25%   75% 97.5%
-## 1       1 10          1 0.067 0.852 1.477 2.769
    +## 1 1 10 0.998 0.118 0.651 1.238 1.885 
    ## The exploration on the same axis
 summary(dispRity(dummy_space, metric = projections,
                               point1 = dummy_space[1,],
                               point2 = dummy_space[2,],
                               measure = "distance"))
    ##   subsets  n obs.median 2.5%   25%   75% 97.5%
-## 1       1 10       0.36    0 0.284 0.456 0.825
    +## 1 1 10 0.719 0 0.568 0.912 1.65

    By default, the vector (point1, point2) is used as unit vector of the projections (i.e. the Euclidean distance between (point1, point2) is set to 1) meaning that a projection value ("distance" or "position") of X means X times the distance between point1 and point2. If you want use the unit vector of the input matrix or are using a space where Euclidean distances are non-sensical, you can remove this option using scale = FALSE:

    ## The elaboration on the same axis using the dummy_space's
@@ -1318,8 +1360,8 @@ 
    4.4.7.7 Projections and phylo pro
                               point1 = dummy_space[1,],
                               point2 = dummy_space[2,],
                               scale = FALSE))
    -
    ##   subsets  n obs.median  2.5%   25% 75% 97.5%
-## 1       1 10      7.137 1.511 4.311 9.1 14.37
    +
    ##   subsets  n obs.median  2.5%   25%  75% 97.5%
+## 1       1 10      4.068 0.481 2.655 5.05 7.685

    The projections.tree is the same as the projections metric but allows to determine the vector ((point1, point2)) using a tree rather than manually entering these points. The function intakes the exact same options as the projections function described above at the exception of point1 and point2. Instead it takes a the argument type that designates the type of vector to draw from the data based on a phylogenetic tree phy. @@ -1353,7 +1395,7 @@

    4.4.7.7 Projections and phylo pro ## Warning in max(nchar(round(column)), na.rm = TRUE): no non-missing arguments to ## max; returning -Inf 
    ##   subsets  n obs.median  2.5%   25%   75% 97.5%
-## 1       1 11         NA 0.085 0.157 0.558 1.046
    +## 1 1 11 NA 0.229 0.416 0.712 1.016

    Of course you can also use any other options from the projections function:

    ## A user defined function that's returns the centroid of
 ## the first three nodes
@@ -1369,13 +1411,37 @@ 
    4.4.7.7 Projections and phylo pro
                              type    = list(fun.root,
                                             tree_space[1, ])))
    ##   subsets  n obs.median  2.5%   25%   75% 97.5%
-## 1       1 11      0.303 0.032 0.231 0.366   0.5
    +## 1 1 11 0.606 0.064 0.462 0.733 0.999

    -
    -

    4.4.7.8 Between group metrics

    +
    +

    4.4.7.8 Roundness

    +

    The roundness coefficient (or metric) ranges between 0 and 1 and expresses the distribution of and ellipse’ major axis ranging from 1, a totally round ellipse (i.e. a circle) to 0 a totally flat ellipse (i.e. a line). A value of \(0.5\) represents a regular ellipse where each major axis is half the size of the previous major axis. A value \(> 0.5\) describes a pancake where the major axis distribution is convex (values close to 1 can be pictured in 3D as a cr`{e}pes with the first two axis being rather big - a circle - and the third axis being particularly thin; values closer to \(0.5\) can be pictured as flying saucers). Conversely, a value \(< 0.5\) describes a cigar where the major axis distribution is concave (values close to 0 can be pictured in 3D as a spaghetti with the first axis rather big and the two next ones being small; values closer to \(0.5\) can be pictured in 3D as a fat cigar).

    +

    This is what it looks for example for three simulated variance-covariance matrices in 3D:

    +
    +
    + + +
    +
    +
    + + +
    +
    +
    + + +
    +

    +
    +
    +

    4.4.7.9 Between group metrics

    You can find detailed explanation on how between group metrics work here.

    -
    -
    4.4.7.8.1 group.dist
    +
    +
    4.4.7.9.1 group.dist

    The group.dist metric allows to measure the distance between two groups in the multidimensional space. This function needs to intake several groups and use the option between.groups = TRUE in the dispRity function. It calculates the vector normal distance (euclidean) between two groups and returns 0 if that distance is negative. @@ -1403,8 +1469,8 @@

    4.4.7.8.1 group.dist 
    ##   subsets n_1 n_2   obs
 ## 1     1:2   5   5 0.059
    -
    -
    4.4.7.8.2 point.dist
    +
    +
    4.4.7.9.2 point.dist

    The metric measures the distance between the elements in one group (matrix) and a point calculated from a second group (matrix2). By default this point is the centroid but can be any point defined by a function passed to the point argument. For example, the centroid of matrix2 is the mean of each column of that matrix so point = colMeans (default). @@ -1431,8 +1497,8 @@

    4.4.7.8.2 point.dist 
    ##   subsets n_1 n_2 obs.median  2.5%   25%   75% 97.5%
 ## 1     1:2   5   5      4.043 2.467 3.567 4.501 6.884
    -
    -
    4.4.7.8.3 projections.between and disalignment
    +
    +
    4.4.7.9.3 projections.between and disalignment

    These two metrics are typically based on variance-covariance matrices from a dispRity object that has a $covar component (see more about that here). Both are based on the projections metric and can take the same optional arguments (more info here). The examples and explanations below are based on the default arguments but it is possible (and easy!) to change them.

    @@ -1464,7 +1530,7 @@
    4.4.7.8.3 projections.betwe summary(groups_angles)
    ##                subsets n_1 n_2 obs.median  2.5%   25%   75% 97.5%
 ## 1      gulls:phylogeny 159 359       8.25 2.101  6.25 14.98  41.8
-## 2    plovers:phylogeny  98 359      33.75 5.700 16.33 83.04 130.4
+## 2    plovers:phylogeny  98 359      33.75 5.700 16.33 75.50 131.5
 ## 3 sandpipers:phylogeny 102 359      10.79 3.876  8.10 16.59  95.9

    The second metric, disalignment rejects the centroid of a group (matrix) onto the major axis of another one (matrix2). This allows to measure wether the center of a group is aligned with the major axis of another. @@ -1476,10 +1542,10 @@

    4.4.7.8.3 projections.betwe between.groups = comparisons_list) ## And here are the groups alignment (0 = aligned) summary(groups_alignement)
    -
    ##                subsets n_1 n_2 obs.median 2.5%   25%   75% 97.5%
-## 1      gulls:phylogeny 159 359      0.002    0 0.001 0.002 0.007
-## 2    plovers:phylogeny  98 359      0.000    0 0.000 0.001 0.003
-## 3 sandpipers:phylogeny 102 359      0.001    0 0.000 0.001 0.005
    +
    ##                subsets n_1 n_2 obs.median  2.5%   25%   75% 97.5%
+## 1      gulls:phylogeny 159 359      0.003 0.001 0.002 0.005 0.015
+## 2    plovers:phylogeny  98 359      0.001 0.000 0.001 0.001 0.006
+## 3 sandpipers:phylogeny 102 359      0.002 0.000 0.001 0.003 0.009
    @@ -1496,7 +1562,7 @@

    4.4.8 Which disparity metric to c

    4.4.8.1 test.metric

    This function allows to test whether a metric picks different changes in disparity. It intakes the space on which to test the metric, the disparity metric and the type of changes to apply gradually to the space. Basically this is a type of biased data rarefaction (or non-biased for "random") to see how the metric reacts to specific changes in trait space.

    -
    # Creating a 2D uniform space
+
    ## Creating a 2D uniform space
 example_space <- space.maker(300, 2, runif)
 
 ## Testing the product of ranges metric on the example space
@@ -1518,17 +1584,17 @@ 
    4.4.8.1 test.metric<
 ## Use summary(x) or plot(x) for more details.
    
 
    ## Summarising these results
 summary(example_test)
    
-
    ##             10%  20%  30%  40%  50%  60%  70%  80%  90% 100%        slope
-## random     0.84 0.88 0.94 0.95 0.96 0.98 0.97 0.98 0.96 0.98 1.450100e-03
-## size.inner 0.10 0.21 0.31 0.45 0.54 0.70 0.78 0.94 0.96 0.98 1.054925e-02
-## size.outer 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 1.453782e-05
-##                 p_value  R^2(adj)
-## random     2.439179e-06 0.5377136
-## size.inner 4.450564e-25 0.9783976
-## size.outer 1.925262e-05 0.4664502
    
+
    ##                  10%  20%  30%  40%  50%  60%  70%  80%  90% 100%        slope
+## random          0.84 0.88 0.94 0.95 0.96 0.98 0.97 0.98 0.96 0.98 1.450100e-03
+## size.increase   0.10 0.21 0.31 0.45 0.54 0.70 0.78 0.94 0.96 0.98 1.054925e-02
+## size.hollowness 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 1.453782e-05
+##                      p_value  R^2(adj)
+## random          2.439179e-06 0.5377136
+## size.increase   4.450564e-25 0.9783976
+## size.hollowness 1.925262e-05 0.4664502
    
 
    ## Or visualising them
 plot(example_test)
    
-
    
+
    @@ -1656,7 +1722,7 @@

    4.5.2 Plotting dispRity ## As above but using polygons while also displaying the number of elements plot(disparity_crown_stem, type = "polygon", elements = TRUE) -

    +

    ## Resetting graphical parameters
 par(op)

    Since plot.dispRity uses the arguments from the generic plot method, it is of course possible to change pretty much everything using the regular plot arguments:

    @@ -1673,7 +1739,7 @@

    4.5.2 Plotting dispRity "Confidence interval 1", "Confidence interval 2"), col = c("blue", "orange", "green"), pch = 19) -

    +

    ## Resetting graphical parameters
 par(op)

    In addition to the classic plot arguments, the function can also take arguments that are specific to plot.dispRity like adding the number of elements or rarefaction level (as described above), and also changing the values of the quantiles to plot as well as the central tendency.

    @@ -1689,7 +1755,7 @@

    4.5.2 Plotting dispRity xlab = "Time (in in units from past to present)", observed = TRUE, main = "Many more options...") -

    +

    ## Resetting graphical parameters
 par(op)
    @@ -1704,7 +1770,7 @@

    4.5.2 Plotting dispRity## Adding the discrete data plot(disparity_time_bins, type = "line", ylim = c(3, 9), xlab = "", ylab = "", add = TRUE) -

    +

    ## Resetting graphical parameters
 par(op)

    Finally, if your data has been fully rarefied, it is also possible to easily look at rarefaction curves by using the rarefaction = TRUE argument:

    @@ -1713,7 +1779,7 @@

    4.5.2 Plotting dispRity ## Plotting the rarefaction curves plot(disparity_crown_stem, rarefaction = TRUE) -

    +

    ## Resetting graphical parameters
 par(op)
    @@ -1745,7 +1811,7 @@

    4.5.3 type = preview ## Plotting with more arguments plot(time_subsets, specific.args = list(dimensions = c(1,2)), main = "Some \"low\" dimensions") -

    +

    DISCLAIMER: This functionality can be handy for exploring the data (e.g. to visually check whether the subset attribution worked) but it might be misleading on how the data is actually distributed in the multidimensional space! Groups that don’t overlap on two set dimensions can totally overlap in all other dimensions!

    @@ -1758,7 +1824,7 @@

    4.5.3 type = preview ## Plotting with more arguments plot(disparity_time_slices, type = "preview", main = "Two first dimensions of the trait space") -

    +

    4.5.4 Graphical options with ...

    @@ -1797,7 +1863,7 @@

    4.5.4 Graphical options with ## And now without the legend plot(time_slices, type = "preview", specific.args = list(tree = TRUE), lines.col = "blue", points.pch = 15, legend = FALSE)

    -

    +

    @@ -2008,7 +2074,7 @@

    4.6.2 geiger::dtt mo ## The following tip(s) was not present in the data: olivacea. 
    ## Plotting the results
 plot(dispRity_dtt)
    -

    +

    Note that, like in the original dtt function, it is possible to change the evolutionary model (see ?geiger::sim.char documentation).

    @@ -2023,8 +2089,8 @@

    4.6.3 null morphospace testing wi ## Calculating the disparity as the average pairwise distances obs_disparity <- dispRity(normal_space, metric = c(mean, pairwise.dist))

    -
    ## Warning in check.dispRity.data(data): Row names have been automatically added to
-## data.
    +
    ## Warning in check.data(data, match_call): Row names have been automatically
+## added to data.
    ## Testing against 100 randomly generated normal spaces
 (results <- null.test(obs_disparity, replicates = 100,
                       null.distrib = rnorm))
    @@ -2044,7 +2110,7 @@

    4.6.3 null morphospace testing wi

    These results have an attributed dispRity and randtest class and can be plotted as randtest objects using the dispRity S3 plot method:

    ## Plotting the results
 plot(results, main = "Is this space normal?")
    -

    +

    For more details on generating spaces see the space.maker function tutorial.

    @@ -2152,20 +2218,20 @@

    4.7.1.1 model.test 
    summary(disp_time)
    ##        aicc delta_aicc weight_aicc log.lik param theta.1 omega ancestral state
 ## Stasis   41      339.5       0.000   -18.7     2   3.629 0.074              NA
 ## BM     -294        3.6       0.112   149.3     2      NA    NA           3.267
-## OU     -296        2.1       0.227   152.1     4      NA    NA           3.255
+## OU     -296        2.1       0.227   152.1     4      NA    NA           3.254
 ## Trend  -298        0.0       0.661   152.1     3      NA    NA           3.255
 ## EB     -246       51.7       0.000   126.3     3      NA    NA           4.092
 ##        sigma squared alpha optima.1 trend     eb
 ## Stasis            NA    NA       NA    NA     NA
 ## BM             0.001    NA       NA    NA     NA
-## OU             0.001 0.001    10.89    NA     NA
+## OU             0.001 0.001    12.35    NA     NA
 ## Trend          0.001    NA       NA 0.007     NA
 ## EB             0.000    NA       NA    NA -0.032

    These models indicate support for a Trend model, and we can plot the relative support of all model AICc weights.

    @@ -2206,7 +2272,7 @@

    4.7.2.1 model.test.wrapper< ## Variance is not pooled. ## Running Stasis model...Done. Log-likelihood = -18.694 ## Running BM model...Done. Log-likelihood = 149.289 -## Running OU model...Done. Log-likelihood = 152.118 +## Running OU model...Done. Log-likelihood = 152.119 ## Running Trend model...Done. Log-likelihood = 152.116 ## Running EB model...Done. Log-likelihood = 126.268
    @@ -2218,26 +2284,26 @@

    4.7.2.1 model.test.wrapper< 
    disp_time
    ##        aicc delta_aicc weight_aicc log.lik param theta.1 omega ancestral state
 ## Trend  -298        0.0       0.661   152.1     3      NA    NA           3.255
-## OU     -296        2.1       0.227   152.1     4      NA    NA           3.255
+## OU     -296        2.1       0.227   152.1     4      NA    NA           3.254
 ## BM     -294        3.6       0.112   149.3     2      NA    NA           3.267
 ## EB     -246       51.7       0.000   126.3     3      NA    NA           4.092
 ## Stasis   41      339.5       0.000   -18.7     2   3.629 0.074              NA
 ##        sigma squared alpha optima.1 trend     eb median p value lower p value
-## Trend          0.001    NA       NA 0.007     NA     0.97752248   0.977022977
-## OU             0.001 0.001    10.89    NA     NA     0.97302697   0.972027972
-## BM             0.001    NA       NA    NA     NA     0.16283716   0.137862138
-## EB             0.000    NA       NA    NA -0.032     0.06893107   0.000999001
-## Stasis            NA    NA       NA    NA     NA     1.00000000   1.000000000
+## Trend          0.001    NA       NA 0.007     NA    0.978021978     0.9760240
+## OU             0.001 0.001    12.35    NA     NA    0.978021978     0.9770230
+## BM             0.001    NA       NA    NA     NA    0.143856144     0.1368631
+## EB             0.000    NA       NA    NA -0.032    0.000999001     0.0000000
+## Stasis            NA    NA       NA    NA     NA    1.000000000     0.9990010
 ##        upper p value
 ## Trend      0.9780220
-## OU         0.9740260
+## OU         0.9780220
 ## BM         0.1878122
 ## EB         0.1368631
 ## Stasis     1.0000000

    From this plot we can see the empirical estimates of disparity through time (pink) compared to the predictive data based upon the simulations using the estimated parameters from each model. There is no significant differences between the empirical data and simulated data, except for the Early Burst model.

    Trend is the best-fitting model but the plot suggests the OU model also follows a trend-like pattern. -This is because the optima for the OU model (10.89) is different to the ancestral state (3.255) and outside the observed value. +This is because the optima for the OU model (12.35) is different to the ancestral state (3.254) and outside the observed value. This is potentially unrealistic, and one way to alleviate this issue is to set the optima of the OU model to equal the ancestral estimate - this is the normal practice for OU models in comparative phylogenetics. To set the optima to the ancestral value we change the argument fixed.optima = TRUE:

    disp_time <- model.test.wrapper(data = BeckLee_disparity,
@@ -2264,11 +2330,11 @@ 
    4.7.2.1 model.test.wrapper<
 ## EB     -246       51.7       0.000   126.3     3      NA    NA           4.092
 ## Stasis   41      339.5       0.000   -18.7     2   3.629 0.074              NA
 ##        sigma squared alpha trend     eb median p value lower p value
-## Trend          0.001    NA 0.007     NA     0.98351648   0.983016983
-## BM             0.001    NA    NA     NA     0.26473526   0.249750250
-## OU             0.001     0    NA     NA     0.30469530   0.292707293
-## EB             0.000    NA    NA -0.032     0.06943057   0.000999001
-## Stasis            NA    NA    NA     NA     0.99900100   0.999000999
+## Trend          0.001    NA 0.007     NA    0.984015984     0.9820180
+## BM             0.001    NA    NA     NA    0.256743257     0.2487512
+## OU             0.001     0    NA     NA    0.293706294     0.2917083
+## EB             0.000    NA    NA -0.032    0.000999001     0.0000000
+## Stasis            NA    NA    NA     NA    0.999000999     0.9980020
 ##        upper p value
 ## Trend      0.9840160
 ## BM         0.2797203
@@ -2299,7 +2365,7 @@ 
    4.7.3 Multiple modes of evolution
 ## Running BM model...Done. Log-likelihood = 149.289
 ## Running Trend model...Done. Log-likelihood = 152.116
 ## Running OU model...Done. Log-likelihood = 149.289
-## Running multi.OU model...Done. Log-likelihood = 152.124
    +## Running multi.OU model...Done. Log-likelihood = 151.958
    Empirical disparity through time (pink), simulate data based on estimated model parameters (grey), delta AICc, and range of p values from the Rank Envelope test for BM, Trend, OU, and multi OU models with a shift in optima allowed at 66 Ma

    @@ -2308,18 +2374,18 @@

    4.7.3 Multiple modes of evolution 

    disp_time
    ##          aicc delta_aicc weight_aicc log.lik param ancestral state
-## Trend    -298      0.000       0.635   152.1     3           3.255
-## multi.OU -296      2.124       0.220   152.1     4           3.254
-## BM       -294      3.550       0.108   149.3     2           3.267
-## OU       -292      5.654       0.038   149.3     3           3.267
+## Trend    -298      0.000       0.657   152.1     3           3.255
+## multi.OU -296      2.456       0.193   152.0     4           3.253
+## BM       -294      3.550       0.111   149.3     2           3.267
+## OU       -292      5.654       0.039   149.3     3           3.267
 ##          sigma squared trend alpha optima.2 median p value lower p value
-## Trend            0.001 0.007    NA       NA      0.9870130     0.9870130
-## multi.OU         0.001    NA 0.001    10.63      0.9690310     0.9680320
-## BM               0.001    NA    NA       NA      0.2012987     0.1818182
-## OU               0.001    NA 0.000       NA      0.2867133     0.2717283
+## Trend            0.001 0.007    NA       NA      0.9870130     0.9860140
+## multi.OU         0.001    NA 0.006    4.686      0.9570430     0.9560440
+## BM               0.001    NA    NA       NA      0.1868132     0.1808192
+## OU               0.001    NA 0.000       NA      0.2727273     0.2707293
 ##          upper p value
 ## Trend        0.9870130
-## multi.OU     0.9700300
+## multi.OU     0.9590410
 ## BM           0.2207792
 ## OU           0.3016983

    The multi-OU model shows an increase an optima at the Cretaceous-Palaeogene boundary, indicating a shift in disparity. @@ -2369,17 +2435,17 @@

    4.7.3 Multiple modes of evolution ## Stasis:OU -240 47.7 0.000 125.1 5 NA ## BM:Stasis -130 157.1 0.000 69.3 4 3.268 ## sigma squared alpha optima.1 theta.1 omega trend median p value -## OU:Trend 0.001 0.041 NA NA NA 0.011 0.3246753 -## BM:OU 0.001 0.000 4.092 NA NA NA 0.5009990 +## OU:Trend 0.001 0.041 NA NA NA 0.011 0.2987013 +## BM:OU 0.001 0.000 4.092 NA NA NA 0.4925075 ## Stasis:BM 0.002 NA NA 3.390 0.004 NA 0.9970030 ## Stasis:OU 0.002 0.000 4.092 3.390 0.004 NA 1.0000000 ## BM:Stasis 0.000 NA NA 3.806 0.058 NA 1.0000000 ## lower p value upper p value -## OU:Trend 0.2957043 0.3536464 -## BM:OU 0.4885115 0.5134865 -## Stasis:BM 0.9970030 0.9970030 -## Stasis:OU 1.0000000 1.0000000 -## BM:Stasis 1.0000000 1.0000000 +## OU:Trend 0.2947053 0.3536464 +## BM:OU 0.4875125 0.5134865 +## Stasis:BM 0.9960040 0.9970030 +## Stasis:OU 0.9990010 1.0000000 +## BM:Stasis 0.9990010 1.0000000

    4.7.4 model.test.sim

    @@ -2434,20 +2500,20 @@

    4.7.4.1 Simulating tested models< ## Variance is not pooled. ## Running Stasis model...Done. Log-likelihood = -18.694 ## Running BM model...Done. Log-likelihood = 149.289 -## Running OU model...Done. Log-likelihood = 152.118 +## Running OU model...Done. Log-likelihood = 152.119 ## Running Trend model...Done. Log-likelihood = 152.116 ## Running EB model...Done. Log-likelihood = 126.268 
    summary(disp_time)
    ##        aicc delta_aicc weight_aicc log.lik param theta.1 omega ancestral state
 ## Stasis   41      339.5       0.000   -18.7     2   3.629 0.074              NA
 ## BM     -294        3.6       0.112   149.3     2      NA    NA           3.267
-## OU     -296        2.1       0.227   152.1     4      NA    NA           3.255
+## OU     -296        2.1       0.227   152.1     4      NA    NA           3.254
 ## Trend  -298        0.0       0.661   152.1     3      NA    NA           3.255
 ## EB     -246       51.7       0.000   126.3     3      NA    NA           4.092
 ##        sigma squared alpha optima.1 trend     eb
 ## Stasis            NA    NA       NA    NA     NA
 ## BM             0.001    NA       NA    NA     NA
-## OU             0.001 0.001    10.89    NA     NA
+## OU             0.001 0.001    12.35    NA     NA
 ## Trend          0.001    NA       NA 0.007     NA
 ## EB             0.000    NA       NA    NA -0.032

    As seen before, the Trend model fitted this dataset the best. @@ -2462,9 +2528,9 @@

    4.7.4.1 Simulating tested models< ## aicc log.lik param ancestral state sigma squared trend ## Trend -298 152.1 3 3.255 0.001 0.007 ## -## Rank envelope test -## p-value of the test: 0.99001 (ties method: midrank) -## p-interval : (0.99001, 0.99001) +## Rank envelope test: +## p-value of the global test: 0.99001 (ties method: erl) +## p-interval : (0.989011, 0.99001)

    By default, the model simulated is the one with the lowest AICc (model.rank = 1) but it is possible to choose any ranked model, for example, the OU (second one):

    ## Simulating 1000 OU model with the observed parameters
 sim_OU <- model.test.sim(sim = 1000, model = disp_time,
@@ -2475,11 +2541,11 @@ 
    4.7.4.1 Simulating tested models<
 ## 
 ## Model simulated (1000 times):
 ##    aicc log.lik param ancestral state sigma squared alpha optima.1
-## OU -296   152.1     4           3.255         0.001 0.001    10.89
+## OU -296   152.1     4           3.254         0.001 0.001    12.35
 ## 
-## Rank envelope test
-##  p-value of the test: 0.9895105 (ties method: midrank)
-##  p-interval         : (0.989011, 0.99001)
    +## Rank envelope test: +## p-value of the global test: 0.992008 (ties method: erl) +## p-interval : (0.99001, 0.992008)

    And as the example above, the simulated data can be plotted or summarised:

    head(summary(sim_trend))
    ##   subsets n        var   median     2.5%      25%      75%    97.5%
@@ -2491,12 +2557,12 @@ 
    4.7.4.1 Simulating tested models<
 ## 6     115 7 0.03264826 3.293918 3.101298 3.231659 3.354321 3.474645
    head(summary(sim_OU))
    ##   subsets n        var   median     2.5%      25%      75%    97.5%
-## 1     120 5 0.01723152 3.254315 3.142419 3.213128 3.294708 3.372570
-## 2     119 5 0.03555816 3.264164 3.084458 3.198432 3.325439 3.441458
-## 3     118 6 0.03833089 3.263995 3.102370 3.204886 3.333641 3.441232
-## 4     117 7 0.03264826 3.273661 3.105529 3.215598 3.331685 3.443935
-## 5     116 7 0.03264826 3.281556 3.101302 3.220882 3.343858 3.477011
-## 6     115 7 0.03264826 3.288532 3.095856 3.223696 3.356467 3.478720
    +## 1 120 5 0.01723152 3.253367 3.141471 3.212180 3.293760 3.371622 +## 2 119 5 0.03555816 3.263167 3.083477 3.197442 3.324438 3.440447 +## 3 118 6 0.03833089 3.262952 3.101351 3.203860 3.332595 3.440163 +## 4 117 7 0.03264826 3.272569 3.104476 3.214511 3.330587 3.442792 +## 5 116 7 0.03264826 3.280423 3.100220 3.219765 3.342726 3.475877 +## 6 115 7 0.03264826 3.287359 3.094699 3.222523 3.355278 3.477518 
    ## The trend model with some graphical options
 plot(sim_trend, xlab = "Time (Mya)", ylab = "sum of variances",
     col = c("#F65205", "#F38336", "#F7B27E"))
@@ -2555,7 +2621,7 @@ 
    4.8 Disparity as a distribution     ylab = "Distribution of all the distances")
 plot(disparity_centroids_median,
      ylab = "Distribution of the medians of all the distances")
    -

    +

    par(op)

    We can then test for differences in the resulting distributions using test.dispRity and the bhatt.coeff test as described above.

    ## Probability of overlap in the distribution of medians
@@ -2668,7 +2734,7 @@ 
    4.9 Disparity from other matrices
 plot(euro_ord_disp, rarefaction = 9, main = "Ordinated differences (rarefied)")
 ## Adding the p-value
 text(1.5, 1400, paste0("p=",round(euro_ord_diff_rar[[2]][[1]], digit = 5) ))
    -

    +

    As expected, the results are pretty similar in pattern but different in terms of scale. The median centroids distance is expressed in km in the “Distance differences” plots and in Euclidean units of variation in the “Ordinated differences” plots.

    @@ -2761,7 +2827,7 @@

    4.10 Disparity from multiple matr abline(v = tree$root.time - slices) } silent <- lapply(trees, fun.plot)

    -

    +

    Note that in this example, the nodes are actually even different in each tree! The node n4 for example, is not direct descendent of t4 and t6 in all trees! To fix that, it is possible to input a list of trees and a list of matrices that correspond to each tree in chrono.subsets by using the bind.data = TRUE option. In this case, the matrices need to all have the same row names and the trees all need the same labels as before:

    @@ -2781,7 +2847,7 @@

    4.10 Disparity from multiple matr ## Measuring disparity as the sum of variances and summarising it summary(dispRity(bound_data, metric = c(sum, variances)))

    ##   subsets  n obs.median  2.5%   25%   75% 97.5%
-## 1     7.9  3      0.079 0.076 0.077 0.272 0.447
+## 1     7.9  3      0.079 0.076 0.077 0.273 0.447
 ## 2    3.95  5      1.790 0.354 1.034 2.348 2.850
 ## 3       0 10      3.320 3.044 3.175 3.381 3.435
    summary(dispRity(unbound_data, metric = c(sum, variances)))
    @@ -2792,6 +2858,8 @@

    4.10 Disparity from multiple matr

    Note here that the results are again rather different: with the bound data, the slices are done across the three trees and each of their corresponding matrix (resulting in three observation) which is more accurate than the previous results from three_trees above. With the unbound data, the slices are done across the three trees and applied to the three matrices (resulting in 9 observations). As we’ve seen before, this is incorrect in this case since the trees don’t have the same topology (so the nodes selected by a slice through the second tree are not equivalent to the nodes in the first matrix) but it can be useful if the topology is fixed to integrate both uncertainty in branch length (slicing through different trees) and uncertainty from, say, ancestral states estimations (applying the slices on different matrices).

    +

    Note that since the version 1.8 the trees and the matrices don’t have to match allowing to run disparity analyses with variable matrices and trees. +This can be useful when running ancestral states estimations from a tree distribution where not all trees have the same topology.

    4.11 Disparity with trees: dispRitree!

    @@ -2824,7 +2892,7 @@

    4.11 Disparity with trees: di ## (using the specific argument as follows) plot(time_slices, type = "preview", specific.args = list(tree = TRUE))

    -

    +

    ## Note that some nodes are never selected thus explaining the branches not reaching them.

    And we can then measure disparity as the sum of the edge length at each time slice on the bootstrapped data:

    ## Measuring the sum of the edge length per slice
@@ -2848,7 +2916,7 @@ 
    4.11 Disparity with trees: di
 ## 14    9.54 10 1391      1391 1391 1391 1391  1391
 ## 15       0 10 1391      1391 1391 1391 1391  1391
    plot(sum_edge_length)
    -

    +

    Of course this can be done with multiple trees and be combined with an approach using multiple matrices (see here)!

    @@ -2908,7 +2976,7 @@

    4.12.2 Visualising covar objects< ## A bit of everything covar.plot(my_covar, col = c("orange", "darkgreen", "blue", "grey"), main = "Ten random VCV matrices", points = TRUE, major.axes = TRUE, points.cex = 1/3, n = 10, ellipses = TRUE, legend = TRUE)

    -

    +

    4.12.3 Disparity analyses with a $covar component

    @@ -2952,7 +3020,7 @@

    4.12.3 Disparity analyses with a

    -

    References

    +

    References

    Aguilera, Antonio, and Ricardo Pérez-Aguila. 2004. “General N-Dimensional Rotations.” http://wscg.zcu.cz/wscg2004/Papers_2004_Short/N29.pdf.

    @@ -3066,7 +3134,7 @@

    References 0.5\) describes a pancake where the major axis distribution is convex (values close to 1 can be pictured in 3D as a cr`\{e\}pes with the first two axis being rather big - a circle - and the third axis being particularly thin; values closer to \(0.5\) can be pictured as flying saucers). Conversely, a value \(< 0.5\) describes a cigar where the major axis distribution is concave (values close to 0 can be pictured in 3D as a spaghetti with the first axis rather big and the two next ones being small; values closer to \(0.5\) can be pictured in 3D as a fat cigar). + +This is what it looks for example for three simulated variance-covariance matrices in 3D: + +\begin{verbatim} +## Warning in snapshot3d(scene = x, width = width, height = height): webshot = +## TRUE requires the webshot2 package and Chrome browser; using rgl.snapshot() +## instead +\end{verbatim} + +\begin{verbatim} +## Warning in rgl.snapshot(filename, fmt, top): this build of rgl does not support +## snapshots +\end{verbatim} + +\includegraphics[width=4in]{../../../../../../tmp/RtmpuRA2JU/file80cb6a29f05b} + +\begin{verbatim} +## Warning in snapshot3d(scene = x, width = width, height = height): webshot = +## TRUE requires the webshot2 package and Chrome browser; using rgl.snapshot() +## instead +\end{verbatim} + +\begin{verbatim} +## Warning in rgl.snapshot(filename, fmt, top): this build of rgl does not support +## snapshots +\end{verbatim} + +\includegraphics[width=4in]{../../../../../../tmp/RtmpuRA2JU/file80cb29a4e334} + +\begin{verbatim} +## Warning in snapshot3d(scene = x, width = width, height = height): webshot = +## TRUE requires the webshot2 package and Chrome browser; using rgl.snapshot() +## instead \end{verbatim} +\begin{verbatim} +## Warning in rgl.snapshot(filename, fmt, top): this build of rgl does not support +## snapshots +\end{verbatim} + +\includegraphics[width=4in]{../../../../../../tmp/RtmpuRA2JU/file80cb4a93cfcb} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-69-1.pdf} + \hypertarget{betweengroupmetricslist}{% \subsubsection{Between group metrics}\label{betweengroupmetricslist}} @@ -2982,7 +3044,7 @@ \subsubsection{Between group metrics}\label{betweengroupmetricslist}} \begin{verbatim} ## subsets n_1 n_2 obs.median 2.5% 25% 75% 97.5% ## 1 gulls:phylogeny 159 359 8.25 2.101 6.25 14.98 41.8 -## 2 plovers:phylogeny 98 359 33.75 5.700 16.33 83.04 130.4 +## 2 plovers:phylogeny 98 359 33.75 5.700 16.33 75.50 131.5 ## 3 sandpipers:phylogeny 102 359 10.79 3.876 8.10 16.59 95.9 \end{verbatim} @@ -3003,10 +3065,10 @@ \subsubsection{Between group metrics}\label{betweengroupmetricslist}} \end{Shaded} \begin{verbatim} -## subsets n_1 n_2 obs.median 2.5% 25% 75% 97.5% -## 1 gulls:phylogeny 159 359 0.002 0 0.001 0.002 0.007 -## 2 plovers:phylogeny 98 359 0.000 0 0.000 0.001 0.003 -## 3 sandpipers:phylogeny 102 359 0.001 0 0.000 0.001 0.005 +## subsets n_1 n_2 obs.median 2.5% 25% 75% 97.5% +## 1 gulls:phylogeny 159 359 0.003 0.001 0.002 0.005 0.015 +## 2 plovers:phylogeny 98 359 0.001 0.000 0.001 0.001 0.006 +## 3 sandpipers:phylogeny 102 359 0.002 0.000 0.001 0.003 0.009 \end{verbatim} \hypertarget{which-disparity-metric-to-choose}{% @@ -3036,7 +3098,7 @@ \subsubsection{\texorpdfstring{\texttt{test.metric}}{test.metric}}\label{test-me \begin{Shaded} \begin{Highlighting}[] -\CommentTok{\# Creating a 2D uniform space} +\CommentTok{\#\# Creating a 2D uniform space} \NormalTok{example\_space \textless{}{-}}\StringTok{ }\KeywordTok{space.maker}\NormalTok{(}\DecValTok{300}\NormalTok{, }\DecValTok{2}\NormalTok{, runif)} \CommentTok{\#\# Testing the product of ranges metric on the example space} @@ -3076,14 +3138,14 @@ \subsubsection{\texorpdfstring{\texttt{test.metric}}{test.metric}}\label{test-me \end{Shaded} \begin{verbatim} -## 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% slope -## random 0.84 0.88 0.94 0.95 0.96 0.98 0.97 0.98 0.96 0.98 1.450100e-03 -## size.inner 0.10 0.21 0.31 0.45 0.54 0.70 0.78 0.94 0.96 0.98 1.054925e-02 -## size.outer 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 1.453782e-05 -## p_value R^2(adj) -## random 2.439179e-06 0.5377136 -## size.inner 4.450564e-25 0.9783976 -## size.outer 1.925262e-05 0.4664502 +## 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% slope +## random 0.84 0.88 0.94 0.95 0.96 0.98 0.97 0.98 0.96 0.98 1.450100e-03 +## size.increase 0.10 0.21 0.31 0.45 0.54 0.70 0.78 0.94 0.96 0.98 1.054925e-02 +## size.hollowness 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 1.453782e-05 +## p_value R^2(adj) +## random 2.439179e-06 0.5377136 +## size.increase 4.450564e-25 0.9783976 +## size.hollowness 1.925262e-05 0.4664502 \end{verbatim} \begin{Shaded} @@ -3093,7 +3155,7 @@ \subsubsection{\texorpdfstring{\texttt{test.metric}}{test.metric}}\label{test-me \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-72-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-77-1.pdf} \hypertarget{summarising-disprity-data-plots}{% \section{Summarising dispRity data (plots)}\label{summarising-disprity-data-plots}} @@ -3293,7 +3355,7 @@ \subsection{\texorpdfstring{Plotting \texttt{dispRity} data}{Plotting dispRity d \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-78-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-83-1.pdf} \begin{Shaded} \begin{Highlighting}[] @@ -3322,7 +3384,7 @@ \subsection{\texorpdfstring{Plotting \texttt{dispRity} data}{Plotting dispRity d \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-79-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-84-1.pdf} \begin{Shaded} \begin{Highlighting}[] @@ -3350,7 +3412,7 @@ \subsection{\texorpdfstring{Plotting \texttt{dispRity} data}{Plotting dispRity d \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-80-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-85-1.pdf} \begin{Shaded} \begin{Highlighting}[] @@ -3378,7 +3440,7 @@ \subsection{\texorpdfstring{Plotting \texttt{dispRity} data}{Plotting dispRity d \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-81-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-86-1.pdf} \begin{Shaded} \begin{Highlighting}[] @@ -3399,7 +3461,7 @@ \subsection{\texorpdfstring{Plotting \texttt{dispRity} data}{Plotting dispRity d \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-82-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-87-1.pdf} \begin{Shaded} \begin{Highlighting}[] @@ -3460,7 +3522,7 @@ \subsection{\texorpdfstring{\texttt{type\ =\ preview}}{type = preview}}\label{ty \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-83-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-88-1.pdf} \begin{quote} DISCLAIMER: This functionality can be handy for exploring the data (e.g.~to visually check whether the subset attribution worked) but it might be misleading on how the data is \emph{actually} distributed in the multidimensional space! @@ -3481,7 +3543,7 @@ \subsection{\texorpdfstring{\texttt{type\ =\ preview}}{type = preview}}\label{ty \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-84-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-89-1.pdf} \hypertarget{graphical-options-with-...}{% \subsection{\texorpdfstring{Graphical options with \texttt{...}}{Graphical options with ...}}\label{graphical-options-with-...}} @@ -3528,7 +3590,7 @@ \subsection{\texorpdfstring{Graphical options with \texttt{...}}{Graphical optio \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-85-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-90-1.pdf} \hypertarget{testing-disparity-hypotheses}{% \section{Testing disparity hypotheses}\label{testing-disparity-hypotheses}} @@ -3870,7 +3932,7 @@ \subsection{\texorpdfstring{\texttt{geiger::dtt} model fitting in \texttt{dispRi \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-91-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-96-1.pdf} Note that, like in the original \texttt{dtt} function, it is possible to change the evolutionary model (see \texttt{?geiger::sim.char} documentation). @@ -3894,8 +3956,8 @@ \subsection{\texorpdfstring{null morphospace testing with \texttt{null.test}}{nu \end{Shaded} \begin{verbatim} -## Warning in check.dispRity.data(data): Row names have been automatically added to -## data. +## Warning in check.data(data, match_call): Row names have been automatically +## added to data. \end{verbatim} \begin{Shaded} @@ -3932,7 +3994,7 @@ \subsection{\texorpdfstring{null morphospace testing with \texttt{null.test}}{nu \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-93-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-98-1.pdf} For more details on generating spaces see the \protect\hyperlink{Simulating-multidimensional-spaces}{\texttt{space.maker}} function tutorial. @@ -4138,7 +4200,7 @@ \subsubsection{\texorpdfstring{\texttt{model.test}}{model.test}}\label{model.tes ## Variance is not pooled. ## Running Stasis model...Done. Log-likelihood = -18.694 ## Running BM model...Done. Log-likelihood = 149.289 -## Running OU model...Done. Log-likelihood = 152.118 +## Running OU model...Done. Log-likelihood = 152.119 ## Running Trend model...Done. Log-likelihood = 152.116 ## Running EB model...Done. Log-likelihood = 126.268 \end{verbatim} @@ -4153,13 +4215,13 @@ \subsubsection{\texorpdfstring{\texttt{model.test}}{model.test}}\label{model.tes ## aicc delta_aicc weight_aicc log.lik param theta.1 omega ancestral state ## Stasis 41 339.5 0.000 -18.7 2 3.629 0.074 NA ## BM -294 3.6 0.112 149.3 2 NA NA 3.267 -## OU -296 2.1 0.227 152.1 4 NA NA 3.255 +## OU -296 2.1 0.227 152.1 4 NA NA 3.254 ## Trend -298 0.0 0.661 152.1 3 NA NA 3.255 ## EB -246 51.7 0.000 126.3 3 NA NA 4.092 ## sigma squared alpha optima.1 trend eb ## Stasis NA NA NA NA NA ## BM 0.001 NA NA NA NA -## OU 0.001 0.001 10.89 NA NA +## OU 0.001 0.001 12.35 NA NA ## Trend 0.001 NA NA 0.007 NA ## EB 0.000 NA NA NA -0.032 \end{verbatim} @@ -4230,7 +4292,7 @@ \subsubsection{\texorpdfstring{\texttt{model.test.wrapper}}{model.test.wrapper}} ## Variance is not pooled. ## Running Stasis model...Done. Log-likelihood = -18.694 ## Running BM model...Done. Log-likelihood = 149.289 -## Running OU model...Done. Log-likelihood = 152.118 +## Running OU model...Done. Log-likelihood = 152.119 ## Running Trend model...Done. Log-likelihood = 152.116 ## Running EB model...Done. Log-likelihood = 126.268 \end{verbatim} @@ -4253,19 +4315,19 @@ \subsubsection{\texorpdfstring{\texttt{model.test.wrapper}}{model.test.wrapper}} \begin{verbatim} ## aicc delta_aicc weight_aicc log.lik param theta.1 omega ancestral state ## Trend -298 0.0 0.661 152.1 3 NA NA 3.255 -## OU -296 2.1 0.227 152.1 4 NA NA 3.255 +## OU -296 2.1 0.227 152.1 4 NA NA 3.254 ## BM -294 3.6 0.112 149.3 2 NA NA 3.267 ## EB -246 51.7 0.000 126.3 3 NA NA 4.092 ## Stasis 41 339.5 0.000 -18.7 2 3.629 0.074 NA ## sigma squared alpha optima.1 trend eb median p value lower p value -## Trend 0.001 NA NA 0.007 NA 0.97752248 0.977022977 -## OU 0.001 0.001 10.89 NA NA 0.97302697 0.972027972 -## BM 0.001 NA NA NA NA 0.16283716 0.137862138 -## EB 0.000 NA NA NA -0.032 0.06893107 0.000999001 -## Stasis NA NA NA NA NA 1.00000000 1.000000000 +## Trend 0.001 NA NA 0.007 NA 0.978021978 0.9760240 +## OU 0.001 0.001 12.35 NA NA 0.978021978 0.9770230 +## BM 0.001 NA NA NA NA 0.143856144 0.1368631 +## EB 0.000 NA NA NA -0.032 0.000999001 0.0000000 +## Stasis NA NA NA NA NA 1.000000000 0.9990010 ## upper p value ## Trend 0.9780220 -## OU 0.9740260 +## OU 0.9780220 ## BM 0.1878122 ## EB 0.1368631 ## Stasis 1.0000000 @@ -4275,7 +4337,7 @@ \subsubsection{\texorpdfstring{\texttt{model.test.wrapper}}{model.test.wrapper}} There is no significant differences between the empirical data and simulated data, except for the Early Burst model. Trend is the best-fitting model but the plot suggests the OU model also follows a trend-like pattern. -This is because the optima for the OU model (10.89) is different to the ancestral state (3.255) and outside the observed value. +This is because the optima for the OU model (12.35) is different to the ancestral state (3.254) and outside the observed value. This is potentially unrealistic, and one way to alleviate this issue is to set the optima of the OU model to equal the ancestral estimate - this is the normal practice for OU models in comparative phylogenetics. To set the optima to the ancestral value we change the argument \texttt{fixed.optima\ =\ TRUE}: @@ -4320,11 +4382,11 @@ \subsubsection{\texorpdfstring{\texttt{model.test.wrapper}}{model.test.wrapper}} ## EB -246 51.7 0.000 126.3 3 NA NA 4.092 ## Stasis 41 339.5 0.000 -18.7 2 3.629 0.074 NA ## sigma squared alpha trend eb median p value lower p value -## Trend 0.001 NA 0.007 NA 0.98351648 0.983016983 -## BM 0.001 NA NA NA 0.26473526 0.249750250 -## OU 0.001 0 NA NA 0.30469530 0.292707293 -## EB 0.000 NA NA -0.032 0.06943057 0.000999001 -## Stasis NA NA NA NA 0.99900100 0.999000999 +## Trend 0.001 NA 0.007 NA 0.984015984 0.9820180 +## BM 0.001 NA NA NA 0.256743257 0.2487512 +## OU 0.001 0 NA NA 0.293706294 0.2917083 +## EB 0.000 NA NA -0.032 0.000999001 0.0000000 +## Stasis NA NA NA NA 0.999000999 0.9980020 ## upper p value ## Trend 0.9840160 ## BM 0.2797203 @@ -4366,7 +4428,7 @@ \subsection{Multiple modes of evolution (time shifts)}\label{multiple-modes-of-e ## Running BM model...Done. Log-likelihood = 149.289 ## Running Trend model...Done. Log-likelihood = 152.116 ## Running OU model...Done. Log-likelihood = 149.289 -## Running multi.OU model...Done. Log-likelihood = 152.124 +## Running multi.OU model...Done. Log-likelihood = 151.958 \end{verbatim} \begin{figure} @@ -4386,18 +4448,18 @@ \subsection{Multiple modes of evolution (time shifts)}\label{multiple-modes-of-e \begin{verbatim} ## aicc delta_aicc weight_aicc log.lik param ancestral state -## Trend -298 0.000 0.635 152.1 3 3.255 -## multi.OU -296 2.124 0.220 152.1 4 3.254 -## BM -294 3.550 0.108 149.3 2 3.267 -## OU -292 5.654 0.038 149.3 3 3.267 +## Trend -298 0.000 0.657 152.1 3 3.255 +## multi.OU -296 2.456 0.193 152.0 4 3.253 +## BM -294 3.550 0.111 149.3 2 3.267 +## OU -292 5.654 0.039 149.3 3 3.267 ## sigma squared trend alpha optima.2 median p value lower p value -## Trend 0.001 0.007 NA NA 0.9870130 0.9870130 -## multi.OU 0.001 NA 0.001 10.63 0.9690310 0.9680320 -## BM 0.001 NA NA NA 0.2012987 0.1818182 -## OU 0.001 NA 0.000 NA 0.2867133 0.2717283 +## Trend 0.001 0.007 NA NA 0.9870130 0.9860140 +## multi.OU 0.001 NA 0.006 4.686 0.9570430 0.9560440 +## BM 0.001 NA NA NA 0.1868132 0.1808192 +## OU 0.001 NA 0.000 NA 0.2727273 0.2707293 ## upper p value ## Trend 0.9870130 -## multi.OU 0.9700300 +## multi.OU 0.9590410 ## BM 0.2207792 ## OU 0.3016983 \end{verbatim} @@ -4474,17 +4536,17 @@ \subsection{Multiple modes of evolution (time shifts)}\label{multiple-modes-of-e ## Stasis:OU -240 47.7 0.000 125.1 5 NA ## BM:Stasis -130 157.1 0.000 69.3 4 3.268 ## sigma squared alpha optima.1 theta.1 omega trend median p value -## OU:Trend 0.001 0.041 NA NA NA 0.011 0.3246753 -## BM:OU 0.001 0.000 4.092 NA NA NA 0.5009990 +## OU:Trend 0.001 0.041 NA NA NA 0.011 0.2987013 +## BM:OU 0.001 0.000 4.092 NA NA NA 0.4925075 ## Stasis:BM 0.002 NA NA 3.390 0.004 NA 0.9970030 ## Stasis:OU 0.002 0.000 4.092 3.390 0.004 NA 1.0000000 ## BM:Stasis 0.000 NA NA 3.806 0.058 NA 1.0000000 ## lower p value upper p value -## OU:Trend 0.2957043 0.3536464 -## BM:OU 0.4885115 0.5134865 -## Stasis:BM 0.9970030 0.9970030 -## Stasis:OU 1.0000000 1.0000000 -## BM:Stasis 1.0000000 1.0000000 +## OU:Trend 0.2947053 0.3536464 +## BM:OU 0.4875125 0.5134865 +## Stasis:BM 0.9960040 0.9970030 +## Stasis:OU 0.9990010 1.0000000 +## BM:Stasis 0.9990010 1.0000000 \end{verbatim} \hypertarget{model.test.sim}{% @@ -4581,7 +4643,7 @@ \subsubsection{Simulating tested models}\label{simulating-tested-models}} ## Variance is not pooled. ## Running Stasis model...Done. Log-likelihood = -18.694 ## Running BM model...Done. Log-likelihood = 149.289 -## Running OU model...Done. Log-likelihood = 152.118 +## Running OU model...Done. Log-likelihood = 152.119 ## Running Trend model...Done. Log-likelihood = 152.116 ## Running EB model...Done. Log-likelihood = 126.268 \end{verbatim} @@ -4596,13 +4658,13 @@ \subsubsection{Simulating tested models}\label{simulating-tested-models}} ## aicc delta_aicc weight_aicc log.lik param theta.1 omega ancestral state ## Stasis 41 339.5 0.000 -18.7 2 3.629 0.074 NA ## BM -294 3.6 0.112 149.3 2 NA NA 3.267 -## OU -296 2.1 0.227 152.1 4 NA NA 3.255 +## OU -296 2.1 0.227 152.1 4 NA NA 3.254 ## Trend -298 0.0 0.661 152.1 3 NA NA 3.255 ## EB -246 51.7 0.000 126.3 3 NA NA 4.092 ## sigma squared alpha optima.1 trend eb ## Stasis NA NA NA NA NA ## BM 0.001 NA NA NA NA -## OU 0.001 0.001 10.89 NA NA +## OU 0.001 0.001 12.35 NA NA ## Trend 0.001 NA NA 0.007 NA ## EB 0.000 NA NA NA -0.032 \end{verbatim} @@ -4626,9 +4688,9 @@ \subsubsection{Simulating tested models}\label{simulating-tested-models}} ## aicc log.lik param ancestral state sigma squared trend ## Trend -298 152.1 3 3.255 0.001 0.007 ## -## Rank envelope test -## p-value of the test: 0.99001 (ties method: midrank) -## p-interval : (0.99001, 0.99001) +## Rank envelope test: +## p-value of the global test: 0.99001 (ties method: erl) +## p-interval : (0.989011, 0.99001) \end{verbatim} By default, the model simulated is the one with the lowest AICc (\texttt{model.rank\ =\ 1}) but it is possible to choose any ranked model, for example, the OU (second one): @@ -4648,11 +4710,11 @@ \subsubsection{Simulating tested models}\label{simulating-tested-models}} ## ## Model simulated (1000 times): ## aicc log.lik param ancestral state sigma squared alpha optima.1 -## OU -296 152.1 4 3.255 0.001 0.001 10.89 +## OU -296 152.1 4 3.254 0.001 0.001 12.35 ## -## Rank envelope test -## p-value of the test: 0.9895105 (ties method: midrank) -## p-interval : (0.989011, 0.99001) +## Rank envelope test: +## p-value of the global test: 0.992008 (ties method: erl) +## p-interval : (0.99001, 0.992008) \end{verbatim} And as the example above, the simulated data can be plotted or summarised: @@ -4681,12 +4743,12 @@ \subsubsection{Simulating tested models}\label{simulating-tested-models}} \begin{verbatim} ## subsets n var median 2.5% 25% 75% 97.5% -## 1 120 5 0.01723152 3.254315 3.142419 3.213128 3.294708 3.372570 -## 2 119 5 0.03555816 3.264164 3.084458 3.198432 3.325439 3.441458 -## 3 118 6 0.03833089 3.263995 3.102370 3.204886 3.333641 3.441232 -## 4 117 7 0.03264826 3.273661 3.105529 3.215598 3.331685 3.443935 -## 5 116 7 0.03264826 3.281556 3.101302 3.220882 3.343858 3.477011 -## 6 115 7 0.03264826 3.288532 3.095856 3.223696 3.356467 3.478720 +## 1 120 5 0.01723152 3.253367 3.141471 3.212180 3.293760 3.371622 +## 2 119 5 0.03555816 3.263167 3.083477 3.197442 3.324438 3.440447 +## 3 118 6 0.03833089 3.262952 3.101351 3.203860 3.332595 3.440163 +## 4 117 7 0.03264826 3.272569 3.104476 3.214511 3.330587 3.442792 +## 5 116 7 0.03264826 3.280423 3.100220 3.219765 3.342726 3.475877 +## 6 115 7 0.03264826 3.287359 3.094699 3.222523 3.355278 3.477518 \end{verbatim} \begin{Shaded} @@ -4790,7 +4852,7 @@ \section{Disparity as a distribution}\label{disparity-distribution}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-111-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-116-1.pdf} \begin{Shaded} \begin{Highlighting}[] @@ -4974,7 +5036,7 @@ \section{Disparity from other matrices}\label{other-matrices}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-117-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-122-1.pdf} As expected, the results are pretty similar in pattern but different in terms of scale. The median centroids distance is expressed in km in the ``Distance differences'' plots and in Euclidean units of variation in the ``Ordinated differences'' plots. @@ -5133,7 +5195,7 @@ \section{Disparity from multiple matrices (and multiple trees!)}\label{multi.inp \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-122-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-127-1.pdf} Note that in this example, the nodes are actually even different in each tree! The node \texttt{n4} for example, is not direct descendent of \texttt{t4} and \texttt{t6} in all trees! To fix that, it is possible to input a list of trees and a list of matrices that correspond to each tree in \texttt{chrono.subsets} by using the \texttt{bind.data\ =\ TRUE} option. @@ -5161,7 +5223,7 @@ \section{Disparity from multiple matrices (and multiple trees!)}\label{multi.inp \begin{verbatim} ## subsets n obs.median 2.5% 25% 75% 97.5% -## 1 7.9 3 0.079 0.076 0.077 0.272 0.447 +## 1 7.9 3 0.079 0.076 0.077 0.273 0.447 ## 2 3.95 5 1.790 0.354 1.034 2.348 2.850 ## 3 0 10 3.320 3.044 3.175 3.381 3.435 \end{verbatim} @@ -5183,6 +5245,9 @@ \section{Disparity from multiple matrices (and multiple trees!)}\label{multi.inp With the unbound data, the slices are done across the three trees and applied to the three matrices (resulting in 9 observations). As we've seen before, this is incorrect in this case since the trees don't have the same topology (so the nodes selected by a slice through the second tree are not equivalent to the nodes in the first matrix) but it can be useful if the topology is fixed to integrate both uncertainty in branch length (slicing through different trees) and uncertainty from, say, ancestral states estimations (applying the slices on different matrices). +Note that since the version \texttt{1.8} the trees and the matrices don't have to match allowing to run disparity analyses with variable matrices and trees. +This can be useful when running ancestral states estimations from a tree distribution where not all trees have the same topology. + \hypertarget{dispRitree}{% \section{\texorpdfstring{Disparity with trees: \emph{dispRitree!}}{Disparity with trees: dispRitree!}}\label{dispRitree}} @@ -5223,7 +5288,7 @@ \section{\texorpdfstring{Disparity with trees: \emph{dispRitree!}}{Disparity wit \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-124-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-129-1.pdf} \begin{Shaded} \begin{Highlighting}[] @@ -5267,7 +5332,7 @@ \section{\texorpdfstring{Disparity with trees: \emph{dispRitree!}}{Disparity wit \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-125-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-130-1.pdf} Of course this can be done with multiple trees and be combined with an approach using multiple matrices (see \protect\hyperlink{multi.input}{here})! @@ -5358,7 +5423,7 @@ \subsection{Visualising covar objects}\label{visualising-covar-objects}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-129-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-134-1.pdf} \hypertarget{disparity-analyses-with-a-covar-component}{% \subsection{\texorpdfstring{Disparity analyses with a \texttt{\$covar} component}{Disparity analyses with a \$covar component}}\label{disparity-analyses-with-a-covar-component}} @@ -5478,11 +5543,11 @@ \section{Simulating discrete morphological data}\label{simulating-discrete-morph \begin{verbatim} ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] -## t10 "1" "0" "1" "1" "1" "0" "0" "1" "0" "1" -## t1 "0" "0" "1" "1" "0" "0" "0" "1" "0" "1" -## t9 "0" "0" "1" "1" "0" "0" "0" "1" "0" "1" -## t14 "1" "0" "1" "1" "0" "0" "0" "1" "0" "1" -## t13 "1" "0" "1" "1" "0" "0" "0" "1" "0" "1" +## t10 "1" "0" "1" "0" "1" "0" "0" "1" "0" "0" +## t1 "0" "0" "1" "0" "0" "0" "0" "1" "0" "0" +## t9 "0" "0" "1" "0" "0" "0" "0" "1" "0" "0" +## t14 "1" "0" "1" "0" "0" "0" "0" "1" "0" "0" +## t13 "1" "0" "1" "0" "0" "0" "0" "1" "0" "0" \end{verbatim} \begin{Shaded} @@ -5494,9 +5559,9 @@ \section{Simulating discrete morphological data}\label{simulating-discrete-morph \begin{verbatim} ## -## Maximum parsimony 143.0000000 -## Consistency index 0.7482517 -## Retention index 0.9168591 +## Maximum parsimony 144.0000000 +## Consistency index 0.7430556 +## Retention index 0.9160998 ## Robinson-Foulds distance 2.0000000 \end{verbatim} @@ -5597,16 +5662,16 @@ \subsubsection{Adding inapplicable characters}\label{adding-inapplicable-charact \begin{verbatim} ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] -## t10 "1" "0" "1" "1" "1" "0" "0" "0" "1" "0" "1" -## t1 "1" "0" "0" "0" "1" "0" "0" "0" "0" "0" "1" -## t9 "1" "2" "1" "0" "1" "0" "0" "0" "0" "0" "1" -## t14 "1" "0" "0" "0" "1" "0" "0" "0" "0" "0" "1" -## t13 "1" "0" "0" "0" "1" "0" "0" "0" "0" "0" "1" -## t5 "1" "0" "0" "0" "1" "0" "0" "0" "0" "0" "1" -## t2 "1" "2" "-" "0" "1" "0" "0" "0" "0" "0" "1" -## t8 "1" "1" "-" "0" "1" "0" "0" "0" "0" "0" "1" -## t6 "1" "2" "1" "0" "0" "1" "1" "2" "0" "1" "0" -## t15 "1" "2" "1" "0" "0" "1" "1" "2" "0" "1" "0" +## t10 "-" "1" "1" "2" "1" "0" "0" "0" "1" "0" "0" +## t1 "-" "1" "0" "0" "1" "0" "0" "0" "-" "0" "0" +## t9 "-" "1" "1" "0" "1" "0" "0" "0" "-" "0" "0" +## t14 "-" "1" "0" "0" "1" "0" "0" "0" "-" "0" "0" +## t13 "-" "1" "0" "0" "1" "0" "0" "0" "-" "0" "0" +## t5 "-" "1" "0" "0" "1" "0" "0" "0" "-" "0" "0" +## t2 "1" "1" "0" "0" "1" "0" "0" "0" "0" "0" "0" +## t8 "2" "1" "0" "0" "1" "0" "0" "0" "0" "0" "0" +## t6 "-" "1" "1" "0" "0" "1" "1" "2" "0" "1" "1" +## t15 "-" "1" "1" "0" "0" "1" "1" "2" "0" "1" "1" \end{verbatim} \hypertarget{parameters-for-a-realisticish-matrix}{% @@ -5687,7 +5752,7 @@ \section{Simulating multidimensional spaces}\label{simulating-multidimensional-s \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-136-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-141-1.pdf} Of course, more complex spaces can be created by changing the distributions, their arguments or adding a correlation matrix or a cumulative variance vector: @@ -5744,7 +5809,7 @@ \subsection{Personalised dimensions distributions}\label{personalised-dimensions \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-138-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-143-1.pdf} \begin{Shaded} \begin{Highlighting}[] @@ -5794,7 +5859,7 @@ \subsection{Visualising the space}\label{visualising-the-space}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-139-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-144-1.pdf} \begin{Shaded} \begin{Highlighting}[] @@ -5844,7 +5909,7 @@ \subsection{Generating realistic spaces}\label{generating-realistic-spaces}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-140-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-145-1.pdf} It is now possible to simulate a space using these observed arguments to test several hypothesis: @@ -5887,7 +5952,7 @@ \subsection{Generating realistic spaces}\label{generating-realistic-spaces}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-141-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-146-1.pdf} If we measure disparity as the median distance from the morphospace centroid, we can explain the distribution of the data as normal with the variable observed mean and standard deviation and with a correlation between the dimensions. @@ -5950,7 +6015,7 @@ \section{\texorpdfstring{\texttt{char.diff}}{char.diff}}\label{char.diff}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-144-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-149-1.pdf} You can check all the numerous plotting options in the \texttt{?plot.char.diff} manual (it won't be developed here). @@ -6253,7 +6318,7 @@ \section{\texorpdfstring{\texttt{match.tip.edge}}{match.tip.edge}}\label{match.t \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-154-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-159-1.pdf} But you can also use this option to only select some specific edges and modify them (for example making them all equal to one): @@ -6268,14 +6333,14 @@ \section{\texorpdfstring{\texttt{match.tip.edge}}{match.tip.edge}}\label{match.t \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-155-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-160-1.pdf} \hypertarget{MCMCglmm-utilities}{% \section{\texorpdfstring{\texttt{MCMCglmm} utilities}{MCMCglmm utilities}}\label{MCMCglmm-utilities}} Since version \texttt{1.7}, the \texttt{dispRity} package contains several utility functions for manipulating \texttt{"MCMCglmm"} (that is, objects returned by the function \texttt{MCMCglmm::MCMCglmm}). These objects are a modification of the \texttt{mcmc} object (from the package \texttt{coda}) and can be sometimes cumbersome to manipulate because of the huge amount of data in it. -You can use the functions \texttt{MCMCglmm.traits} for extracting the number of traits, \texttt{MCMCglmm.levels} for extracting the level names, \texttt{MCMCglmm.sample} for sampling posterior IDs and \texttt{MCMCglmm.covars} for extracting variance-covariance matrices. +You can use the functions \texttt{MCMCglmm.traits} for extracting the number of traits, \texttt{MCMCglmm.levels} for extracting the level names, \texttt{MCMCglmm.sample} for sampling posterior IDs and \texttt{MCMCglmm.covars} for extracting variance-covariance matrices. You can also quickly calculate the variance (or relative variance) for each terms in the model using \texttt{MCMCglmm.variance} (the variance is calculated as the sum of the diagonal of each variance-covariance matrix for each term). \begin{Shaded} \begin{Highlighting}[] @@ -6336,11 +6401,18 @@ \section{\texorpdfstring{\texttt{MCMCglmm} utilities}{MCMCglmm utilities}}\label \begin{Shaded} \begin{Highlighting}[] -\CommentTok{\#\# Extracting these two random amples} +\CommentTok{\#\# Extracting these two random samples} \NormalTok{my\_covars \textless{}{-}}\StringTok{ }\KeywordTok{MCMCglmm.covars}\NormalTok{(my\_MCMCglmm, }\DataTypeTok{sample =}\NormalTok{ random\_samples)} + +\CommentTok{\#\# Plotting the variance for each term in the model} +\KeywordTok{boxplot}\NormalTok{(}\KeywordTok{MCMCglmm.variance}\NormalTok{(my\_MCMCglmm), }\DataTypeTok{horizontal =} \OtherTok{TRUE}\NormalTok{, }\DataTypeTok{las =} \DecValTok{1}\NormalTok{,} + \DataTypeTok{xlab =} \StringTok{"Relative variance"}\NormalTok{,} + \DataTypeTok{main =} \StringTok{"Variance explained by each term"}\NormalTok{)} \end{Highlighting} \end{Shaded} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-161-1.pdf} + See more in the \protect\hyperlink{covar}{\texttt{\$covar} section} on what to do with these \texttt{"MCMCglmm"} objects. \hypertarget{pair.plot}{% @@ -6360,7 +6432,7 @@ \section{\texorpdfstring{\texttt{pair.plot}}{pair.plot}}\label{pair.plot}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-157-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-162-1.pdf} Here blue squares are ones that have a high value and orange ones the ones that have low values. Note that the values plotted correspond the first column of the data as designated by \texttt{what\ =\ 1}. @@ -6379,7 +6451,7 @@ \section{\texorpdfstring{\texttt{pair.plot}}{pair.plot}}\label{pair.plot}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-158-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-163-1.pdf} This function can also be used as a binary display when running a series of pairwise t-tests. For example, the following script runs a wilcoxon test between the time-slices from the \texttt{disparity} example dataset and displays in black which pairs of slices have a p-value below 0.05: @@ -6397,7 +6469,7 @@ \section{\texorpdfstring{\texttt{pair.plot}}{pair.plot}}\label{pair.plot}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-159-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-164-1.pdf} \hypertarget{reduce.matrix}{% \section{\texorpdfstring{\texttt{reduce.matrix}}{reduce.matrix}}\label{reduce.matrix}} @@ -6422,7 +6494,7 @@ \section{\texorpdfstring{\texttt{reduce.matrix}}{reduce.matrix}}\label{reduce.ma \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-160-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-165-1.pdf} We can use the \texttt{reduce.matrix} to double check whether any rows cannot be compared. The functions needs as an input the type of distance that will be used, say a \texttt{"gower"} distance: @@ -6451,7 +6523,7 @@ \section{\texorpdfstring{\texttt{reduce.matrix}}{reduce.matrix}}\label{reduce.ma \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-162-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-167-1.pdf} \hypertarget{select.axes}{% \section{\texorpdfstring{\texttt{select.axes}}{select.axes}}\label{select.axes}} @@ -6541,7 +6613,7 @@ \section{\texorpdfstring{\texttt{select.axes}}{select.axes}}\label{select.axes}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-165-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-170-1.pdf} \begin{Shaded} \begin{Highlighting}[] @@ -6580,7 +6652,7 @@ \section{\texorpdfstring{\texttt{select.axes}}{select.axes}}\label{select.axes}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-166-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-171-1.pdf} As you can see here, the whole space requires the three first axes to explain at least 90\% of the variance (in fact, 95\% as seen before). However, different groups have a different story! @@ -6612,7 +6684,7 @@ \section{\texorpdfstring{\texttt{select.axes}}{select.axes}}\label{select.axes}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-167-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-172-1.pdf} \begin{Shaded} \begin{Highlighting}[] @@ -6670,7 +6742,7 @@ \section{\texorpdfstring{\texttt{slice.tree}}{slice.tree}}\label{slice.tree}} \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-168-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-173-1.pdf} \hypertarget{slide.nodes-and-remove.zero.brlen}{% \section{\texorpdfstring{\texttt{slide.nodes} and \texttt{remove.zero.brlen}}{slide.nodes and remove.zero.brlen}}\label{slide.nodes-and-remove.zero.brlen}} @@ -6701,7 +6773,7 @@ \section{\texorpdfstring{\texttt{slide.nodes} and \texttt{remove.zero.brlen}}{sl \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-169-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-174-1.pdf} The \texttt{remove.zero.brlen} is a ``clever'' wrapping function that uses the \texttt{slide.nodes} function to stochastically remove zero branch lengths across a whole tree. This function will slide nodes up or down in successive postorder traversals (i.e.~going down the tree clade by clade) in order to minimise the number of nodes to slide while making sure there are no silly negative branch lengths produced! @@ -6761,7 +6833,7 @@ \section{\texorpdfstring{\texttt{slide.nodes} and \texttt{remove.zero.brlen}}{sl \end{Highlighting} \end{Shaded} -\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-170-1.pdf} +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-175-1.pdf} \hypertarget{tree.age}{% \section{\texorpdfstring{\texttt{tree.age}}{tree.age}}\label{tree.age}} @@ -6864,6 +6936,363 @@ \section{\texorpdfstring{\texttt{tree.age}}{tree.age}}\label{tree.age}} ## 19 1.369 19 \end{verbatim} +\hypertarget{multi.ace}{% +\section{\texorpdfstring{\texttt{multi.ace}}{multi.ace}}\label{multi.ace}} + +This function allows to run the \texttt{ape::ace} function (ancestral characters estimations) on multiple trees. +In it's most basic structure (e.g.~using all default arguments) this function is using a mix of \texttt{ape::ace} and \texttt{castor::asr\_mk\_model} depending on the data and the situation and is generally faster than both functions when applied to a list of trees. +However, this function provides also some more complex and modular functionalities, especially appropriate when using discrete morphological character data. + +\hypertarget{using-different-character-tokens-in-different-situations}{% +\subsection{Using different character tokens in different situations}\label{using-different-character-tokens-in-different-situations}} + +This data can be often coded in non-standard way with different character tokens having different meanings. +For example, in some datasets the token \texttt{-} can mean ``the trait is inapplicable'' but this can be also coded by the more conventional \texttt{NA} or can mean ``this trait is missing'' (often coded \texttt{?}). +This makes the meaning of specific tokens idiosyncratic to different matrices. +For example we can have the following discrete morphological matrix with all the data encoded: + +\begin{Shaded} +\begin{Highlighting}[] +\KeywordTok{set.seed}\NormalTok{(}\DecValTok{42}\NormalTok{)} +\CommentTok{\#\# A random tree with 10 tips} +\NormalTok{tree \textless{}{-}}\StringTok{ }\KeywordTok{rcoal}\NormalTok{(}\DecValTok{10}\NormalTok{)} +\CommentTok{\#\# Setting up the parameters} +\NormalTok{my\_rates =}\StringTok{ }\KeywordTok{c}\NormalTok{(rgamma, }\DataTypeTok{rate =} \DecValTok{10}\NormalTok{, }\DataTypeTok{shape =} \DecValTok{5}\NormalTok{)} + +\CommentTok{\#\# Generating a bunch of trees} +\NormalTok{multiple\_trees \textless{}{-}}\StringTok{ }\KeywordTok{rmtree}\NormalTok{(}\DecValTok{5}\NormalTok{, }\DecValTok{10}\NormalTok{)} + +\CommentTok{\#\# A random Mk matrix (10*50)} +\NormalTok{matrix\_simple \textless{}{-}}\StringTok{ }\KeywordTok{sim.morpho}\NormalTok{(tree, }\DataTypeTok{characters =} \DecValTok{50}\NormalTok{, }\DataTypeTok{model =} \StringTok{"ER"}\NormalTok{, }\DataTypeTok{rates =}\NormalTok{ my\_rates,} + \DataTypeTok{invariant =} \OtherTok{FALSE}\NormalTok{)} +\NormalTok{matrix\_simple[}\DecValTok{1}\OperatorTok{:}\DecValTok{10}\NormalTok{, }\DecValTok{1}\OperatorTok{:}\DecValTok{10}\NormalTok{]} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] +## t8 "1" "1" "1" "1" "0" "0" "0" "0" "0" "1" +## t3 "1" "1" "1" "1" "0" "0" "0" "0" "0" "1" +## t2 "1" "1" "1" "1" "0" "1" "1" "1" "0" "1" +## t1 "1" "1" "1" "1" "0" "0" "1" "1" "0" "1" +## t10 "1" "1" "1" "1" "0" "0" "1" "0" "1" "1" +## t9 "1" "1" "1" "1" "0" "0" "1" "0" "0" "1" +## t5 "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" +## t6 "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" +## t4 "0" "0" "0" "0" "1" "0" "0" "0" "1" "0" +## t7 "0" "0" "0" "0" "1" "0" "0" "0" "1" "0" +\end{verbatim} + +But of course, as mentioned above, in practice, such matrices have more nuance and can including missing characters, ambiguous characters, multi-state characters, inapplicable characters, etc\ldots{} +All these coded and defined by different authors using different tokens (or symbols). +Let's give it a go and transform this simple data to something more messy: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Modify the matrix to contain missing and special data} +\NormalTok{matrix\_complex \textless{}{-}}\StringTok{ }\NormalTok{matrix\_simple} +\CommentTok{\#\# Adding 50 random "{-}" tokens} +\NormalTok{matrix\_complex[}\KeywordTok{sample}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\KeywordTok{length}\NormalTok{(matrix\_complex), }\DecValTok{50}\NormalTok{)] \textless{}{-}}\StringTok{ "{-}"} +\CommentTok{\#\# Adding 50 random "?" tokens} +\NormalTok{matrix\_complex[}\KeywordTok{sample}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\KeywordTok{length}\NormalTok{(matrix\_complex), }\DecValTok{50}\NormalTok{)] \textless{}{-}}\StringTok{ "?"} +\CommentTok{\#\# Adding 50 random "0\%2" tokens} +\NormalTok{matrix\_complex[}\KeywordTok{sample}\NormalTok{(}\DecValTok{1}\OperatorTok{:}\KeywordTok{length}\NormalTok{(matrix\_complex), }\DecValTok{50}\NormalTok{)] \textless{}{-}}\StringTok{ "0\%2"} +\NormalTok{matrix\_complex[}\DecValTok{1}\OperatorTok{:}\DecValTok{10}\NormalTok{,}\DecValTok{1}\OperatorTok{:}\DecValTok{10}\NormalTok{]} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] +## t8 "1" "1" "1" "1" "?" "0" "0" "0" "0" "0%2" +## t3 "1" "-" "1" "1" "?" "0" "0" "0" "0" "1" +## t2 "1" "1" "1" "0%2" "0" "0%2" "1" "1" "0" "1" +## t1 "1" "1" "1" "1" "0" "0" "1" "?" "0" "1" +## t10 "1" "0%2" "1" "1" "-" "?" "0%2" "0%2" "1" "1" +## t9 "1" "1" "?" "1" "0%2" "0" "1" "0" "0" "1" +## t5 "0" "-" "?" "0" "1" "1" "1" "0" "0" "-" +## t6 "0" "-" "0" "0" "1" "1" "-" "-" "?" "0" +## t4 "?" "0" "0" "0" "1" "0" "0" "0" "1" "0" +## t7 "0" "0" "0" "0%2" "1" "0" "0" "-" "1" "-" +\end{verbatim} + +In \texttt{multi.ace} you can specify what all these tokens actually mean and how the code should interpret them. +For example, \texttt{-} often means inapplicable data (i.e.~the specimen does not have the coded feature, for example, the colour of the tail of a tailless bird); or \texttt{?} that often means missing data (i.e.~it is unknown if the specimen has a tail or not since only the head was available). +And more than the differences in meaning between these characters, different people treat these characters differently even if they have the same meaning for the token. +For example, one might want to treat \texttt{-} as meaning ``we don't know'' (which will be treated by the algorithm as ``any possible trait value'') or ``we know, and it's no possible'' (which will be treated by the algorithm as \texttt{NA}). +Because of this situation, \texttt{multi.ace} allows combining any special case marked with a special token to a special behaviour. +For example we might want to create a special case called \texttt{"missing"} (i.e.~the data is missing) that we want to denote using the token \texttt{"?"} and we can specify the algorithm to treat this \texttt{"missing"} cases (\texttt{"?"}) as treating the character token value as ``any possible values''. +This behaviour can be hard coded by providing a function with the name of the behaviour. +For example: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# The specific token for the missing cases (note the "\textbackslash{}\textbackslash{}" for protecting the value)} +\NormalTok{special.tokens \textless{}{-}}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"missing"}\NormalTok{ =}\StringTok{ "}\CharTok{\textbackslash{}\textbackslash{}}\StringTok{?"}\NormalTok{)} + +\CommentTok{\#\# The behaviour for the missing cases (?)} +\NormalTok{special.behaviour \textless{}{-}}\StringTok{ }\KeywordTok{list}\NormalTok{(missing \textless{}{-}}\StringTok{ }\ControlFlowTok{function}\NormalTok{(x, y) }\KeywordTok{return}\NormalTok{(y))} +\CommentTok{\#\# Where x is the input value (here "?") and y is all the possible normal values for the character} +\end{Highlighting} +\end{Shaded} + +This example shows a very common case (and is actually used by default, more on that below) but this architecture allows for very modular combination of tokens and behaviours. +For example, in our code above we introduced the token \texttt{"\%"} which is very odd (to my knowledge) and might mean something very specific in our case. +Say we want to call this case \texttt{"weirdtoken"} and mean that whenever this token is encountered in a character, it should be interpreted by the algorithm as the values 1 and 2, no matter what: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Set a list of extra special tokens} +\NormalTok{my\_spec\_tokens \textless{}{-}}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"weirdtoken"}\NormalTok{ =}\StringTok{ "}\CharTok{\textbackslash{}\textbackslash{}}\StringTok{\%"}\NormalTok{)} + +\CommentTok{\#\# Weird tokens are considered as state 0 and 3} +\NormalTok{my\_spec\_behaviours \textless{}{-}}\StringTok{ }\KeywordTok{list}\NormalTok{()} +\NormalTok{my\_spec\_behaviours}\OperatorTok{$}\NormalTok{weirdtoken \textless{}{-}}\StringTok{ }\ControlFlowTok{function}\NormalTok{(x,y) }\KeywordTok{return}\NormalTok{(}\KeywordTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{,}\DecValTok{2}\NormalTok{))} +\end{Highlighting} +\end{Shaded} + +If you don't need/don't have any of this specific tokens, don't worry, most special but common tokens are handled by default as such: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# The token for missing values:} +\NormalTok{default\_tokens \textless{}{-}}\StringTok{ }\KeywordTok{c}\NormalTok{(}\StringTok{"missing"}\NormalTok{ =}\StringTok{ "}\CharTok{\textbackslash{}\textbackslash{}}\StringTok{?"}\NormalTok{,} +\CommentTok{\#\# The token for inapplicable values: } + \StringTok{"inapplicable"}\NormalTok{ =}\StringTok{ "}\CharTok{\textbackslash{}\textbackslash{}}\StringTok{{-}"}\NormalTok{,} +\CommentTok{\#\# The token for polymorphisms:} + \StringTok{"polymorphism"}\NormalTok{ =}\StringTok{ "}\CharTok{\textbackslash{}\textbackslash{}}\StringTok{\&"}\NormalTok{,} +\CommentTok{\#\# The token for uncertainties:} + \StringTok{"uncertanity"}\NormalTok{ =}\StringTok{ "}\CharTok{\textbackslash{}\textbackslash{}}\StringTok{/"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +With the following associated default behaviours + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Treating missing data as all data values} +\NormalTok{default\_behaviour \textless{}{-}}\StringTok{ }\KeywordTok{list}\NormalTok{(missing \textless{}{-}}\StringTok{ }\ControlFlowTok{function}\NormalTok{(x,y) y,} +\CommentTok{\#\# Treating inapplicable data as all data values (like missing) } +\NormalTok{ inapplicable \textless{}{-}}\StringTok{ }\ControlFlowTok{function}\NormalTok{(x, y) y,} +\CommentTok{\#\# Treating polymorphisms as all values present:} +\NormalTok{ polymorphism \textless{}{-}}\StringTok{ }\ControlFlowTok{function}\NormalTok{(x,y) }\KeywordTok{strsplit}\NormalTok{(x, }\DataTypeTok{split =} \StringTok{"}\CharTok{\textbackslash{}\textbackslash{}}\StringTok{\&"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]],} +\CommentTok{\#\# Treating uncertainties as all values present (like polymorphisms):} +\NormalTok{ uncertanity \textless{}{-}}\StringTok{ }\ControlFlowTok{function}\NormalTok{(x,y) }\KeywordTok{strsplit}\NormalTok{(x, }\DataTypeTok{split =} \StringTok{"}\CharTok{\textbackslash{}\textbackslash{}}\StringTok{\&"}\NormalTok{)[[}\DecValTok{1}\NormalTok{]])} +\end{Highlighting} +\end{Shaded} + +We can then use these token description along with our complex matrix and our list of trees to run the ancestral states estimations as follows: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Running ancestral states} +\NormalTok{ancestral\_states \textless{}{-}}\StringTok{ }\KeywordTok{multi.ace}\NormalTok{(matrix\_complex, multiple\_trees,} + \DataTypeTok{special.tokens =}\NormalTok{ my\_spec\_tokens,} + \DataTypeTok{special.behaviours =}\NormalTok{ my\_spec\_behaviours,} + \DataTypeTok{verbose =} \OtherTok{TRUE}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## Preparing the data:... +\end{verbatim} + +\begin{verbatim} +## Warning: The characters 39 are invariant (using the current special behaviours +## for special characters) and are simply duplicated for each node. +\end{verbatim} + +\begin{verbatim} +## ..Done. +## Running ancestral states estimations: +## ................................................. +\end{verbatim} + +\begin{verbatim} +## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs = +## list(special.tokens = special.tokens), : longer argument not a multiple of +## length of shorter +\end{verbatim} + +\begin{verbatim} +## Done. +## Running ancestral states estimations: +## ................................................. +\end{verbatim} + +\begin{verbatim} +## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs = +## list(special.tokens = special.tokens), : longer argument not a multiple of +## length of shorter +\end{verbatim} + +\begin{verbatim} +## Done. +## Running ancestral states estimations: +## ................................................. +\end{verbatim} + +\begin{verbatim} +## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs = +## list(special.tokens = special.tokens), : longer argument not a multiple of +## length of shorter +\end{verbatim} + +\begin{verbatim} +## Done. +## Running ancestral states estimations: +## ................................................. +\end{verbatim} + +\begin{verbatim} +## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs = +## list(special.tokens = special.tokens), : longer argument not a multiple of +## length of shorter +\end{verbatim} + +\begin{verbatim} +## Done. +## Running ancestral states estimations: +## ................................................. +\end{verbatim} + +\begin{verbatim} +## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs = +## list(special.tokens = special.tokens), : longer argument not a multiple of +## length of shorter +\end{verbatim} + +\begin{verbatim} +## Done. +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# This outputs a list of ancestral parts of the matrices for each tree} +\CommentTok{\#\# For example, here\textquotesingle{}s the first one:} +\NormalTok{ancestral\_states[[}\DecValTok{1}\NormalTok{]][}\DecValTok{1}\OperatorTok{:}\DecValTok{9}\NormalTok{, }\DecValTok{1}\OperatorTok{:}\DecValTok{10}\NormalTok{]} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] +## [1,] "1" "1" "1" "1" "1" "0/1/2" "1" "0" "0" "1" +## [2,] "1" "1" "1" "1" "0/1" "0/1/2" "0/1" "0" "0" "1" +## [3,] "1" "1" "1" "1" "0/1" "0/1/2" "0" "0" "0" "1" +## [4,] "1" "1" "1" "1" "0" "0/1/2" "1" "1" "0" "1" +## [5,] "1" "1" "1" "1" "1" "0/1/2" "1" "0" "0" "1" +## [6,] "1" "1" "1" "1" "1" "0/1/2" "1" "0" "0" "1" +## [7,] "0" "0/1" "0/1" "0" "1" "1" "1" "0" "0" "0/1" +## [8,] "0" "0" "0" "0" "1" "0/1/2" "0" "0" "1" "0" +## [9,] "0" "0" "0" "0" "1" "1" "0" "0" "1" "0" +\end{verbatim} + +Note that there are many different options that are not covered here. +For example, you can use different models for each character via the \texttt{models} argument, you can specify how to handle uncertainties via the \texttt{threshold} argument, use a branch length modifier (\texttt{brlen.multiplier}), specify the type of output, etc\ldots{} + +\hypertarget{feeding-the-results-to-char.diff-to-get-distance-matrices}{% +\subsection{\texorpdfstring{Feeding the results to \texttt{char.diff} to get distance matrices}{Feeding the results to char.diff to get distance matrices}}\label{feeding-the-results-to-char.diff-to-get-distance-matrices}} + +Finally, after running your ancestral states estimations, it is not uncommon to then use these resulting data to calculate the distances between taxa and then ordinate the results to measure disparity. +You can do that using the \texttt{char.diff} function \protect\hyperlink{char.diff}{described above} but instead of measuring the distances between characters (columns) you can measure the distances between species (rows). +You might notice that this function uses the same modular token and behaviour descriptions. +That makes sense because they're using the same core C functions implemented in dispRity that greatly speed up distance calculations. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Running ancestral states} +\CommentTok{\#\# and outputing a list of combined matrices (tips and nodes)} +\NormalTok{ancestral\_states \textless{}{-}}\StringTok{ }\KeywordTok{multi.ace}\NormalTok{(matrix\_complex, multiple\_trees,} + \DataTypeTok{special.tokens =}\NormalTok{ my\_spec\_tokens,} + \DataTypeTok{special.behaviours =}\NormalTok{ my\_spec\_behaviours,} + \DataTypeTok{output =} \StringTok{"combined.matrix"}\NormalTok{,} + \DataTypeTok{verbose =} \OtherTok{TRUE}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## Preparing the data:... +\end{verbatim} + +\begin{verbatim} +## Warning: The characters 39 are invariant (using the current special behaviours +## for special characters) and are simply duplicated for each node. +\end{verbatim} + +\begin{verbatim} +## ..Done. +## Running ancestral states estimations: +## ................................................. +\end{verbatim} + +\begin{verbatim} +## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs = +## list(special.tokens = special.tokens), : longer argument not a multiple of +## length of shorter +\end{verbatim} + +\begin{verbatim} +## Done. +## Running ancestral states estimations: +## ................................................. +\end{verbatim} + +\begin{verbatim} +## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs = +## list(special.tokens = special.tokens), : longer argument not a multiple of +## length of shorter +\end{verbatim} + +\begin{verbatim} +## Done. +## Running ancestral states estimations: +## ................................................. +\end{verbatim} + +\begin{verbatim} +## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs = +## list(special.tokens = special.tokens), : longer argument not a multiple of +## length of shorter +\end{verbatim} + +\begin{verbatim} +## Done. +## Running ancestral states estimations: +## ................................................. +\end{verbatim} + +\begin{verbatim} +## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs = +## list(special.tokens = special.tokens), : longer argument not a multiple of +## length of shorter +\end{verbatim} + +\begin{verbatim} +## Done. +## Running ancestral states estimations: +## ................................................. +\end{verbatim} + +\begin{verbatim} +## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs = +## list(special.tokens = special.tokens), : longer argument not a multiple of +## length of shorter +\end{verbatim} + +\begin{verbatim} +## Done. +\end{verbatim} + +We can then feed these matrices directly to \texttt{char.diff}, say for calculating the ``MORD'' distance: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Measuring the distances between rows using the MORD distance} +\NormalTok{distances \textless{}{-}}\StringTok{ }\KeywordTok{lapply}\NormalTok{(ancestral\_states, char.diff, }\DataTypeTok{method =} \StringTok{"mord"}\NormalTok{, }\DataTypeTok{by.col =} \OtherTok{FALSE}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +And we now have a list of distances matrices with ancestral states estimated! + \hypertarget{the-guts-of-the-disprity-package}{% \chapter{\texorpdfstring{The guts of the \texttt{dispRity} package}{The guts of the dispRity package}}\label{the-guts-of-the-disprity-package}} @@ -6991,7 +7420,7 @@ \subsubsection{\texorpdfstring{\texttt{fill.dispRity}}{fill.dispRity}}\label{fil \end{Shaded} \begin{verbatim} -## Warning in check.dispRity.data(data$matrix): Row names have been automatically +## Warning in check.data(data, match_call): Row names have been automatically ## added to data$matrix. \end{verbatim} @@ -7063,6 +7492,22 @@ \subsubsection{\texorpdfstring{\texttt{n.subsets}}{n.subsets}}\label{n.subsets}} ## [1] 7 \end{verbatim} +\hypertarget{name.subsets}{% +\subsubsection{\texorpdfstring{\texttt{name.subsets}}{name.subsets}}\label{name.subsets}} + +This function gets you the names of the subsets in a \texttt{dispRity} object as a vector. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# What are they called?} +\KeywordTok{name.subsets}\NormalTok{(disparity)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [1] "90" "80" "70" "60" "50" "40" "30" +\end{verbatim} + \hypertarget{size.subsets}{% \subsubsection{\texorpdfstring{\texttt{size.subsets}}{size.subsets}}\label{size.subsets}} @@ -7125,8 +7570,8 @@ \subsubsection{\texorpdfstring{\texttt{get.disparity}}{get.disparity}}\label{get \end{Highlighting} \end{Shaded} -\hypertarget{rescale.disprity}{% -\subsubsection{\texorpdfstring{\texttt{rescale.dispRity}}{rescale.dispRity}}\label{rescale.disprity}} +\hypertarget{scale.disprity}{% +\subsubsection{\texorpdfstring{\texttt{scale.dispRity}}{scale.dispRity}}\label{scale.disprity}} This is the modified S3 method for \texttt{scale} (scaling and/or centring) that can be applied to the disparity data of a \texttt{dispRity} object and can take optional arguments (for example the rescaling by dividing by a maximum value). @@ -7136,13 +7581,13 @@ \subsubsection{\texorpdfstring{\texttt{rescale.dispRity}}{rescale.dispRity}}\lab \KeywordTok{head}\NormalTok{(}\KeywordTok{summary}\NormalTok{(disparity))} \CommentTok{\#\# Scaling the same disparity values} -\KeywordTok{head}\NormalTok{(}\KeywordTok{summary}\NormalTok{(}\KeywordTok{rescale.dispRity}\NormalTok{(disparity, }\DataTypeTok{scale =} \OtherTok{TRUE}\NormalTok{)))} +\KeywordTok{head}\NormalTok{(}\KeywordTok{summary}\NormalTok{(}\KeywordTok{scale.dispRity}\NormalTok{(disparity, }\DataTypeTok{scale =} \OtherTok{TRUE}\NormalTok{)))} \CommentTok{\#\# Scaling and centering:} -\KeywordTok{head}\NormalTok{(}\KeywordTok{summary}\NormalTok{(}\KeywordTok{rescale.dispRity}\NormalTok{(disparity, }\DataTypeTok{scale =} \OtherTok{TRUE}\NormalTok{, }\DataTypeTok{center =} \OtherTok{TRUE}\NormalTok{)))} +\KeywordTok{head}\NormalTok{(}\KeywordTok{summary}\NormalTok{(}\KeywordTok{scale.dispRity}\NormalTok{(disparity, }\DataTypeTok{scale =} \OtherTok{TRUE}\NormalTok{, }\DataTypeTok{center =} \OtherTok{TRUE}\NormalTok{)))} \CommentTok{\#\# Rescaling the value by dividing by a maximum value} -\KeywordTok{head}\NormalTok{(}\KeywordTok{summary}\NormalTok{(}\KeywordTok{rescale.dispRity}\NormalTok{(disparity, }\DataTypeTok{max =} \DecValTok{10}\NormalTok{)))} +\KeywordTok{head}\NormalTok{(}\KeywordTok{summary}\NormalTok{(}\KeywordTok{scale.dispRity}\NormalTok{(disparity, }\DataTypeTok{max =} \DecValTok{10}\NormalTok{)))} \end{Highlighting} \end{Shaded} @@ -7179,39 +7624,126 @@ \subsubsection{\texorpdfstring{\texttt{get.tree} \texttt{add.tree} and \texttt{r \end{Highlighting} \end{Shaded} -\hypertarget{disprity-object}{% -\section{\texorpdfstring{The \texttt{dispRity} object content}{The dispRity object content}}\label{disprity-object}} +Note that \texttt{get.tree} can also be used to extract trees from different subsets (custom or continuous/discrete subsets). -The functions above are utilities to easily and safely access different elements in the \texttt{dispRity} object. -Alternatively, of course, each elements can be accessed manually. -Here is an explanation on how it works. -The \texttt{dispRity} object is a \texttt{list} of two to four elements, each of which are detailed below: +For example, if we have three time bins like in the example below we have three time bins and we can extract the subtrees for these three time bins in different ways using the option \texttt{subsets} and \texttt{to.root}: -\begin{itemize} -\tightlist -\item - \texttt{\$matrix}: an object of class \texttt{list} that contains at least one object of class \texttt{matrix}: the full multidimensional space. -\item - \texttt{\$call}: an object of class \texttt{list} containing information on the \texttt{dispRity} object content. -\item - \texttt{\$subsets}: an object of class \texttt{list} containing the subsets of the multidimensional space. -\item - \texttt{\$disparity}: an object of class \texttt{list} containing the disparity values. -\end{itemize} +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Load the Beck \& Lee 2014 data} +\KeywordTok{data}\NormalTok{(BeckLee\_tree) ; }\KeywordTok{data}\NormalTok{(BeckLee\_mat99) ; }\KeywordTok{data}\NormalTok{(BeckLee\_ages)} -The \texttt{dispRity} object is loosely based on \texttt{C} structure objects. -In fact, it is composed of one unique instance of a matrix (the multidimensional space) upon which the metric function is called via ``pointers'' to only a certain number of elements and/or dimensions of this matrix. -This allows for: (1) faster and easily tractable execution time: the metric functions are called through apply family function and can be parallelised; and (2) a really low memory footprint: at any time, only one matrix (or list of matrices) is present in the \texttt{R} environment rather than multiple copies of it for each subset. +\CommentTok{\#\# Time binning (discrete method)} +\CommentTok{\#\# Generate two discrete time bins from 120 to 40 Ma every 20 Ma} +\NormalTok{time\_bins \textless{}{-}}\StringTok{ }\KeywordTok{chrono.subsets}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ BeckLee\_mat99, }\DataTypeTok{tree =}\NormalTok{ BeckLee\_tree,} + \DataTypeTok{method =} \StringTok{"discrete"}\NormalTok{, }\DataTypeTok{time =} \KeywordTok{c}\NormalTok{(}\DecValTok{120}\NormalTok{, }\DecValTok{100}\NormalTok{, }\DecValTok{80}\NormalTok{, }\DecValTok{60}\NormalTok{),} + \DataTypeTok{inc.nodes =} \OtherTok{TRUE}\NormalTok{, }\DataTypeTok{FADLAD =}\NormalTok{ BeckLee\_ages)} -\hypertarget{matrix}{% -\subsection{\texorpdfstring{\texttt{\$matrix}}{\$matrix}}\label{matrix}} +\CommentTok{\#\# Getting the subtrees all the way to the root} +\NormalTok{root\_subsets \textless{}{-}}\StringTok{ }\KeywordTok{get.tree}\NormalTok{(time\_bins, }\DataTypeTok{subsets =} \OtherTok{TRUE}\NormalTok{)} -This is the multidimensional space, stored in the \texttt{R} environment as a \texttt{list} object containing one or more \texttt{matrix} objects. -Each \texttt{matrix} requires row names but not column names (optional). -By default, if the row names are missing, \texttt{dispRity} function will arbitrarily generate them in numeric order (i.e.~\texttt{rownames(matrix)\ \textless{}-\ 1:nrow(matrix)}). -This element of the \texttt{dispRity} object is never modified. +\CommentTok{\#\# Plotting the bin contents} +\NormalTok{old\_par \textless{}{-}}\StringTok{ }\KeywordTok{par}\NormalTok{(}\DataTypeTok{mfrow =} \KeywordTok{c}\NormalTok{(}\DecValTok{2}\NormalTok{,}\DecValTok{2}\NormalTok{))} +\KeywordTok{plot}\NormalTok{(BeckLee\_tree, }\DataTypeTok{main =} \StringTok{"original tree"}\NormalTok{, }\DataTypeTok{show.tip.label =} \OtherTok{FALSE}\NormalTok{)} +\KeywordTok{axisPhylo}\NormalTok{()} +\KeywordTok{abline}\NormalTok{(}\DataTypeTok{v =}\NormalTok{ BeckLee\_tree}\OperatorTok{$}\NormalTok{root.time }\OperatorTok{{-}}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{120}\NormalTok{, }\DecValTok{100}\NormalTok{, }\DecValTok{80}\NormalTok{, }\DecValTok{60}\NormalTok{))} +\ControlFlowTok{for}\NormalTok{(i }\ControlFlowTok{in} \DecValTok{1}\OperatorTok{:}\DecValTok{3}\NormalTok{) \{} + \KeywordTok{plot}\NormalTok{(root\_subsets[[i]], }\DataTypeTok{main =} \KeywordTok{names}\NormalTok{(root\_subsets)[i],} + \DataTypeTok{show.tip.label =} \OtherTok{FALSE}\NormalTok{)} + \KeywordTok{axisPhylo}\NormalTok{()} +\NormalTok{\}} +\end{Highlighting} +\end{Shaded} -\hypertarget{call}{% +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-202-1.pdf} + +\begin{Shaded} +\begin{Highlighting}[] +\KeywordTok{par}\NormalTok{(old\_par)} +\end{Highlighting} +\end{Shaded} + +But we can also extract the subtrees containing only branch lengths for the actual bins using \texttt{to.root\ =\ FALSE}: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Getting the subtrees all the way to the root} +\NormalTok{bin\_subsets \textless{}{-}}\StringTok{ }\KeywordTok{get.tree}\NormalTok{(time\_bins, }\DataTypeTok{subsets =} \OtherTok{TRUE}\NormalTok{, }\DataTypeTok{to.root =} \OtherTok{FALSE}\NormalTok{)} + +\CommentTok{\#\# Plotting the bin contents} +\NormalTok{old\_par \textless{}{-}}\StringTok{ }\KeywordTok{par}\NormalTok{(}\DataTypeTok{mfrow =} \KeywordTok{c}\NormalTok{(}\DecValTok{2}\NormalTok{,}\DecValTok{2}\NormalTok{))} +\KeywordTok{plot}\NormalTok{(BeckLee\_tree, }\DataTypeTok{main =} \StringTok{"original tree"}\NormalTok{, }\DataTypeTok{show.tip.label =} \OtherTok{FALSE}\NormalTok{)} +\KeywordTok{axisPhylo}\NormalTok{()} +\KeywordTok{abline}\NormalTok{(}\DataTypeTok{v =}\NormalTok{ BeckLee\_tree}\OperatorTok{$}\NormalTok{root.time }\OperatorTok{{-}}\StringTok{ }\KeywordTok{c}\NormalTok{(}\DecValTok{120}\NormalTok{, }\DecValTok{100}\NormalTok{, }\DecValTok{80}\NormalTok{, }\DecValTok{60}\NormalTok{))} +\ControlFlowTok{for}\NormalTok{(i }\ControlFlowTok{in} \DecValTok{1}\OperatorTok{:}\DecValTok{3}\NormalTok{) \{} + \KeywordTok{plot}\NormalTok{(bin\_subsets[[i]], }\DataTypeTok{main =} \KeywordTok{names}\NormalTok{(bin\_subsets)[i],} + \DataTypeTok{show.tip.label =} \OtherTok{FALSE}\NormalTok{)} + \KeywordTok{axisPhylo}\NormalTok{()} +\NormalTok{\}} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-203-1.pdf} + +\begin{Shaded} +\begin{Highlighting}[] +\KeywordTok{par}\NormalTok{(old\_par)} +\end{Highlighting} +\end{Shaded} + +This can be useful for example for calculating the branch lengths in each bin: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# How many cumulated phylogenetic diversity in each bin?} +\KeywordTok{lapply}\NormalTok{(bin\_subsets, }\ControlFlowTok{function}\NormalTok{(tree) }\KeywordTok{sum}\NormalTok{(tree}\OperatorTok{$}\NormalTok{edge.length))} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## $`120 - 100` +## [1] 189.2799 +## +## $`100 - 80` +## [1] 341.7199 +## +## $`80 - 60` +## [1] 426.7493 +\end{verbatim} + +\hypertarget{disprity-object}{% +\section{\texorpdfstring{The \texttt{dispRity} object content}{The dispRity object content}}\label{disprity-object}} + +The functions above are utilities to easily and safely access different elements in the \texttt{dispRity} object. +Alternatively, of course, each elements can be accessed manually. +Here is an explanation on how it works. +The \texttt{dispRity} object is a \texttt{list} of two to four elements, each of which are detailed below: + +\begin{itemize} +\tightlist +\item + \texttt{\$matrix}: an object of class \texttt{list} that contains at least one object of class \texttt{matrix}: the full multidimensional space. +\item + \texttt{\$call}: an object of class \texttt{list} containing information on the \texttt{dispRity} object content. +\item + \texttt{\$subsets}: an object of class \texttt{list} containing the subsets of the multidimensional space. +\item + \texttt{\$disparity}: an object of class \texttt{list} containing the disparity values. +\end{itemize} + +The \texttt{dispRity} object is loosely based on \texttt{C} structure objects. +In fact, it is composed of one unique instance of a matrix (the multidimensional space) upon which the metric function is called via ``pointers'' to only a certain number of elements and/or dimensions of this matrix. +This allows for: (1) faster and easily tractable execution time: the metric functions are called through apply family function and can be parallelised; and (2) a really low memory footprint: at any time, only one matrix (or list of matrices) is present in the \texttt{R} environment rather than multiple copies of it for each subset. + +\hypertarget{matrix}{% +\subsection{\texorpdfstring{\texttt{\$matrix}}{\$matrix}}\label{matrix}} + +This is the multidimensional space, stored in the \texttt{R} environment as a \texttt{list} object containing one or more \texttt{matrix} objects. +Each \texttt{matrix} requires row names but not column names (optional). +By default, if the row names are missing, \texttt{dispRity} function will arbitrarily generate them in numeric order (i.e.~\texttt{rownames(matrix)\ \textless{}-\ 1:nrow(matrix)}). +This element of the \texttt{dispRity} object is never modified. + +\hypertarget{call}{% \subsection{\texorpdfstring{\texttt{\$call}}{\$call}}\label{call}} This element contains the information on the \texttt{dispRity} object content. @@ -7320,6 +7852,1129 @@ \subsection{\texorpdfstring{\texttt{\$disparity}}{\$disparity}}\label{disparity} [1,] 1.744668 1.777418 1.781624 1.739679 \end{verbatim} +\hypertarget{disprity-ecology-demo}{% +\chapter{dispRity ecology demo}\label{disprity-ecology-demo}} + +This is an example of typical disparity analysis that can be performed in ecology. + +\hypertarget{data}{% +\section{Data}\label{data}} + +For this example, we will use the famous \texttt{iris} inbuilt data set + +\begin{Shaded} +\begin{Highlighting}[] +\KeywordTok{data}\NormalTok{(iris)} +\end{Highlighting} +\end{Shaded} + +This data contains petal and sepal length for 150 individual plants sorted into three species. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Separating the species} +\NormalTok{species \textless{}{-}}\StringTok{ }\NormalTok{iris[,}\DecValTok{5}\NormalTok{]} +\CommentTok{\#\# Which species?} +\KeywordTok{unique}\NormalTok{(species)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [1] setosa versicolor virginica +## Levels: setosa versicolor virginica +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Separating the petal/sepal length} +\NormalTok{measurements \textless{}{-}}\StringTok{ }\NormalTok{iris[,}\DecValTok{1}\OperatorTok{:}\DecValTok{4}\NormalTok{]} +\KeywordTok{head}\NormalTok{(measurements)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## Sepal.Length Sepal.Width Petal.Length Petal.Width +## 1 5.1 3.5 1.4 0.2 +## 2 4.9 3.0 1.4 0.2 +## 3 4.7 3.2 1.3 0.2 +## 4 4.6 3.1 1.5 0.2 +## 5 5.0 3.6 1.4 0.2 +## 6 5.4 3.9 1.7 0.4 +\end{verbatim} + +We can then ordinate the data using a PCA (\texttt{prcomp} function) thus defining our four dimensional space as the poetically named petal-space. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Ordinating the data} +\NormalTok{ordination \textless{}{-}}\StringTok{ }\KeywordTok{prcomp}\NormalTok{(measurements)} + +\CommentTok{\#\# The petal{-}space} +\NormalTok{petal\_space \textless{}{-}}\StringTok{ }\NormalTok{ordination}\OperatorTok{$}\NormalTok{x} + +\CommentTok{\#\# Adding the elements names to the petal{-}space (the individuals IDs)} +\KeywordTok{rownames}\NormalTok{(petal\_space) \textless{}{-}}\StringTok{ }\DecValTok{1}\OperatorTok{:}\KeywordTok{nrow}\NormalTok{(petal\_space)} +\end{Highlighting} +\end{Shaded} + +\hypertarget{classic-analysis}{% +\section{Classic analysis}\label{classic-analysis}} + +A classical way to represent this ordinated data would be to use two dimensional plots to look at how the different species are distributed in the petal-space. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Measuring the variance on each axis} +\NormalTok{axis\_variances \textless{}{-}}\StringTok{ }\KeywordTok{apply}\NormalTok{(petal\_space, }\DecValTok{2}\NormalTok{, var)} +\NormalTok{axis\_variances \textless{}{-}}\StringTok{ }\NormalTok{axis\_variances}\OperatorTok{/}\KeywordTok{sum}\NormalTok{(axis\_variances)} + +\CommentTok{\#\# Graphical option} +\KeywordTok{par}\NormalTok{(}\DataTypeTok{bty =} \StringTok{"n"}\NormalTok{)} + +\CommentTok{\#\# A classic 2D ordination plot} +\KeywordTok{plot}\NormalTok{(petal\_space[, }\DecValTok{1}\NormalTok{], petal\_space[, }\DecValTok{2}\NormalTok{], }\DataTypeTok{col =}\NormalTok{ species,} + \DataTypeTok{xlab =} \KeywordTok{paste0}\NormalTok{(}\StringTok{"PC 1 ("}\NormalTok{, }\KeywordTok{round}\NormalTok{(axis\_variances[}\DecValTok{1}\NormalTok{], }\DecValTok{2}\NormalTok{), }\StringTok{")"}\NormalTok{),} + \DataTypeTok{ylab =} \KeywordTok{paste0}\NormalTok{(}\StringTok{"PC 2 ("}\NormalTok{, }\KeywordTok{round}\NormalTok{(axis\_variances[}\DecValTok{2}\NormalTok{], }\DecValTok{2}\NormalTok{), }\StringTok{")"}\NormalTok{))} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-208-1.pdf} + +This shows the distribution of the different species in the petal-space along the two first axis of variation. +This is a pretty standard way to visualise the multidimensional space and further analysis might be necessary to test wether the groups are different such as a linear discriminant analysis (LDA). +However, in this case we are ignoring the two other dimensions of the ordination! +If we look at the two other axis we see a totally different result: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Plotting the two second axis of the petal{-}space} +\KeywordTok{plot}\NormalTok{(petal\_space[, }\DecValTok{3}\NormalTok{], petal\_space[, }\DecValTok{4}\NormalTok{], }\DataTypeTok{col =}\NormalTok{ species,} + \DataTypeTok{xlab =} \KeywordTok{paste0}\NormalTok{(}\StringTok{"PC 3 ("}\NormalTok{, }\KeywordTok{round}\NormalTok{(axis\_variances[}\DecValTok{3}\NormalTok{], }\DecValTok{2}\NormalTok{), }\StringTok{")"}\NormalTok{),} + \DataTypeTok{ylab =} \KeywordTok{paste0}\NormalTok{(}\StringTok{"PC 4 ("}\NormalTok{, }\KeywordTok{round}\NormalTok{(axis\_variances[}\DecValTok{4}\NormalTok{], }\DecValTok{2}\NormalTok{), }\StringTok{")"}\NormalTok{))} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-209-1.pdf} + +Additionally, these two represented dimensions do not represent a biological reality \emph{per se}; i.e.~the values on the first dimension do not represent a continuous trait (e.g.~petal length), instead they just represent the ordinations of correlations between the data and some factors. + +Therefore, we might want to approach this problem without getting stuck in only two dimensions and consider the whole dataset as a \emph{n}-dimensional object. + +\hypertarget{a-multidimensional-approach-with-disprity}{% +\section{\texorpdfstring{A multidimensional approach with \texttt{dispRity}}{A multidimensional approach with dispRity}}\label{a-multidimensional-approach-with-disprity}} + +The first step is to create different subsets that represent subsets of the ordinated space (i.e.~sub-regions within the \emph{n}-dimensional object). +Each of these subsets will contain only the individuals of a specific species. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Creating the table that contain the elements and their attributes} +\NormalTok{petal\_subsets \textless{}{-}}\StringTok{ }\KeywordTok{custom.subsets}\NormalTok{(petal\_space, }\DataTypeTok{group =} \KeywordTok{list}\NormalTok{(} + \StringTok{"setosa"}\NormalTok{ =}\StringTok{ }\KeywordTok{which}\NormalTok{(species }\OperatorTok{==}\StringTok{ "setosa"}\NormalTok{),} + \StringTok{"versicolor"}\NormalTok{ =}\StringTok{ }\KeywordTok{which}\NormalTok{(species }\OperatorTok{==}\StringTok{ "versicolor"}\NormalTok{),} + \StringTok{"virginica"}\NormalTok{ =}\StringTok{ }\KeywordTok{which}\NormalTok{(species }\OperatorTok{==}\StringTok{ "virginica"}\NormalTok{)))} + +\CommentTok{\#\# Visualising the dispRity object content} +\NormalTok{petal\_subsets} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## ---- dispRity object ---- +## 3 customised subsets for 150 elements in one matrix: +## setosa, versicolor, virginica. +\end{verbatim} + +This created a \texttt{dispRity} object (more about that \protect\hyperlink{guts}{here}) with three subsets corresponding to each subspecies. + +\hypertarget{bootstrapping-the-data}{% +\subsection{Bootstrapping the data}\label{bootstrapping-the-data}} + +We can the bootstrap the subsets to be able test the robustness of the measured disparity to outliers. +We can do that using the default options of \texttt{boot.matrix} (more about that \protect\hyperlink{bootstraps-and-rarefactions}{here}): + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Bootstrapping the data} +\NormalTok{(petal\_bootstrapped \textless{}{-}}\StringTok{ }\KeywordTok{boot.matrix}\NormalTok{(petal\_subsets))} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## ---- dispRity object ---- +## 3 customised subsets for 150 elements in one matrix with 4 dimensions: +## setosa, versicolor, virginica. +## Data was bootstrapped 100 times (method:"full"). +\end{verbatim} + +\hypertarget{calculating-disparity}{% +\subsection{Calculating disparity}\label{calculating-disparity}} + +Disparity can be calculated in many ways, therefore the \texttt{dispRity} function allows users to define their own measure of disparity. +For more details on measuring disparity, see the \protect\hyperlink{disparity-metrics}{dispRity metrics section}. + +In this example, we are going to define disparity as the median distance between the different individuals and the centroid of the ordinated space. +High values of disparity will indicate a generally high spread of points from this centroid (i.e.~on average, the individuals are far apart in the ordinated space). +We can define the metrics easily in the \texttt{dispRity} function by feeding them to the \texttt{metric} argument. +Here we are going to feed the functions \texttt{stats::median} and \texttt{dispRity::centroids} which calculates distances between elements and their centroid. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Calculating disparity as the median distance between each elements and} +\CommentTok{\#\# the centroid of the petal{-}space} +\NormalTok{(petal\_disparity \textless{}{-}}\StringTok{ }\KeywordTok{dispRity}\NormalTok{(petal\_bootstrapped, }\DataTypeTok{metric =} \KeywordTok{c}\NormalTok{(median, centroids)))} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## ---- dispRity object ---- +## 3 customised subsets for 150 elements in one matrix with 4 dimensions: +## setosa, versicolor, virginica. +## Data was bootstrapped 100 times (method:"full"). +## Disparity was calculated as: c(median, centroids). +\end{verbatim} + +\hypertarget{summarising-the-results-plot}{% +\subsection{Summarising the results (plot)}\label{summarising-the-results-plot}} + +Similarly to the \texttt{custom.subsets} and \texttt{boot.matrix} function, \texttt{dispRity} displays a \texttt{dispRity} object. +But we are definitely more interested in actually look at the calculated values. + +First we can summarise the data in a table by simply using \texttt{summary}: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Displaying the summary of the calculated disparity} +\KeywordTok{summary}\NormalTok{(petal\_disparity)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## subsets n obs bs.median 2.5% 25% 75% 97.5% +## 1 setosa 50 0.421 0.432 0.370 0.408 0.454 0.501 +## 2 versicolor 50 0.693 0.656 0.511 0.619 0.697 0.770 +## 3 virginica 50 0.785 0.747 0.580 0.674 0.806 0.936 +\end{verbatim} + +We can also plot the results in a similar way: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Graphical options} +\KeywordTok{par}\NormalTok{(}\DataTypeTok{bty =} \StringTok{"n"}\NormalTok{)} + +\CommentTok{\#\# Plotting the disparity in the petal\_space} +\KeywordTok{plot}\NormalTok{(petal\_disparity)} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-214-1.pdf} + +Now contrary to simply plotting the two first axis of the PCA where we saw that the species have a different position in the two first petal-space, we can now also see that they occupy this space clearly differently! + +\hypertarget{testing-hypothesis}{% +\subsection{Testing hypothesis}\label{testing-hypothesis}} + +Finally we can test our hypothesis that we guessed from the disparity plot (that some groups occupy different volume of the petal-space) by using the \texttt{test.dispRity} option. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Running a PERMANOVA} +\KeywordTok{test.dispRity}\NormalTok{(petal\_disparity, }\DataTypeTok{test =}\NormalTok{ adonis.dispRity)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## Warning in test.dispRity(petal_disparity, test = adonis.dispRity): adonis.dispRity test will be applied to the data matrix, not to the calculated disparity. +## See ?adonis.dispRity for more details. +\end{verbatim} + +\begin{verbatim} +## Warning in adonis.dispRity(data, ...): The input data for adonis.dispRity was not a distance matrix. +## The results are thus based on the distance matrix for the input data (i.e. dist(data$matrix[[1]])). +## Make sure that this is the desired methodological approach! +\end{verbatim} + +\begin{verbatim} +## Permutation test for adonis under reduced model +## Terms added sequentially (first to last) +## Permutation: free +## Number of permutations: 999 +## +## vegan::adonis2(formula = dist(matrix) ~ group, method = "euclidean") +## Df SumOfSqs R2 F Pr(>F) +## group 2 592.07 0.86894 487.33 0.001 *** +## Residual 147 89.30 0.13106 +## Total 149 681.37 1.00000 +## --- +## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Post{-}hoc testing of the differences between species (corrected for multiple tests)} +\KeywordTok{test.dispRity}\NormalTok{(petal\_disparity, }\DataTypeTok{test =}\NormalTok{ t.test, }\DataTypeTok{correction =} \StringTok{"bonferroni"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [[1]] +## statistic: t +## setosa : versicolor -29.998366 +## setosa : virginica -30.465933 +## versicolor : virginica -7.498179 +## +## [[2]] +## parameter: df +## setosa : versicolor 149.8429 +## setosa : virginica 124.4227 +## versicolor : virginica 175.4758 +## +## [[3]] +## p.value +## setosa : versicolor 9.579095e-65 +## setosa : virginica 4.625567e-59 +## versicolor : virginica 9.247421e-12 +## +## [[4]] +## stderr +## setosa : versicolor 0.007378905 +## setosa : virginica 0.010103449 +## versicolor : virginica 0.011530255 +\end{verbatim} + +We can now see that there is a significant difference in petal-space occupancy between all species of iris. + +\hypertarget{setting-up-a-multidimensional-null-hypothesis}{% +\subsubsection{Setting up a multidimensional null-hypothesis}\label{setting-up-a-multidimensional-null-hypothesis}} + +One other series of test can be done on the shape of the petal-space. +Using a MCMC permutation test we can simulate a petal-space with specific properties and see if our observed petal-space matches these properties (similarly to \citet{diaz2016global}): + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Testing against a uniform distribution} +\NormalTok{disparity\_uniform \textless{}{-}}\StringTok{ }\KeywordTok{null.test}\NormalTok{(petal\_disparity, }\DataTypeTok{replicates =} \DecValTok{200}\NormalTok{,} + \DataTypeTok{null.distrib =}\NormalTok{ runif, }\DataTypeTok{scale =} \OtherTok{FALSE}\NormalTok{)} +\KeywordTok{plot}\NormalTok{(disparity\_uniform)} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-216-1.pdf} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Testing against a normal distribution} +\NormalTok{disparity\_normal \textless{}{-}}\StringTok{ }\KeywordTok{null.test}\NormalTok{(petal\_disparity, }\DataTypeTok{replicates =} \DecValTok{200}\NormalTok{,} + \DataTypeTok{null.distrib =}\NormalTok{ rnorm, }\DataTypeTok{scale =} \OtherTok{TRUE}\NormalTok{)} +\KeywordTok{plot}\NormalTok{(disparity\_normal)} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-217-1.pdf} + +In both cases we can see that our petal-space is not entirely normal or uniform. +This is expected because of the simplicity of these parameters. + +\hypertarget{palaeobiology-demo-disparity-through-time-and-within-groups}{% +\chapter{Palaeobiology demo: disparity-through-time and within groups}\label{palaeobiology-demo-disparity-through-time-and-within-groups}} + +This demo aims to give quick overview of the \texttt{dispRity} package (v.1.7) for palaeobiology analyses of disparity, including disparity through time analyses. + +This demo showcases a typical disparity-through-time analysis: we are going to test whether the disparity changed through time in a subset of eutherian mammals from the last 100 million years using a dataset from \citet{beckancient2014}. + +\hypertarget{before-starting}{% +\section{Before starting}\label{before-starting}} + +\hypertarget{the-morphospace}{% +\subsection{The morphospace}\label{the-morphospace}} + +In this example, we are going to use a subset of the data from \citet{beckancient2014}. +See the \protect\hyperlink{example-data}{example data} description for more details. +Briefly, this dataset contains an ordinated matrix of the Gower distance between 50 mammals based (\texttt{BeckLee\_mat50}), another matrix of the same 50 mammals and the estimated discrete data characters of their descendants (thus 50 + 49 rows, \texttt{BeckLee\_mat99}), a dataframe containing the ages of each taxon in the dataset (\texttt{BeckLee\_ages}) and finally a phylogenetic tree with the relationships among the 50 mammals (\texttt{BeckLee\_tree}). +The ordinated matrix will represent our full morphospace, i.e.~all the mammalian morphologies that ever existed through time (for this dataset). + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Loading demo and the package data} +\KeywordTok{library}\NormalTok{(dispRity)} + +\CommentTok{\#\# Setting the random seed for repeatability} +\KeywordTok{set.seed}\NormalTok{(}\DecValTok{123}\NormalTok{)} + +\CommentTok{\#\# Loading the ordinated matrix/morphospace:} +\KeywordTok{data}\NormalTok{(BeckLee\_mat50)} +\KeywordTok{data}\NormalTok{(BeckLee\_mat99)} +\KeywordTok{head}\NormalTok{(BeckLee\_mat50[,}\DecValTok{1}\OperatorTok{:}\DecValTok{5}\NormalTok{])} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [,1] [,2] [,3] [,4] [,5] +## Cimolestes -0.5613001 0.06006259 0.08414761 -0.2313084 -0.18825039 +## Maelestes -0.4186019 -0.12186005 0.25556379 0.2737995 -0.28510479 +## Batodon -0.8337640 0.28718501 -0.10594610 -0.2381511 -0.07132646 +## Bulaklestes -0.7708261 -0.07629583 0.04549285 -0.4951160 -0.39962626 +## Daulestes -0.8320466 -0.09559563 0.04336661 -0.5792351 -0.37385914 +## Uchkudukodon -0.5074468 -0.34273248 0.40410310 -0.1223782 -0.34857351 +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\KeywordTok{dim}\NormalTok{(BeckLee\_mat50)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [1] 50 48 +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# The morphospace contains 50 taxa and has 48 dimensions (or axes)} + +\CommentTok{\#\# Showing a list of first and last occurrences data for some fossils} +\KeywordTok{data}\NormalTok{(BeckLee\_ages)} +\KeywordTok{head}\NormalTok{(BeckLee\_ages)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## FAD LAD +## Adapis 37.2 36.8 +## Asioryctes 83.6 72.1 +## Leptictis 33.9 33.3 +## Miacis 49.0 46.7 +## Mimotona 61.6 59.2 +## Notharctus 50.2 47.0 +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Plotting a phylogeny} +\KeywordTok{data}\NormalTok{(BeckLee\_tree)} +\KeywordTok{plot}\NormalTok{(BeckLee\_tree, }\DataTypeTok{cex =} \FloatTok{0.7}\NormalTok{)} +\KeywordTok{axisPhylo}\NormalTok{(}\DataTypeTok{root =} \DecValTok{140}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-218-1.pdf} + +\begin{quote} +You can have an even nicer looking tree if you use the \texttt{strap} package! +\end{quote} + +\begin{Shaded} +\begin{Highlighting}[] +\ControlFlowTok{if}\NormalTok{(}\OperatorTok{!}\KeywordTok{require}\NormalTok{(strap)) }\KeywordTok{install.packages}\NormalTok{(}\StringTok{"strap"}\NormalTok{)} +\NormalTok{strap}\OperatorTok{::}\KeywordTok{geoscalePhylo}\NormalTok{(BeckLee\_tree, }\DataTypeTok{cex.tip =} \FloatTok{0.7}\NormalTok{, }\DataTypeTok{cex.ts =} \FloatTok{0.6}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-219-1.pdf} + +\hypertarget{setting-up-your-own-data}{% +\subsection{Setting up your own data}\label{setting-up-your-own-data}} + +I greatly encourage you to follow along this tutorial with your very own data: it is more exciting and, ultimately, that's probably your objective. + +\begin{quote} +What data can I use? +\end{quote} + +You can use any type of morphospace in any dataset form (\texttt{"matrix"}, \texttt{"data.frame"}). Throughout this tutorial, you we assume you are using the (loose) morphospace definition from \citet{Guillerme2020}: any matrix were columns are traits and rows are observations (in a distance matrix, columns are still trait, i.e.~``distance to species A'', etc.). +We won't cover it here but you can also use lists of matrices and list of trees. + +\begin{quote} +How should I format my data for this tutorial? +\end{quote} + +To go through this tutorial you will need: + +\begin{itemize} +\tightlist +\item + A matrix with tip data +\item + A phylogenetic tree +\item + A matrix with tip and node data +\item + A table of first and last occurrences data (FADLAD) +\end{itemize} + +If you are missing any of these, fear not, here are a couple of functions to simulate the missing data, it will surely make your results look funky but it'll let you go through the tutorial. + +\begin{quote} +\textbf{WARNING:} the data generated by the functions \texttt{i.need.a.matrix}, \texttt{i.need.a.tree}, \texttt{i.need.node.data} and \texttt{i.need.FADLAD} are used to \textbf{SIMULATE} data for this tutorial. This is \emph{not} to be used for publications or analysing real data! +If you need a data matrix, a phylogenetic tree or FADLAD data, (\texttt{i.need.a.matrix}, \texttt{i.need.a.tree} and \texttt{i.need.FADLAD}), you will actually need to collect data from the literature or the field! If you need node data, you will need to use ancestral states estimations (e.g.~using \texttt{estimate\_ancestral\_states} from the \href{https://cran.r-project.org/web/packages/Claddis/index.html}{\texttt{Claddis} package}). +\end{quote} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Functions to get simulate a PCO looking like matrix from a tree} +\NormalTok{i.need.a.matrix \textless{}{-}}\StringTok{ }\ControlFlowTok{function}\NormalTok{(tree) \{} +\NormalTok{ matrix \textless{}{-}}\StringTok{ }\KeywordTok{space.maker}\NormalTok{(}\DataTypeTok{elements =} \KeywordTok{Ntip}\NormalTok{(tree), }\DataTypeTok{dimensions =} \KeywordTok{Ntip}\NormalTok{(tree), }\DataTypeTok{distribution =}\NormalTok{ rnorm,} + \DataTypeTok{scree =} \KeywordTok{rev}\NormalTok{(}\KeywordTok{cumsum}\NormalTok{(}\KeywordTok{rep}\NormalTok{(}\DecValTok{1}\OperatorTok{/}\KeywordTok{Ntip}\NormalTok{(tree), }\KeywordTok{Ntip}\NormalTok{(tree)))))} + \KeywordTok{rownames}\NormalTok{(matrix) \textless{}{-}}\StringTok{ }\NormalTok{tree}\OperatorTok{$}\NormalTok{tip.label} + \KeywordTok{return}\NormalTok{(matrix)} +\NormalTok{\}} + +\CommentTok{\#\# Function to simulate a tree} +\NormalTok{i.need.a.tree \textless{}{-}}\StringTok{ }\ControlFlowTok{function}\NormalTok{(matrix) \{} +\NormalTok{ tree \textless{}{-}}\StringTok{ }\KeywordTok{rtree}\NormalTok{(}\KeywordTok{nrow}\NormalTok{(matrix))} +\NormalTok{ tree}\OperatorTok{$}\NormalTok{root.time \textless{}{-}}\StringTok{ }\KeywordTok{max}\NormalTok{(}\KeywordTok{tree.age}\NormalTok{(tree)}\OperatorTok{$}\NormalTok{age)} +\NormalTok{ tree}\OperatorTok{$}\NormalTok{tip.label \textless{}{-}}\StringTok{ }\KeywordTok{rownames}\NormalTok{(matrix)} +\NormalTok{ tree}\OperatorTok{$}\NormalTok{node.label \textless{}{-}}\StringTok{ }\KeywordTok{paste0}\NormalTok{(}\StringTok{"n"}\NormalTok{, }\DecValTok{1}\OperatorTok{:}\NormalTok{(}\KeywordTok{nrow}\NormalTok{(matrix)}\OperatorTok{{-}}\DecValTok{1}\NormalTok{))} + \KeywordTok{return}\NormalTok{(tree)} +\NormalTok{\}} + +\CommentTok{\#\# Function to simulate some "node" data} +\NormalTok{i.need.node.data \textless{}{-}}\StringTok{ }\ControlFlowTok{function}\NormalTok{(matrix, tree) \{} +\NormalTok{ matrix\_node \textless{}{-}}\StringTok{ }\KeywordTok{space.maker}\NormalTok{(}\DataTypeTok{elements =} \KeywordTok{Nnode}\NormalTok{(tree), }\DataTypeTok{dimensions =} \KeywordTok{ncol}\NormalTok{(matrix),} + \DataTypeTok{distribution =}\NormalTok{ rnorm, }\DataTypeTok{scree =} \KeywordTok{apply}\NormalTok{(matrix, }\DecValTok{2}\NormalTok{, var))} + \ControlFlowTok{if}\NormalTok{(}\OperatorTok{!}\KeywordTok{is.null}\NormalTok{(tree}\OperatorTok{$}\NormalTok{node.label)) \{} + \KeywordTok{rownames}\NormalTok{(matrix\_node) \textless{}{-}}\StringTok{ }\NormalTok{tree}\OperatorTok{$}\NormalTok{node.label} +\NormalTok{ \} }\ControlFlowTok{else}\NormalTok{ \{} + \KeywordTok{rownames}\NormalTok{(matrix\_node) \textless{}{-}}\StringTok{ }\KeywordTok{paste0}\NormalTok{(}\StringTok{"n"}\NormalTok{, }\DecValTok{1}\OperatorTok{:}\NormalTok{(}\KeywordTok{nrow}\NormalTok{(matrix)}\OperatorTok{{-}}\DecValTok{1}\NormalTok{))} +\NormalTok{ \}} + \KeywordTok{return}\NormalTok{(}\KeywordTok{rbind}\NormalTok{(matrix, matrix\_node))} +\NormalTok{\}} + +\CommentTok{\#\# Function to simulate some "FADLAD" data} +\NormalTok{i.need.FADLAD \textless{}{-}}\StringTok{ }\ControlFlowTok{function}\NormalTok{(tree) \{} +\NormalTok{ tree\_ages \textless{}{-}}\StringTok{ }\KeywordTok{tree.age}\NormalTok{(tree)[}\DecValTok{1}\OperatorTok{:}\KeywordTok{Ntip}\NormalTok{(tree),]} + \KeywordTok{return}\NormalTok{(}\KeywordTok{data.frame}\NormalTok{(}\DataTypeTok{FAD =}\NormalTok{ tree\_ages[,}\DecValTok{1}\NormalTok{], }\DataTypeTok{LAD =}\NormalTok{ tree\_ages[,}\DecValTok{1}\NormalTok{], }\DataTypeTok{row.names =}\NormalTok{ tree\_ages[,}\DecValTok{2}\NormalTok{]))} +\NormalTok{\}} +\end{Highlighting} +\end{Shaded} + +You can use these functions for the generating the data you need. For example + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Aaaaah I don\textquotesingle{}t have FADLAD data!} +\NormalTok{my\_FADLAD \textless{}{-}}\StringTok{ }\KeywordTok{i.need.FADLAD}\NormalTok{(tree)} +\CommentTok{\#\# Sorted.} +\end{Highlighting} +\end{Shaded} + +In the end this is what your data should be named to facilitate the rest of this tutorial (fill in yours here): + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# A matrix with tip data} +\NormalTok{my\_matrix \textless{}{-}}\StringTok{ }\NormalTok{BeckLee\_mat50} + +\CommentTok{\#\# A phylogenetic tree } +\NormalTok{my\_tree \textless{}{-}}\StringTok{ }\NormalTok{BeckLee\_tree} + +\CommentTok{\#\# A matrix with tip and node data} +\NormalTok{my\_tip\_node\_matrix \textless{}{-}}\StringTok{ }\NormalTok{BeckLee\_mat99} + +\CommentTok{\#\# A table of first and last occurrences data (FADLAD)} +\NormalTok{my\_fadlad \textless{}{-}}\StringTok{ }\NormalTok{BeckLee\_ages} +\end{Highlighting} +\end{Shaded} + +\hypertarget{a-disparity-through-time-analysis}{% +\section{A disparity-through-time analysis}\label{a-disparity-through-time-analysis}} + +\hypertarget{splitting-the-morphospace-through-time}{% +\subsection{Splitting the morphospace through time}\label{splitting-the-morphospace-through-time}} + +One of the crucial steps in disparity-through-time analysis is to split the full morphospace into smaller time subsets that contain the total number of morphologies at certain points in time (time-slicing) or during certain periods in time (time-binning). +Basically, the full morphospace represents the total number of morphologies across all time and will be greater than any of the time subsets of the morphospace. + +The \texttt{dispRity} package provides a \texttt{chrono.subsets} function that allows users to split the morphospace into time slices (using \texttt{method\ =\ continuous}) or into time bins (using \texttt{method\ =\ discrete}). +In this example, we are going to split the morphospace into five equal time bins of 20 million years long from 100 million years ago to the present. +We will also provide to the function a table containing the first and last occurrences dates for some fossils to take into account that some fossils might occur in several of our different time bins. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Creating the vector of time bins ages} +\NormalTok{time\_bins \textless{}{-}}\StringTok{ }\KeywordTok{rev}\NormalTok{(}\KeywordTok{seq}\NormalTok{(}\DataTypeTok{from =} \DecValTok{0}\NormalTok{, }\DataTypeTok{to =} \DecValTok{100}\NormalTok{, }\DataTypeTok{by =} \DecValTok{20}\NormalTok{))} + +\CommentTok{\#\# Splitting the morphospace using the chrono.subsets function} +\NormalTok{binned\_morphospace \textless{}{-}}\StringTok{ }\KeywordTok{chrono.subsets}\NormalTok{(}\DataTypeTok{data =}\NormalTok{ my\_matrix, }\DataTypeTok{tree =}\NormalTok{ my\_tree,} + \DataTypeTok{method =} \StringTok{"discrete"}\NormalTok{, }\DataTypeTok{time =}\NormalTok{ time\_bins, }\DataTypeTok{inc.nodes =} \OtherTok{FALSE}\NormalTok{,} + \DataTypeTok{FADLAD =}\NormalTok{ my\_fadlad)} +\end{Highlighting} +\end{Shaded} + +The output object is a \texttt{dispRity} object (see more about that \protect\hyperlink{The-guts-of-the-dispRity-package}{here}. +In brief, \texttt{dispRity} objects are lists of different elements (i.e.~disparity results, morphospace time subsets, morphospace attributes, etc.) that display only a summary of the object when calling the object to avoiding filling the \texttt{R} console with superfluous output. +It also allows easy plotting/summarising/analysing for repeatability down the line but we will not go into this right now. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Printing the class of the object} +\KeywordTok{class}\NormalTok{(binned\_morphospace)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [1] "dispRity" +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Printing the content of the object} +\KeywordTok{str}\NormalTok{(binned\_morphospace)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## List of 4 +## $ matrix :List of 1 +## ..$ : num [1:50, 1:48] -0.561 -0.419 -0.834 -0.771 -0.832 ... +## .. ..- attr(*, "dimnames")=List of 2 +## .. .. ..$ : chr [1:50] "Cimolestes" "Maelestes" "Batodon" "Bulaklestes" ... +## .. .. ..$ : NULL +## $ tree :Class "multiPhylo" +## List of 1 +## ..$ :List of 6 +## .. ..$ edge : int [1:98, 1:2] 51 52 52 53 53 51 54 55 56 56 ... +## .. ..$ edge.length: num [1:98] 24.5 24.6 12.7 11.8 11.8 ... +## .. ..$ Nnode : int 49 +## .. ..$ tip.label : chr [1:50] "Daulestes" "Bulaklestes" "Uchkudukodon" "Kennalestes" ... +## .. ..$ node.labels: chr [1:49] "n1" "n2" "n3" "n4" ... +## .. ..$ root.time : num 139 +## .. ..- attr(*, "class")= chr "phylo" +## .. ..- attr(*, "order")= chr "cladewise" +## $ call :List of 1 +## ..$ subsets: Named chr [1:4] "discrete" "1" "1" "FALSE" +## .. ..- attr(*, "names")= chr [1:4] "" "trees" "matrices" "bind" +## $ subsets:List of 5 +## ..$ 100 - 80:List of 1 +## .. ..$ elements: int [1:8, 1] 5 4 6 8 43 10 11 42 +## ..$ 80 - 60 :List of 1 +## .. ..$ elements: int [1:15, 1] 7 8 9 1 2 3 12 13 14 44 ... +## ..$ 60 - 40 :List of 1 +## .. ..$ elements: int [1:13, 1] 41 49 24 25 26 27 28 21 22 19 ... +## ..$ 40 - 20 :List of 1 +## .. ..$ elements: int [1:6, 1] 15 39 40 35 23 47 +## ..$ 20 - 0 :List of 1 +## .. ..$ elements: int [1:10, 1] 36 37 38 32 33 34 50 48 29 30 +## - attr(*, "class")= chr "dispRity" +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\KeywordTok{names}\NormalTok{(binned\_morphospace)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [1] "matrix" "tree" "call" "subsets" +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Printing the object as a dispRity class} +\NormalTok{binned\_morphospace} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## ---- dispRity object ---- +## 5 discrete time subsets for 50 elements in one matrix with 1 phylogenetic tree +## 100 - 80, 80 - 60, 60 - 40, 40 - 20, 20 - 0. +\end{verbatim} + +\begin{quote} +These objects will gradually contain more information when completing the following steps in the disparity-through-time analysis. +\end{quote} + +\hypertarget{bootstrapping-the-data-1}{% +\subsection{Bootstrapping the data}\label{bootstrapping-the-data-1}} + +Once we obtain our different time subsets, we can bootstrap and rarefy them (i.e.~pseudo-replicating the data). +The bootstrapping allows us to make each subset more robust to outliers and the rarefaction allows us to compare subsets with the same number of taxa to remove sampling biases (i.e.~more taxa in one subset than the others). +The \texttt{boot.matrix} function bootstraps the \texttt{dispRity} object and the \texttt{rarefaction} option within performs rarefaction. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Getting the minimum number of rows (i.e. taxa) in the time subsets} +\NormalTok{minimum\_size \textless{}{-}}\StringTok{ }\KeywordTok{min}\NormalTok{(}\KeywordTok{size.subsets}\NormalTok{(binned\_morphospace))} + +\CommentTok{\#\# Bootstrapping each time subset 100 times and rarefying them } +\NormalTok{rare\_bin\_morphospace \textless{}{-}}\StringTok{ }\KeywordTok{boot.matrix}\NormalTok{(binned\_morphospace, }\DataTypeTok{bootstraps =} \DecValTok{100}\NormalTok{,} + \DataTypeTok{rarefaction =}\NormalTok{ minimum\_size)} +\end{Highlighting} +\end{Shaded} + +\begin{quote} +Note how information is adding up to the \texttt{dispRity} object. +\end{quote} + +\hypertarget{calculating-disparity-1}{% +\subsection{Calculating disparity}\label{calculating-disparity-1}} + +We can now calculate the disparity within each time subsets along with some confidence intervals generated by the pseudoreplication step above (bootstraps/rarefaction). +Disparity can be calculated in many ways and this package allows users to come up with their own disparity metrics. +For more details, please refer to the \protect\hyperlink{disparity-metrics}{\texttt{dispRity} metric section} (or directly use \href{https://tguillerme.shinyapps.io/moms/}{\texttt{moms}}). + +In this example, we are going to look at how the spread of the data in the morphospace through time. +For that we are going to use the sum of the variance from each dimension of the morphospace in the morphospace. +We highly recommend using a metric that makes sense for your specific analysis and for your specific dataset and not just because everyone uses it \citep[\citet{Guillerme2020}]{moms}! + +\begin{quote} +How can I be sure that the metric is the most appropriate for my morphospace and question? +\end{quote} + +This is not a straightforward question but you can use the \texttt{test.metric} function to check your assumptions (more details \protect\hyperlink{disparity-metrics}{here}): basically what \texttt{test.metric} does is modifying your morphospace using a null process of interest (e.g.~changes in size) and checks whether your metric does indeed pick up that change. +For example here, let see if the sum of variances picks up changes in size but not random changes: + +\begin{Shaded} +\begin{Highlighting}[] +\NormalTok{my\_test \textless{}{-}}\StringTok{ }\KeywordTok{test.metric}\NormalTok{(my\_matrix, }\DataTypeTok{metric =} \KeywordTok{c}\NormalTok{(sum, dispRity}\OperatorTok{::}\NormalTok{variances), }\DataTypeTok{shifts =} \KeywordTok{c}\NormalTok{(}\StringTok{"random"}\NormalTok{, }\StringTok{"size"}\NormalTok{))} +\KeywordTok{summary}\NormalTok{(my\_test)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% slope +## random 2.41 2.51 2.56 2.50 2.54 2.51 2.52 2.53 2.53 2.52 0.0006434981 +## size.increase 2.23 2.19 2.25 2.33 2.31 2.35 2.43 2.44 2.48 2.52 0.0036071419 +## size.hollowness 2.40 2.56 2.56 2.60 2.63 2.64 2.60 2.58 2.55 2.52 0.0006032204 +## p_value R^2(adj) +## random 3.046683e-02 0.12638784 +## size.increase 4.009847e-16 0.90601561 +## size.hollowness 1.324664e-01 0.04783366 +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\KeywordTok{plot}\NormalTok{(my\_test)} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-226-1.pdf} + +We see that changes in the inner size (see \citet{moms} for more details) is actually picked up by the sum of variances but not random changes or outer changes. Which is a good thing! + +As you've noted, the sum of variances is defined in \texttt{test.metric} as \texttt{c(sum,\ variances)}. This is a core bit of the \texttt{dispRity} package were you can define your own metric as a function or a set of functions. +You can find more info about this in the \protect\hyperlink{disparity-metrics}{\texttt{dispRity} metric section} but in brief, the \texttt{dispRity} package considers metrics by their ``dimensions'' level which corresponds to what they output. For example, the function \texttt{sum} is a dimension level 1 function because no matter the input it outputs a single value (the sum), \texttt{variances} on the other hand is a dimension level 2 function because it will output the variance of each column in a matrix (an example of a dimensions level 3 would be the function \texttt{var} that outputs a matrix). +The \texttt{dispRity} package always automatically sorts the dimensions levels: it will always run dimensions level 3 \textgreater{} dimensions level 2 \textgreater{} and dimensions level 1. In this case both \texttt{c(sum,\ variances)} and \texttt{c(variances,\ sum)} will result in actually running \texttt{sum(variances(matrix))}. + +Anyways, let's calculate the sum of variances on our bootstrapped and rarefied morphospaces: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Calculating disparity for the bootstrapped and rarefied data} +\NormalTok{disparity \textless{}{-}}\StringTok{ }\KeywordTok{dispRity}\NormalTok{(rare\_bin\_morphospace , }\DataTypeTok{metric =} \KeywordTok{c}\NormalTok{(sum, dispRity}\OperatorTok{::}\NormalTok{variances))} +\end{Highlighting} +\end{Shaded} + +To display the actual calculated scores, we need to summarise the disparity object using the S3 method \texttt{summary} that is applied to a \texttt{dispRity} object (see \texttt{?summary.dispRity} for more details). +By the way, as for any \texttt{R} package, you can refer to the help files for each individual function for more details. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Summarising the disparity results} +\KeywordTok{summary}\NormalTok{(disparity)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## subsets n obs bs.median 2.5% 25% 75% 97.5% +## 1 100 - 80 8 2.207 1.962 1.615 1.876 2.017 2.172 +## 2 100 - 80 6 NA 1.923 1.477 1.768 2.065 2.222 +## 3 80 - 60 15 2.315 2.167 1.979 2.111 2.227 2.308 +## 4 80 - 60 6 NA 2.167 1.831 2.055 2.300 2.460 +## 5 60 - 40 13 2.435 2.244 2.006 2.183 2.304 2.384 +## 6 60 - 40 6 NA 2.284 1.683 2.140 2.383 2.532 +## 7 40 - 20 6 2.604 2.206 1.628 2.026 2.388 2.604 +## 8 20 - 0 10 2.491 2.257 1.958 2.170 2.326 2.421 +## 9 20 - 0 6 NA 2.302 1.766 2.143 2.366 2.528 +\end{verbatim} + +\begin{quote} +The \texttt{summary.dispRity} function comes with many options on which values to calculate (central tendency and quantiles) and on how many digits to display. Refer to the function's manual for more details. +\end{quote} + +\hypertarget{plotting-the-results}{% +\subsection{Plotting the results}\label{plotting-the-results}} + +It is sometimes easier to visualise the results in a plot than in a table. +For that we can use the \texttt{plot} S3 function to plot the \texttt{dispRity} objects (see \texttt{?plot.dispRity} for more details). + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Graphical options} +\KeywordTok{quartz}\NormalTok{(}\DataTypeTok{width =} \DecValTok{10}\NormalTok{, }\DataTypeTok{height =} \DecValTok{5}\NormalTok{) ; }\KeywordTok{par}\NormalTok{(}\DataTypeTok{mfrow =}\NormalTok{ (}\KeywordTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{,}\DecValTok{2}\NormalTok{)), }\DataTypeTok{bty =} \StringTok{"n"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## Warning in quartz(width = 10, height = 5): Quartz device is not available on +## this platform +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Plotting the bootstrapped and rarefied results} +\KeywordTok{plot}\NormalTok{(disparity, }\DataTypeTok{type =} \StringTok{"continuous"}\NormalTok{, }\DataTypeTok{main =} \StringTok{"bootstrapped results"}\NormalTok{)} +\KeywordTok{plot}\NormalTok{(disparity, }\DataTypeTok{type =} \StringTok{"continuous"}\NormalTok{, }\DataTypeTok{main =} \StringTok{"rarefied results"}\NormalTok{,} + \DataTypeTok{rarefaction =}\NormalTok{ minimum\_size)} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-229-1.pdf} + +Nice. The curves look pretty similar. + +\begin{quote} +Same as for the \texttt{summary.dispRity} function, check out the \texttt{plot.dispRity} manual for the many, many options available. +\end{quote} + +\hypertarget{testing-differences}{% +\subsection{Testing differences}\label{testing-differences}} + +Finally, to draw some valid conclusions from these results, we can apply some statistical tests. +We can test, for example, if mammalian disparity changed significantly through time over the last 100 million years. +To do so, we can compare the means of each time-bin in a sequential manner to see whether the disparity in bin \emph{n} is equal to the disparity in bin \emph{n+1}, and whether this is in turn equal to the disparity in bin \emph{n+2}, etc. +Because our data is temporally autocorrelated (i.e.~what happens in bin \emph{n+1} depends on what happened in bin \emph{n}) and pseudoreplicated (i.e.~each bootstrap draw creates non-independent time subsets because they are all based on the same time subsets), we apply a non-parametric mean comparison: the \texttt{wilcox.test}. +Also, we need to apply a p-value correction (e.g.~Bonferroni correction) to correct for multiple testing (see \texttt{?p.adjust} for more details). + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Testing the differences between bins in the bootstrapped dataset.} +\KeywordTok{test.dispRity}\NormalTok{(disparity, }\DataTypeTok{test =}\NormalTok{ wilcox.test, }\DataTypeTok{comparison =} \StringTok{"sequential"}\NormalTok{,} + \DataTypeTok{correction =} \StringTok{"bonferroni"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [[1]] +## statistic: W +## 100 - 80 : 80 - 60 730 +## 80 - 60 : 60 - 40 2752 +## 60 - 40 : 40 - 20 5461 +## 40 - 20 : 20 - 0 4506 +## +## [[2]] +## p.value +## 100 - 80 : 80 - 60 7.081171e-25 +## 80 - 60 : 60 - 40 1.593988e-07 +## 60 - 40 : 40 - 20 1.000000e+00 +## 40 - 20 : 20 - 0 9.115419e-01 +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Testing the differences between bins in the rarefied dataset.} +\KeywordTok{test.dispRity}\NormalTok{(disparity, }\DataTypeTok{test =}\NormalTok{ wilcox.test, }\DataTypeTok{comparison =} \StringTok{"sequential"}\NormalTok{,} + \DataTypeTok{correction =} \StringTok{"bonferroni"}\NormalTok{, }\DataTypeTok{rarefaction =}\NormalTok{ minimum\_size)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [[1]] +## statistic: W +## 100 - 80 : 80 - 60 1518 +## 80 - 60 : 60 - 40 3722 +## 60 - 40 : 40 - 20 5676 +## 40 - 20 : 20 - 0 4160 +## +## [[2]] +## p.value +## 100 - 80 : 80 - 60 7.158946e-17 +## 80 - 60 : 60 - 40 7.199018e-03 +## 60 - 40 : 40 - 20 3.953427e-01 +## 40 - 20 : 20 - 0 1.609715e-01 +\end{verbatim} + +Here our results show significant changes in disparity through time between all time bins (all p-values \textless{} 0.05). +However, when looking at the rarefied results, there is no significant difference between the time bins in the Palaeogene (60-40 to 40-20 Mya), suggesting that the differences detected in the first test might just be due to the differences in number of taxa sampled (13 or 6 taxa) in each time bin. + +\hypertarget{some-more-advanced-stuff}{% +\section{Some more advanced stuff}\label{some-more-advanced-stuff}} + +The previous section detailed some of the basic functionalities in the \texttt{dispRity} package but of course, you can do some much more advanced analysis, here is just a list of some specific tutorials from this manual that you might be interested in: + +\begin{itemize} +\tightlist +\item + \protect\hyperlink{time-slicing}{Time slicing}: an alternative method to look at disparity through time that allows you to specify evolutionary models \citep{time-slice}. +\item + \protect\hyperlink{disparity-metrics}{Many more disparity metrics}: there are many, many different things you might be interested to measure in your morphospace! This manual has some extended documentation on what to use (or check \citet{moms}). +\item + Many more ways to look at disparity: you can for example, \protect\hyperlink{disparity-distribution}{use distributions rather than point estimates} for your disparity metric (e.g.~the variances rather than the sum of variances); or calculate \protect\hyperlink{other-matrices}{disparity from non ordinated matrices} or even \protect\hyperlink{multi.input}{from multiple matrices and trees}. +\item + And finally there are much more advanced statistical tests you might be interested in using, such as the \protect\hyperlink{adonis}{NPMANOVA}, the \protect\hyperlink{dtt}{``disparity-through-time test''}, using a \protect\hyperlink{null-test}{null model approach} or some \protect\hyperlink{model-fitting}{model fitting}\ldots{} +\end{itemize} + +You can even come up with your own ideas, implementations and modifications of the package: the \texttt{dispRity} package is a modular and collaborative package and I encourage you to contact me (\href{mailto:guillert@tcd.e}{\nolinkurl{guillert@tcd.e}}) for any ideas you have about adding new features to the package (whether you have them already implemented or not)! + +\hypertarget{morphometric-geometric-demo-a-between-group-analysis}{% +\chapter{Morphometric geometric demo: a between group analysis}\label{morphometric-geometric-demo-a-between-group-analysis}} + +This demo aims to give quick overview of the \texttt{dispRity} package (v.1.7) for palaeobiology analyses of disparity, including disparity through time analyses. + +This demo showcases a typical between groups geometric morphometric analysis: we are going to test whether the disparity in two species of salamander (plethodons!) are different and in which ways they are different. + +\hypertarget{before-starting-1}{% +\section{Before starting}\label{before-starting-1}} + +Here we are going to use the \texttt{geomorph} \texttt{plethodon} dataset that is a set of 12 2D landmark coordinates for 40 specimens from two species of salamanders. +This section will really quickly cover how to make a Procrustes sumperimposition analysis and create a \texttt{geomorph} data.frame to have data ready for the \texttt{dispRity} package. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Loading geomorph} +\KeywordTok{library}\NormalTok{(geomorph)} + +\CommentTok{\#\# Loading the plethodon dataset} +\KeywordTok{data}\NormalTok{(plethodon)} + +\CommentTok{\#\# Running a simple Procrustes superimposition} +\NormalTok{gpa\_plethodon \textless{}{-}}\StringTok{ }\KeywordTok{gpagen}\NormalTok{(plethodon}\OperatorTok{$}\NormalTok{land)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## +## Performing GPA +## | | | 0% | |================== | 25% | |=================================== | 50% | |======================================================================| 100% +## +## Making projections... Finished! +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Making a geomorph data frame object with the species and sites attributes} +\NormalTok{gdf\_plethodon \textless{}{-}}\StringTok{ }\KeywordTok{geomorph.data.frame}\NormalTok{(gpa\_plethodon,} + \DataTypeTok{species =}\NormalTok{ plethodon}\OperatorTok{$}\NormalTok{species,} + \DataTypeTok{site =}\NormalTok{ plethodon}\OperatorTok{$}\NormalTok{site)} +\end{Highlighting} +\end{Shaded} + +You can of course use your very own landmark coordinates dataset (though you will have to do some modifications in the scripts that will come below - they will be easy though!). + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# You can replace the gdf\_plethodon by your own geomorph data frame!} +\NormalTok{my\_geomorph\_data \textless{}{-}}\StringTok{ }\NormalTok{gdf\_plethodon} +\end{Highlighting} +\end{Shaded} + +\hypertarget{the-morphospace-1}{% +\subsection{The morphospace}\label{the-morphospace-1}} + +The first step of every disparity analysis is to define your morphospace. + +\begin{quote} +Note that this is actually not true at all and kept as a erroneous sentence: the first step of your disparity analysis should be to define your question! +\end{quote} + +Our question here will be: is there a difference in disparity between the different species of salamanders and between the different sites (allopatric and sympatric)? + +OK, now we can go to the \emph{second} step of every disparity analysis: defining the morphospace. +Here we will define it with the ordination of all possible Procrustes superimposed plethodon landmark coordinates. +You can do this directly in \texttt{dispRity} using the \texttt{geomorph.ordination} function that can input a geomorph data frame: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# The morphospace} +\NormalTok{morphospace \textless{}{-}}\StringTok{ }\KeywordTok{geomorph.ordination}\NormalTok{(gdf\_plethodon)} +\end{Highlighting} +\end{Shaded} + +This automatically generates a \texttt{dispRity} object with the information of each groups. You can find more information about \texttt{dispRity} objects \protect\hyperlink{disprity-object}{here} but basically it summarises the content of your object without spamming your R console and is associated with many utility functions like \texttt{summary} or \texttt{plot}. For example here you can quickly visualise the two first dimensions of your space using the \texttt{plot} function: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# The dispRity object} +\NormalTok{morphospace} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## ---- dispRity object ---- +## 4 customised subsets for 40 elements in one matrix: +## species.Jord, species.Teyah, site.Allo, site.Symp. +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Plotting the morphospace} +\KeywordTok{plot}\NormalTok{(morphospace)} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-234-1.pdf} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Note that this only displays the two last groups (site.Allo and site.Symp) since they overlap!} +\end{Highlighting} +\end{Shaded} + +The \texttt{dispRity} package function comes with a lot of documentation of examples so don't hesitate to type \texttt{plot.dispRity} to check more plotting options. + +\hypertarget{calculating-disparity-2}{% +\section{Calculating disparity}\label{calculating-disparity-2}} + +Now that we have our morphospace, we can think about what we want to measure. +Two aspects of disparity that would be interesting for our question (is there a difference in disparity between the different species of salamanders and between the different sites?) would be the differences in size in the morphospace (do both groups occupy the same amount of morphospace) and position in the morphospace (do the do groups occupy the same position in the morphospace?). + +To choose which metric would cover best these two aspects, please check the \citet{moms} paper and associated \href{https://tguillerme.shinyapps.io/moms/}{app}. Here we are going to use the procrustes variance (\texttt{geomorph::morphol.disparity}) for measuring the size of the trait space and the average displacements \citep{moms} for the position in the trait space. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Defining a the procrustes variance metric} +\CommentTok{\#\# (as in geomorph::morphol.disparity)} +\NormalTok{proc.var \textless{}{-}}\StringTok{ }\ControlFlowTok{function}\NormalTok{(matrix) \{}\KeywordTok{sum}\NormalTok{(matrix}\OperatorTok{\^{}}\DecValTok{2}\NormalTok{)}\OperatorTok{/}\KeywordTok{nrow}\NormalTok{(matrix)\}} +\end{Highlighting} +\end{Shaded} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# The size metric} +\NormalTok{test\_size \textless{}{-}}\StringTok{ }\KeywordTok{test.metric}\NormalTok{(morphospace, }\DataTypeTok{metric =}\NormalTok{ proc.var,} + \DataTypeTok{shifts =} \KeywordTok{c}\NormalTok{(}\StringTok{"random"}\NormalTok{, }\StringTok{"size"}\NormalTok{))} +\KeywordTok{plot}\NormalTok{(test\_size)} +\KeywordTok{summary}\NormalTok{(test\_size)} + +\CommentTok{\#\# The position metric} +\NormalTok{test\_position \textless{}{-}}\StringTok{ }\KeywordTok{test.metric}\NormalTok{(morphospace, }\DataTypeTok{metric =} \KeywordTok{c}\NormalTok{(mean, displacements),} + \DataTypeTok{shifts =} \KeywordTok{c}\NormalTok{(}\StringTok{"random"}\NormalTok{, }\StringTok{"position"}\NormalTok{))} +\KeywordTok{plot}\NormalTok{(test\_position)} +\KeywordTok{summary}\NormalTok{(test\_position)} +\end{Highlighting} +\end{Shaded} + +You can see \protect\hyperlink{test-metric}{here} for more details on the \texttt{test.metric} function but basically these graphs are showing that there is a relation between changes in size and in position for each metric. +Note that there are some caveats here but the selection of the metric is just for the sake of the example! + +Note also the format of defining the disparity metrics here using \texttt{metric\ =\ c(mean,\ displacements)} or \texttt{metric\ =\ proc.var}. This is a core bit of the \texttt{dispRity} package were you can define your own metric as a function or a set of functions. You can find more info about this in the \protect\hyperlink{disparity-metrics}{\texttt{dispRity} metric section} but in brief, the \texttt{dispRity} package considers metrics by their ``dimensions'' level which corresponds to what they output. For example, the function \texttt{mean} is a dimension level 1 function because no matter the input it outputs a single value (the mean), \texttt{displacements} on the other hand is a dimension level 2 function because it will output the ratio between the distance from the centroid and from the centre of the trait space for each row in a matrix (an example of a dimensions level 3 would be the function \texttt{var} that outputs a matrix). +The \texttt{dispRity} package always automatically sorts the dimensions levels: it will always run dimensions level 3 \textgreater{} dimensions level 2 \textgreater{} and dimensions level 1. In this case both \texttt{c(mean,\ displacements)} and \texttt{c(mean,\ displacements)} will result in actually running \texttt{mean(displacements(matrix))}. +Alternatively you can define your metric prior to the disparity analysis like we did for the \texttt{proc.var} function. + +Anyways, we can measure disparity using these two metrics on all the groups as follows: + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Bootstrapped disparity} +\NormalTok{disparity\_size \textless{}{-}}\StringTok{ }\KeywordTok{dispRity}\NormalTok{(}\KeywordTok{boot.matrix}\NormalTok{(morphospace), }\DataTypeTok{metric =}\NormalTok{ proc.var)} +\NormalTok{disparity\_position \textless{}{-}}\StringTok{ }\KeywordTok{dispRity}\NormalTok{(}\KeywordTok{boot.matrix}\NormalTok{(morphospace), }\DataTypeTok{metric =} \KeywordTok{c}\NormalTok{(mean, displacements))} +\end{Highlighting} +\end{Shaded} + +Note that here we use the \texttt{boot.matrix} function for quickly bootstrapping the matrix. +This is not an essential step in this kind of analysis but it allows to ``reduce'' the effect of outliers and create a distribution of disparity measures (rather than single point estimates). + +\hypertarget{analyse-the-results}{% +\section{Analyse the results}\label{analyse-the-results}} + +We can visualise the results using the \texttt{plot} function on the resulting disparity objects (or summarising them using \texttt{summary}): + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Plotting the results} +\KeywordTok{par}\NormalTok{(}\DataTypeTok{mfrow =} \KeywordTok{c}\NormalTok{(}\DecValTok{1}\NormalTok{,}\DecValTok{2}\NormalTok{))} +\KeywordTok{plot}\NormalTok{(disparity\_size, }\DataTypeTok{main =} \StringTok{"group sizes"}\NormalTok{, }\DataTypeTok{las =} \DecValTok{2}\NormalTok{, }\DataTypeTok{xlab =} \StringTok{""}\NormalTok{)} +\KeywordTok{plot}\NormalTok{(disparity\_position, }\DataTypeTok{main =} \StringTok{"group positions"}\NormalTok{, }\DataTypeTok{las =} \DecValTok{2}\NormalTok{, }\DataTypeTok{xlab =} \StringTok{""}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\includegraphics{dispRity_manual_files/figure-latex/unnamed-chunk-238-1.pdf} + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Summarising the results} +\KeywordTok{summary}\NormalTok{(disparity\_size)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## subsets n obs bs.median 2.5% 25% 75% 97.5% +## 1 species.Jord 20 0.005 0.005 0.004 0.005 0.005 0.006 +## 2 species.Teyah 20 0.005 0.005 0.004 0.005 0.005 0.006 +## 3 site.Allo 20 0.004 0.004 0.003 0.003 0.004 0.004 +## 4 site.Symp 20 0.006 0.006 0.006 0.006 0.006 0.007 +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\KeywordTok{summary}\NormalTok{(disparity\_position)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## subsets n obs bs.median 2.5% 25% 75% 97.5% +## 1 species.Jord 20 1.096 1.122 1.067 1.101 1.171 1.380 +## 2 species.Teyah 20 1.070 1.105 1.033 1.065 1.143 1.345 +## 3 site.Allo 20 1.377 1.407 1.315 1.381 1.448 1.530 +## 4 site.Symp 20 1.168 1.221 1.148 1.187 1.269 1.458 +\end{verbatim} + +Just from looking at the data, we can guess that there is not much difference in terms of morphospace occupancy and position for the species but there is on for the sites (allopatric or sympatric). +We can test it using a simple non-parametric mean difference test (e.g.~\texttt{wilcox.test}) using the \texttt{dispRity} package. + +\begin{Shaded} +\begin{Highlighting}[] +\CommentTok{\#\# Testing the differences} +\KeywordTok{test.dispRity}\NormalTok{(disparity\_size, }\DataTypeTok{test =}\NormalTok{ wilcox.test, }\DataTypeTok{correction =} \StringTok{"bonferroni"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [[1]] +## statistic: W +## species.Jord : species.Teyah 3803 +## species.Jord : site.Allo 9922 +## species.Jord : site.Symp 14 +## species.Teyah : site.Allo 9927 +## species.Teyah : site.Symp 238 +## site.Allo : site.Symp 0 +## +## [[2]] +## p.value +## species.Jord : species.Teyah 2.076623e-02 +## species.Jord : site.Allo 1.572891e-32 +## species.Jord : site.Symp 2.339811e-33 +## species.Teyah : site.Allo 1.356528e-32 +## species.Teyah : site.Symp 1.657077e-30 +## site.Allo : site.Symp 1.537286e-33 +\end{verbatim} + +\begin{Shaded} +\begin{Highlighting}[] +\KeywordTok{test.dispRity}\NormalTok{(disparity\_position, }\DataTypeTok{test =}\NormalTok{ wilcox.test, }\DataTypeTok{correction =} \StringTok{"bonferroni"}\NormalTok{)} +\end{Highlighting} +\end{Shaded} + +\begin{verbatim} +## [[1]] +## statistic: W +## species.Jord : species.Teyah 6536 +## species.Jord : site.Allo 204 +## species.Jord : site.Symp 1473 +## species.Teyah : site.Allo 103 +## species.Teyah : site.Symp 1042 +## site.Allo : site.Symp 9288 +## +## [[2]] +## p.value +## species.Jord : species.Teyah 1.053318e-03 +## species.Jord : site.Allo 6.238014e-31 +## species.Jord : site.Symp 4.137900e-17 +## species.Teyah : site.Allo 3.289139e-32 +## species.Teyah : site.Symp 2.433117e-21 +## site.Allo : site.Symp 6.679158e-25 +\end{verbatim} + +So by applying the tests we see a difference in terms of position between each groups and differences in size between groups but between the species. + +\hypertarget{disprity-r-package-manual}{% +\chapter{dispRity R package manual}\label{disprity-r-package-manual}} + +\hypertarget{references}{% +\chapter{References}\label{references}} + \bibliography{../References.bib,../packages.bib} \end{document} diff --git a/inst/gitbook/_book/dispRity_manual_files/figure-html/plot2-1.png b/inst/gitbook/_book/dispRity_manual_files/figure-html/plot2-1.png index f5d6288a..d716d67f 100644 Binary files a/inst/gitbook/_book/dispRity_manual_files/figure-html/plot2-1.png and b/inst/gitbook/_book/dispRity_manual_files/figure-html/plot2-1.png differ diff --git a/inst/gitbook/_book/dispRity_manual_files/figure-html/plot3-1.png b/inst/gitbook/_book/dispRity_manual_files/figure-html/plot3-1.png index 4a3e8f2a..5997c799 100644 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--git a/inst/gitbook/_book/ecology-demo.html b/inst/gitbook/_book/disprity-ecology-demo.html similarity index 66% rename from inst/gitbook/_book/ecology-demo.html rename to inst/gitbook/_book/disprity-ecology-demo.html index 351e7c04..fecb274d 100644 --- a/inst/gitbook/_book/ecology-demo.html +++ b/inst/gitbook/_book/disprity-ecology-demo.html @@ -4,26 +4,26 @@ - 8 Ecology demo | Morphometric geometric demo: a between group analysis - - + 8 dispRity ecology demo | dispRity R package manual + + - + - + - + - + - + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -325,23 +358,23 @@

    -
    -

    8 Ecology demo

    +
    +

    8 dispRity ecology demo

    This is an example of typical disparity analysis that can be performed in ecology.

    -

    8.1 Data

    +

    8.1 Data

    For this example, we will use the famous iris inbuilt data set

    -
    data(iris)
    +
    data(iris)

    This data contains petal and sepal length for 150 individual plants sorted into three species.

    -
    ## Separating the species
-species <- iris[,5]
-## Which species?
-unique(species)
    +
    ## Separating the species
+species <- iris[,5]
+## Which species?
+unique(species)
    ## [1] setosa     versicolor virginica 
 ## Levels: setosa versicolor virginica
    -
    ## Separating the petal/sepal length
-measurements <- iris[,1:4]
-head(measurements)
    +
    ## Separating the petal/sepal length
+measurements <- iris[,1:4]
+head(measurements)
    ##   Sepal.Length Sepal.Width Petal.Length Petal.Width
 ## 1          5.1         3.5          1.4         0.2
 ## 2          4.9         3.0          1.4         0.2
@@ -350,80 +383,80 @@ 
    8.1 Data
    ## Ordinating the data
-ordination <- prcomp(measurements)
-
-## The petal-space
-petal_space <- ordination$x
-
-## Adding the elements names to the petal-space (the individuals IDs)
-rownames(petal_space) <- 1:nrow(petal_space)
    +
    ## Ordinating the data
+ordination <- prcomp(measurements)
+
+## The petal-space
+petal_space <- ordination$x
+
+## Adding the elements names to the petal-space (the individuals IDs)
+rownames(petal_space) <- 1:nrow(petal_space)
    -

    8.2 Classic analysis

    +

    8.2 Classic analysis

    A classical way to represent this ordinated data would be to use two dimensional plots to look at how the different species are distributed in the petal-space.

    -
    ## Measuring the variance on each axis
-axis_variances <- apply(petal_space, 2, var)
-axis_variances <- axis_variances/sum(axis_variances)
-
-## Graphical option
-par(bty = "n")
-
-## A classic 2D ordination plot
-plot(petal_space[, 1], petal_space[, 2], col = species,
-    xlab = paste0("PC 1 (", round(axis_variances[1], 2), ")"),
-    ylab = paste0("PC 2 (", round(axis_variances[2], 2), ")"))
    -

    +
    ## Measuring the variance on each axis
+axis_variances <- apply(petal_space, 2, var)
+axis_variances <- axis_variances/sum(axis_variances)
+
+## Graphical option
+par(bty = "n")
+
+## A classic 2D ordination plot
+plot(petal_space[, 1], petal_space[, 2], col = species,
+    xlab = paste0("PC 1 (", round(axis_variances[1], 2), ")"),
+    ylab = paste0("PC 2 (", round(axis_variances[2], 2), ")"))
    +

    This shows the distribution of the different species in the petal-space along the two first axis of variation. This is a pretty standard way to visualise the multidimensional space and further analysis might be necessary to test wether the groups are different such as a linear discriminant analysis (LDA). However, in this case we are ignoring the two other dimensions of the ordination! If we look at the two other axis we see a totally different result:

    -
    ## Plotting the two second axis of the petal-space
-plot(petal_space[, 3], petal_space[, 4], col = species,
-    xlab = paste0("PC 3 (", round(axis_variances[3], 2), ")"),
-    ylab = paste0("PC 4 (", round(axis_variances[4], 2), ")"))
    -

    +
    ## Plotting the two second axis of the petal-space
+plot(petal_space[, 3], petal_space[, 4], col = species,
+    xlab = paste0("PC 3 (", round(axis_variances[3], 2), ")"),
+    ylab = paste0("PC 4 (", round(axis_variances[4], 2), ")"))
    +

    Additionally, these two represented dimensions do not represent a biological reality per se; i.e. the values on the first dimension do not represent a continuous trait (e.g. petal length), instead they just represent the ordinations of correlations between the data and some factors.

    Therefore, we might want to approach this problem without getting stuck in only two dimensions and consider the whole dataset as a n-dimensional object.

    -

    8.3 A multidimensional approach with dispRity

    +

    8.3 A multidimensional approach with dispRity

    The first step is to create different subsets that represent subsets of the ordinated space (i.e. sub-regions within the n-dimensional object). Each of these subsets will contain only the individuals of a specific species.

    -
    ## Creating the table that contain the elements and their attributes
-petal_subsets <- custom.subsets(petal_space, group = list(
-                                "setosa" = which(species == "setosa"),
-                                "versicolor" = which(species == "versicolor"),
-                                "virginica" = which(species == "virginica")))
-
-## Visualising the dispRity object content
-petal_subsets
    +
    ## Creating the table that contain the elements and their attributes
+petal_subsets <- custom.subsets(petal_space, group = list(
+                                "setosa" = which(species == "setosa"),
+                                "versicolor" = which(species == "versicolor"),
+                                "virginica" = which(species == "virginica")))
+
+## Visualising the dispRity object content
+petal_subsets
    ##  ---- dispRity object ---- 
 ## 3 customised subsets for 150 elements in one matrix:
 ##     setosa, versicolor, virginica.

    This created a dispRity object (more about that here) with three subsets corresponding to each subspecies.

    -

    8.3.1 Bootstrapping the data

    +

    8.3.1 Bootstrapping the data

    We can the bootstrap the subsets to be able test the robustness of the measured disparity to outliers. We can do that using the default options of boot.matrix (more about that here):

    -
    ## Bootstrapping the data
-(petal_bootstrapped <- boot.matrix(petal_subsets))
    +
    ## Bootstrapping the data
+(petal_bootstrapped <- boot.matrix(petal_subsets))
    ##  ---- dispRity object ---- 
 ## 3 customised subsets for 150 elements in one matrix with 4 dimensions:
 ##     setosa, versicolor, virginica.
 ## Data was bootstrapped 100 times (method:"full").
    -

    8.3.2 Calculating disparity

    +

    8.3.2 Calculating disparity

    Disparity can be calculated in many ways, therefore the dispRity function allows users to define their own measure of disparity. For more details on measuring disparity, see the dispRity metrics section.

    In this example, we are going to define disparity as the median distance between the different individuals and the centroid of the ordinated space. High values of disparity will indicate a generally high spread of points from this centroid (i.e. on average, the individuals are far apart in the ordinated space). We can define the metrics easily in the dispRity function by feeding them to the metric argument. Here we are going to feed the functions stats::median and dispRity::centroids which calculates distances between elements and their centroid.

    -
    ## Calculating disparity as the median distance between each elements and
-## the centroid of the petal-space
-(petal_disparity <- dispRity(petal_bootstrapped, metric = c(median, centroids)))
    +
    ## Calculating disparity as the median distance between each elements and
+## the centroid of the petal-space
+(petal_disparity <- dispRity(petal_bootstrapped, metric = c(median, centroids)))
    ##  ---- dispRity object ---- 
 ## 3 customised subsets for 150 elements in one matrix with 4 dimensions:
 ##     setosa, versicolor, virginica.
@@ -431,30 +464,30 @@ 
    8.3.2 Calculating disparity
    -

    8.3.3 Summarising the results (plot)

    +

    8.3.3 Summarising the results (plot)

    Similarly to the custom.subsets and boot.matrix function, dispRity displays a dispRity object. But we are definitely more interested in actually look at the calculated values.

    First we can summarise the data in a table by simply using summary:

    -
    ## Displaying the summary of the calculated disparity
-summary(petal_disparity)
    +
    ## Displaying the summary of the calculated disparity
+summary(petal_disparity)
    ##      subsets  n   obs bs.median  2.5%   25%   75% 97.5%
-## 1     setosa 50 0.421     0.430 0.355 0.409 0.452 0.509
-## 2 versicolor 50 0.693     0.662 0.537 0.635 0.699 0.751
-## 3  virginica 50 0.785     0.723 0.544 0.650 0.780 0.883
    +## 1 setosa 50 0.421 0.432 0.370 0.408 0.454 0.501 +## 2 versicolor 50 0.693 0.656 0.511 0.619 0.697 0.770 +## 3 virginica 50 0.785 0.747 0.580 0.674 0.806 0.936

    We can also plot the results in a similar way:

    -
    ## Graphical options
-par(bty = "n")
-
-## Plotting the disparity in the petal_space
-plot(petal_disparity)
    -

    +
    ## Graphical options
+par(bty = "n")
+
+## Plotting the disparity in the petal_space
+plot(petal_disparity)
    +

    Now contrary to simply plotting the two first axis of the PCA where we saw that the species have a different position in the two first petal-space, we can now also see that they occupy this space clearly differently!

    -

    8.3.4 Testing hypothesis

    +

    8.3.4 Testing hypothesis

    Finally we can test our hypothesis that we guessed from the disparity plot (that some groups occupy different volume of the petal-space) by using the test.dispRity option.

    -
    ## Running a PERMANOVA
-test.dispRity(petal_disparity, test = adonis.dispRity)
    +
    ## Running a PERMANOVA
+test.dispRity(petal_disparity, test = adonis.dispRity)
    ## Warning in test.dispRity(petal_disparity, test = adonis.dispRity): adonis.dispRity test will be applied to the data matrix, not to the calculated disparity.
 ## See ?adonis.dispRity for more details.
    ## Warning in adonis.dispRity(data, ...): The input data for adonis.dispRity was not a distance matrix.
@@ -472,46 +505,46 @@ 
    8.3.4 Testing hypothesis
    ## Post-hoc testing of the differences between species (corrected for multiple tests)
-test.dispRity(petal_disparity, test = t.test, correction = "bonferroni")
    +
    ## Post-hoc testing of the differences between species (corrected for multiple tests)
+test.dispRity(petal_disparity, test = t.test, correction = "bonferroni")
    ## [[1]]
 ##                        statistic: t
-## setosa : versicolor      -34.301682
-## setosa : virginica       -29.604472
-## versicolor : virginica    -5.773033
+## setosa : versicolor      -29.998366
+## setosa : virginica       -30.465933
+## versicolor : virginica    -7.498179
 ## 
 ## [[2]]
 ##                        parameter: df
-## setosa : versicolor         183.5486
-## setosa : virginica          137.4942
-## versicolor : virginica      162.1354
+## setosa : versicolor         149.8429
+## setosa : virginica          124.4227
+## versicolor : virginica      175.4758
 ## 
 ## [[3]]
 ##                             p.value
-## setosa : versicolor    2.812033e-81
-## setosa : virginica     4.865500e-61
-## versicolor : virginica 1.158407e-07
+## setosa : versicolor    9.579095e-65
+## setosa : virginica     4.625567e-59
+## versicolor : virginica 9.247421e-12
 ## 
 ## [[4]]
 ##                             stderr
-## setosa : versicolor    0.006679214
-## setosa : virginica     0.009764192
-## versicolor : virginica 0.010385437
    +## setosa : versicolor 0.007378905 +## setosa : virginica 0.010103449 +## versicolor : virginica 0.011530255

    We can now see that there is a significant difference in petal-space occupancy between all species of iris.

    -

    8.3.4.1 Setting up a multidimensional null-hypothesis

    +

    8.3.4.1 Setting up a multidimensional null-hypothesis

    One other series of test can be done on the shape of the petal-space. Using a MCMC permutation test we can simulate a petal-space with specific properties and see if our observed petal-space matches these properties (similarly to Dı́az et al. (2016)):

    -
    ## Testing against a uniform distribution
-disparity_uniform <- null.test(petal_disparity, replicates = 200,
-    null.distrib = runif, scale = FALSE)
-plot(disparity_uniform)
    -

    -
    ## Testing against a normal distribution
-disparity_normal <- null.test(petal_disparity, replicates = 200,
-    null.distrib = rnorm, scale = TRUE)
-plot(disparity_normal)
    -

    +
    ## Testing against a uniform distribution
+disparity_uniform <- null.test(petal_disparity, replicates = 200,
+    null.distrib = runif, scale = FALSE)
+plot(disparity_uniform)
    +

    +
    ## Testing against a normal distribution
+disparity_normal <- null.test(petal_disparity, replicates = 200,
+    null.distrib = rnorm, scale = TRUE)
+plot(disparity_normal)
    +

    In both cases we can see that our petal-space is not entirely normal or uniform. This is expected because of the simplicity of these parameters.

    @@ -519,7 +552,7 @@

    8.3.4.1 Setting up a multidimensi

    -

    References

    +

    References

    Dı́az, Sandra, Jens Kattge, Johannes HC Cornelissen, Ian J Wright, Sandra Lavorel, Stéphane Dray, Björn Reu, et al. 2016. “The Global Spectrum of Plant Form and Function.” Nature 529 (7585): 167. http://dx.doi.org/10.1038/nature16489.

    @@ -591,7 +624,7 @@

    References + + + + + + 11 dispRity R package manual | dispRity R package manual + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
    +
    + + +
    +
    + +
    +
    +

    11 dispRity R package manual

    +
    +
    + +
    +
    +
    + + +
    +
    + + + + + + + + + + + + + + + diff --git a/inst/gitbook/_book/getting-started-with-disprity.html b/inst/gitbook/_book/getting-started-with-disprity.html index 43b23e25..bd38ab61 100644 --- a/inst/gitbook/_book/getting-started-with-disprity.html +++ b/inst/gitbook/_book/getting-started-with-disprity.html @@ -4,26 +4,26 @@ - 3 Getting started with dispRity | Morphometric geometric demo: a between group analysis - - + 3 Getting started with dispRity | dispRity R package manual + + - + - + - + - + - + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -386,10 +419,10 @@

    3.2.2 Ordination matrices from ## Ordinating the example data from Claddis Claddis.ordination(michaux_1989)

    ##                      [,1]          [,2]       [,3]
-## Ancilla     -5.237743e-17 -4.154578e-01  0.2534942
-## Turrancilla -5.106645e-01  7.175378e-16 -0.2534942
-## Ancillista   5.106645e-01  5.544611e-16 -0.2534942
-## Amalda       1.603581e-16  4.154578e-01  0.2534942
    +## Ancilla 0.000000e+00 4.154578e-01 0.2534942 +## Turrancilla -5.106645e-01 -1.304614e-16 -0.2534942 +## Ancillista 5.106645e-01 -1.630768e-17 -0.2534942 +## Amalda 1.603581e-16 -4.154578e-01 0.2534942

    Note that several options are available, namely which type of distance should be computed. See more info in the function manual (?Claddis.ordination). Alternatively, it is of course also possible to manual calculate the ordination matrix using the functions Claddis::calculate_morphological_distances and stats::cmdscale.

    @@ -615,7 +648,7 @@

    3.3.3 Disparity among groups

    -

    References

    +

    References

    Beck, Robin M, and Michael S Lee. 2014. “Ancient Dates or Accelerated Rates? Morphological Clocks and the Antiquity of Placental Mammals.” Proceedings of the Royal Society B: Biological Sciences 281 (20141278): 1–10. https://doi.org/10.1098/rspb.2014.1278.

    @@ -687,7 +720,7 @@

    References - 2 Glossary | Morphometric geometric demo: a between group analysis - - + 2 Glossary | dispRity R package manual + + - + - + - + - + - + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -458,7 +491,7 @@

    2.1 Glossary equivalences in pala var script = document.createElement("script"); script.type = "text/javascript"; var src = "true"; - if (src === "" || src === "true") src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-MML-AM_CHTML"; + if (src === "" || src === "true") src = "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.9/latest.js?config=TeX-MML-AM_CHTML"; if (location.protocol !== "file:") if (/^https?:/.test(src)) src = src.replace(/^https?:/, ''); diff --git a/inst/gitbook/_book/index.html b/inst/gitbook/_book/index.html index 882e6bf7..31614f86 100644 --- a/inst/gitbook/_book/index.html +++ b/inst/gitbook/_book/index.html @@ -4,26 +4,26 @@ - Morphometric geometric demo: a between group analysis - - + dispRity R package manual + + - + - + - + - + - + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -326,9 +359,9 @@

    1 dispRity

    @@ -513,7 +546,7 @@

    1.6.1 Why is it important to cite var script = document.createElement("script"); script.type = "text/javascript"; var src = "true"; - if (src === "" || src === "true") src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-MML-AM_CHTML"; + if (src === "" || src === "true") src = "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.9/latest.js?config=TeX-MML-AM_CHTML"; if (location.protocol !== "file:") if (/^https?:/.test(src)) src = src.replace(/^https?:/, ''); diff --git a/inst/gitbook/_book/libs/CanvasMatrix4-1.2.1/CanvasMatrix.src.js b/inst/gitbook/_book/libs/CanvasMatrix4-1.2.1/CanvasMatrix.src.js new file mode 100644 index 00000000..2bfbd588 --- /dev/null +++ b/inst/gitbook/_book/libs/CanvasMatrix4-1.2.1/CanvasMatrix.src.js @@ -0,0 +1,729 @@ +/* globals CanvasMatrix4: true */ +/* globals WebGLFloatArray */ +/* jshint eqeqeq: false */ +/* + * Copyright (C) 2009 Apple Inc. All Rights Reserved. + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions + * are met: + * 1. Redistributions of source code must retain the above copyright + * notice, this list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright + * notice, this list of conditions and the following disclaimer in the + * documentation and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY APPLE INC. ``AS IS'' AND ANY + * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR + * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL APPLE INC. OR + * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY + * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + * Copyright (2016) Duncan Murdoch - fixed CanvasMatrix4.ortho, + * cleaned up. + */ +/* + CanvasMatrix4 class + + This class implements a 4x4 matrix. It has functions which + duplicate the functionality of the OpenGL matrix stack and + glut functions. + + IDL: + + [ + Constructor(in CanvasMatrix4 matrix), // copy passed matrix into new CanvasMatrix4 + Constructor(in sequence array) // create new CanvasMatrix4 with 16 floats (row major) + Constructor() // create new CanvasMatrix4 with identity matrix + ] + interface CanvasMatrix4 { + attribute float m11; + attribute float m12; + attribute float m13; + attribute float m14; + attribute float m21; + attribute float m22; + attribute float m23; + attribute float m24; + attribute float m31; + attribute float m32; + attribute float m33; + attribute float m34; + attribute float m41; + attribute float m42; + attribute float m43; + attribute float m44; + + void load(in CanvasMatrix4 matrix); // copy the values from the passed matrix + void load(in sequence array); // copy 16 floats into the matrix + sequence getAsArray(); // return the matrix as an array of 16 floats + WebGLFloatArray getAsCanvasFloatArray(); // return the matrix as a WebGLFloatArray with 16 values + void makeIdentity(); // replace the matrix with identity + void transpose(); // replace the matrix with its transpose + void invert(); // replace the matrix with its inverse + + void translate(in float x, in float y, in float z); // multiply the matrix by passed translation values on the right + void scale(in float x, in float y, in float z); // multiply the matrix by passed scale values on the right + void rotate(in float angle, // multiply the matrix by passed rotation values on the right + in float x, in float y, in float z); // (angle is in degrees) + void multRight(in CanvasMatrix matrix); // multiply the matrix by the passed matrix on the right + void multLeft(in CanvasMatrix matrix); // multiply the matrix by the passed matrix on the left + void ortho(in float left, in float right, // multiply the matrix by the passed ortho values on the right + in float bottom, in float top, + in float near, in float far); + void frustum(in float left, in float right, // multiply the matrix by the passed frustum values on the right + in float bottom, in float top, + in float near, in float far); + void perspective(in float fovy, in float aspect, // multiply the matrix by the passed perspective values on the right + in float zNear, in float zFar); + void lookat(in float eyex, in float eyey, in float eyez, // multiply the matrix by the passed lookat + in float ctrx, in float ctry, in float ctrz, // values on the right + in float upx, in float upy, in float upz); + } +*/ + +CanvasMatrix4 = function(m) +{ + if (typeof m == 'object') { + if ("length" in m && m.length >= 16) { + this.load(m[0], m[1], m[2], m[3], m[4], m[5], m[6], m[7], m[8], m[9], m[10], m[11], m[12], m[13], m[14], m[15]); + return; + } + else if (m instanceof CanvasMatrix4) { + this.load(m); + return; + } + } + this.makeIdentity(); +}; + +CanvasMatrix4.prototype.load = function() +{ + if (arguments.length == 1 && typeof arguments[0] == 'object') { + var matrix = arguments[0]; + + if ("length" in matrix && matrix.length == 16) { + this.m11 = matrix[0]; + this.m12 = matrix[1]; + this.m13 = matrix[2]; + this.m14 = matrix[3]; + + this.m21 = matrix[4]; + this.m22 = matrix[5]; + this.m23 = matrix[6]; + this.m24 = matrix[7]; + + this.m31 = matrix[8]; + this.m32 = matrix[9]; + this.m33 = matrix[10]; + this.m34 = matrix[11]; + + this.m41 = matrix[12]; + this.m42 = matrix[13]; + this.m43 = matrix[14]; + this.m44 = matrix[15]; + return; + } + + if (arguments[0] instanceof CanvasMatrix4) { + + this.m11 = matrix.m11; + this.m12 = matrix.m12; + this.m13 = matrix.m13; + this.m14 = matrix.m14; + + this.m21 = matrix.m21; + this.m22 = matrix.m22; + this.m23 = matrix.m23; + this.m24 = matrix.m24; + + this.m31 = matrix.m31; + this.m32 = matrix.m32; + this.m33 = matrix.m33; + this.m34 = matrix.m34; + + this.m41 = matrix.m41; + this.m42 = matrix.m42; + this.m43 = matrix.m43; + this.m44 = matrix.m44; + return; + } + } + + this.makeIdentity(); +}; + +CanvasMatrix4.prototype.getAsArray = function() +{ + return [ + this.m11, this.m12, this.m13, this.m14, + this.m21, this.m22, this.m23, this.m24, + this.m31, this.m32, this.m33, this.m34, + this.m41, this.m42, this.m43, this.m44 + ]; +}; + +CanvasMatrix4.prototype.getAsWebGLFloatArray = function() +{ + return new WebGLFloatArray(this.getAsArray()); +}; + +CanvasMatrix4.prototype.makeIdentity = function() +{ + this.m11 = 1; + this.m12 = 0; + this.m13 = 0; + this.m14 = 0; + + this.m21 = 0; + this.m22 = 1; + this.m23 = 0; + this.m24 = 0; + + this.m31 = 0; + this.m32 = 0; + this.m33 = 1; + this.m34 = 0; + + this.m41 = 0; + this.m42 = 0; + this.m43 = 0; + this.m44 = 1; +}; + +CanvasMatrix4.prototype.transpose = function() +{ + var tmp = this.m12; + this.m12 = this.m21; + this.m21 = tmp; + + tmp = this.m13; + this.m13 = this.m31; + this.m31 = tmp; + + tmp = this.m14; + this.m14 = this.m41; + this.m41 = tmp; + + tmp = this.m23; + this.m23 = this.m32; + this.m32 = tmp; + + tmp = this.m24; + this.m24 = this.m42; + this.m42 = tmp; + + tmp = this.m34; + this.m34 = this.m43; + this.m43 = tmp; +}; + +CanvasMatrix4.prototype.invert = function() +{ + // Calculate the 4x4 determinant + // If the determinant is zero, + // then the inverse matrix is not unique. + var det = this._determinant4x4(); + + if (Math.abs(det) < 1e-8) + return null; + + this._makeAdjoint(); + + // Scale the adjoint matrix to get the inverse + this.m11 /= det; + this.m12 /= det; + this.m13 /= det; + this.m14 /= det; + + this.m21 /= det; + this.m22 /= det; + this.m23 /= det; + this.m24 /= det; + + this.m31 /= det; + this.m32 /= det; + this.m33 /= det; + this.m34 /= det; + + this.m41 /= det; + this.m42 /= det; + this.m43 /= det; + this.m44 /= det; +}; + +CanvasMatrix4.prototype.translate = function(x,y,z) +{ + if (x === undefined) + x = 0; + if (y === undefined) + y = 0; + if (z === undefined) + z = 0; + + var matrix = new CanvasMatrix4(); + matrix.m41 = x; + matrix.m42 = y; + matrix.m43 = z; + + this.multRight(matrix); +}; + +CanvasMatrix4.prototype.scale = function(x,y,z) +{ + if (x === undefined) + x = 1; + if (z === undefined) { + if (y === undefined) { + y = x; + z = x; + } + else + z = 1; + } + else if (y === undefined) + y = x; + + var matrix = new CanvasMatrix4(); + matrix.m11 = x; + matrix.m22 = y; + matrix.m33 = z; + + this.multRight(matrix); +}; + +CanvasMatrix4.prototype.rotate = function(angle,x,y,z) +{ + // angles are in degrees. Switch to radians + angle = angle / 180 * Math.PI; + + angle /= 2; + var sinA = Math.sin(angle); + var cosA = Math.cos(angle); + var sinA2 = sinA * sinA; + + // normalize + var length = Math.sqrt(x * x + y * y + z * z); + if (length === 0) { + // bad vector, just use something reasonable + x = 0; + y = 0; + z = 1; + } else if (length != 1) { + x /= length; + y /= length; + z /= length; + } + + var mat = new CanvasMatrix4(); + + // optimize case where axis is along major axis + if (x == 1 && y === 0 && z === 0) { + mat.m11 = 1; + mat.m12 = 0; + mat.m13 = 0; + mat.m21 = 0; + mat.m22 = 1 - 2 * sinA2; + mat.m23 = 2 * sinA * cosA; + mat.m31 = 0; + mat.m32 = -2 * sinA * cosA; + mat.m33 = 1 - 2 * sinA2; + mat.m14 = mat.m24 = mat.m34 = 0; + mat.m41 = mat.m42 = mat.m43 = 0; + mat.m44 = 1; + } else if (x === 0 && y == 1 && z === 0) { + mat.m11 = 1 - 2 * sinA2; + mat.m12 = 0; + mat.m13 = -2 * sinA * cosA; + mat.m21 = 0; + mat.m22 = 1; + mat.m23 = 0; + mat.m31 = 2 * sinA * cosA; + mat.m32 = 0; + mat.m33 = 1 - 2 * sinA2; + mat.m14 = mat.m24 = mat.m34 = 0; + mat.m41 = mat.m42 = mat.m43 = 0; + mat.m44 = 1; + } else if (x === 0 && y === 0 && z == 1) { + mat.m11 = 1 - 2 * sinA2; + mat.m12 = 2 * sinA * cosA; + mat.m13 = 0; + mat.m21 = -2 * sinA * cosA; + mat.m22 = 1 - 2 * sinA2; + mat.m23 = 0; + mat.m31 = 0; + mat.m32 = 0; + mat.m33 = 1; + mat.m14 = mat.m24 = mat.m34 = 0; + mat.m41 = mat.m42 = mat.m43 = 0; + mat.m44 = 1; + } else { + var x2 = x*x; + var y2 = y*y; + var z2 = z*z; + + mat.m11 = 1 - 2 * (y2 + z2) * sinA2; + mat.m12 = 2 * (x * y * sinA2 + z * sinA * cosA); + mat.m13 = 2 * (x * z * sinA2 - y * sinA * cosA); + mat.m21 = 2 * (y * x * sinA2 - z * sinA * cosA); + mat.m22 = 1 - 2 * (z2 + x2) * sinA2; + mat.m23 = 2 * (y * z * sinA2 + x * sinA * cosA); + mat.m31 = 2 * (z * x * sinA2 + y * sinA * cosA); + mat.m32 = 2 * (z * y * sinA2 - x * sinA * cosA); + mat.m33 = 1 - 2 * (x2 + y2) * sinA2; + mat.m14 = mat.m24 = mat.m34 = 0; + mat.m41 = mat.m42 = mat.m43 = 0; + mat.m44 = 1; + } + this.multRight(mat); +}; + +CanvasMatrix4.prototype.multRight = function(mat) +{ + var m11 = (this.m11 * mat.m11 + this.m12 * mat.m21 + + this.m13 * mat.m31 + this.m14 * mat.m41); + var m12 = (this.m11 * mat.m12 + this.m12 * mat.m22 + + this.m13 * mat.m32 + this.m14 * mat.m42); + var m13 = (this.m11 * mat.m13 + this.m12 * mat.m23 + + this.m13 * mat.m33 + this.m14 * mat.m43); + var m14 = (this.m11 * mat.m14 + this.m12 * mat.m24 + + this.m13 * mat.m34 + this.m14 * mat.m44); + + var m21 = (this.m21 * mat.m11 + this.m22 * mat.m21 + + this.m23 * mat.m31 + this.m24 * mat.m41); + var m22 = (this.m21 * mat.m12 + this.m22 * mat.m22 + + this.m23 * mat.m32 + this.m24 * mat.m42); + var m23 = (this.m21 * mat.m13 + this.m22 * mat.m23 + + this.m23 * mat.m33 + this.m24 * mat.m43); + var m24 = (this.m21 * mat.m14 + this.m22 * mat.m24 + + this.m23 * mat.m34 + this.m24 * mat.m44); + + var m31 = (this.m31 * mat.m11 + this.m32 * mat.m21 + + this.m33 * mat.m31 + this.m34 * mat.m41); + var m32 = (this.m31 * mat.m12 + this.m32 * mat.m22 + + this.m33 * mat.m32 + this.m34 * mat.m42); + var m33 = (this.m31 * mat.m13 + this.m32 * mat.m23 + + this.m33 * mat.m33 + this.m34 * mat.m43); + var m34 = (this.m31 * mat.m14 + this.m32 * mat.m24 + + this.m33 * mat.m34 + this.m34 * mat.m44); + + var m41 = (this.m41 * mat.m11 + this.m42 * mat.m21 + + this.m43 * mat.m31 + this.m44 * mat.m41); + var m42 = (this.m41 * mat.m12 + this.m42 * mat.m22 + + this.m43 * mat.m32 + this.m44 * mat.m42); + var m43 = (this.m41 * mat.m13 + this.m42 * mat.m23 + + this.m43 * mat.m33 + this.m44 * mat.m43); + var m44 = (this.m41 * mat.m14 + this.m42 * mat.m24 + + this.m43 * mat.m34 + this.m44 * mat.m44); + + this.m11 = m11; + this.m12 = m12; + this.m13 = m13; + this.m14 = m14; + + this.m21 = m21; + this.m22 = m22; + this.m23 = m23; + this.m24 = m24; + + this.m31 = m31; + this.m32 = m32; + this.m33 = m33; + this.m34 = m34; + + this.m41 = m41; + this.m42 = m42; + this.m43 = m43; + this.m44 = m44; +}; + +CanvasMatrix4.prototype.multLeft = function(mat) +{ + var m11 = (mat.m11 * this.m11 + mat.m12 * this.m21 + + mat.m13 * this.m31 + mat.m14 * this.m41); + var m12 = (mat.m11 * this.m12 + mat.m12 * this.m22 + + mat.m13 * this.m32 + mat.m14 * this.m42); + var m13 = (mat.m11 * this.m13 + mat.m12 * this.m23 + + mat.m13 * this.m33 + mat.m14 * this.m43); + var m14 = (mat.m11 * this.m14 + mat.m12 * this.m24 + + mat.m13 * this.m34 + mat.m14 * this.m44); + + var m21 = (mat.m21 * this.m11 + mat.m22 * this.m21 + + mat.m23 * this.m31 + mat.m24 * this.m41); + var m22 = (mat.m21 * this.m12 + mat.m22 * this.m22 + + mat.m23 * this.m32 + mat.m24 * this.m42); + var m23 = (mat.m21 * this.m13 + mat.m22 * this.m23 + + mat.m23 * this.m33 + mat.m24 * this.m43); + var m24 = (mat.m21 * this.m14 + mat.m22 * this.m24 + + mat.m23 * this.m34 + mat.m24 * this.m44); + + var m31 = (mat.m31 * this.m11 + mat.m32 * this.m21 + + mat.m33 * this.m31 + mat.m34 * this.m41); + var m32 = (mat.m31 * this.m12 + mat.m32 * this.m22 + + mat.m33 * this.m32 + mat.m34 * this.m42); + var m33 = (mat.m31 * this.m13 + mat.m32 * this.m23 + + mat.m33 * this.m33 + mat.m34 * this.m43); + var m34 = (mat.m31 * this.m14 + mat.m32 * this.m24 + + mat.m33 * this.m34 + mat.m34 * this.m44); + + var m41 = (mat.m41 * this.m11 + mat.m42 * this.m21 + + mat.m43 * this.m31 + mat.m44 * this.m41); + var m42 = (mat.m41 * this.m12 + mat.m42 * this.m22 + + mat.m43 * this.m32 + mat.m44 * this.m42); + var m43 = (mat.m41 * this.m13 + mat.m42 * this.m23 + + mat.m43 * this.m33 + mat.m44 * this.m43); + var m44 = (mat.m41 * this.m14 + mat.m42 * this.m24 + + mat.m43 * this.m34 + mat.m44 * this.m44); + + this.m11 = m11; + this.m12 = m12; + this.m13 = m13; + this.m14 = m14; + + this.m21 = m21; + this.m22 = m22; + this.m23 = m23; + this.m24 = m24; + + this.m31 = m31; + this.m32 = m32; + this.m33 = m33; + this.m34 = m34; + + this.m41 = m41; + this.m42 = m42; + this.m43 = m43; + this.m44 = m44; +}; + +CanvasMatrix4.prototype.ortho = function(left, right, bottom, top, near, far) +{ + var tx = (left + right) / (left - right); + var ty = (top + bottom) / (bottom - top); + var tz = (far + near) / (near - far); + + var matrix = new CanvasMatrix4(); + matrix.m11 = 2 / (right - left); + matrix.m12 = 0; + matrix.m13 = 0; + matrix.m14 = 0; + matrix.m21 = 0; + matrix.m22 = 2 / (top - bottom); + matrix.m23 = 0; + matrix.m24 = 0; + matrix.m31 = 0; + matrix.m32 = 0; + matrix.m33 = -2 / (far - near); + matrix.m34 = 0; + matrix.m41 = tx; + matrix.m42 = ty; + matrix.m43 = tz; + matrix.m44 = 1; + + this.multRight(matrix); +}; + +CanvasMatrix4.prototype.frustum = function(left, right, bottom, top, near, far) +{ + var matrix = new CanvasMatrix4(); + var A = (right + left) / (right - left); + var B = (top + bottom) / (top - bottom); + var C = -(far + near) / (far - near); + var D = -(2 * far * near) / (far - near); + + matrix.m11 = (2 * near) / (right - left); + matrix.m12 = 0; + matrix.m13 = 0; + matrix.m14 = 0; + + matrix.m21 = 0; + matrix.m22 = 2 * near / (top - bottom); + matrix.m23 = 0; + matrix.m24 = 0; + + matrix.m31 = A; + matrix.m32 = B; + matrix.m33 = C; + matrix.m34 = -1; + + matrix.m41 = 0; + matrix.m42 = 0; + matrix.m43 = D; + matrix.m44 = 0; + + this.multRight(matrix); +}; + +CanvasMatrix4.prototype.perspective = function(fovy, aspect, zNear, zFar) +{ + var top = Math.tan(fovy * Math.PI / 360) * zNear; + var bottom = -top; + var left = aspect * bottom; + var right = aspect * top; + this.frustum(left, right, bottom, top, zNear, zFar); +}; + +CanvasMatrix4.prototype.lookat = function(eyex, eyey, eyez, centerx, centery, centerz, upx, upy, upz) +{ + var matrix = new CanvasMatrix4(), + xx, xy, xz; + + // Make rotation matrix + + // Z vector + var zx = eyex - centerx; + var zy = eyey - centery; + var zz = eyez - centerz; + var mag = Math.sqrt(zx * zx + zy * zy + zz * zz); + if (mag) { + zx /= mag; + zy /= mag; + zz /= mag; + } + + // Y vector + var yx = upx; + var yy = upy; + var yz = upz; + + // X vector = Y cross Z + xx = yy * zz - yz * zy; + xy = -yx * zz + yz * zx; + xz = yx * zy - yy * zx; + + // Recompute Y = Z cross X + yx = zy * xz - zz * xy; + yy = -zx * xz + zz * xx; + yx = zx * xy - zy * xx; + + // cross product gives area of parallelogram, which is < 1.0 for + // non-perpendicular unit-length vectors; so normalize x, y here + + mag = Math.sqrt(xx * xx + xy * xy + xz * xz); + if (mag) { + xx /= mag; + xy /= mag; + xz /= mag; + } + + mag = Math.sqrt(yx * yx + yy * yy + yz * yz); + if (mag) { + yx /= mag; + yy /= mag; + yz /= mag; + } + + matrix.m11 = xx; + matrix.m12 = xy; + matrix.m13 = xz; + matrix.m14 = 0; + + matrix.m21 = yx; + matrix.m22 = yy; + matrix.m23 = yz; + matrix.m24 = 0; + + matrix.m31 = zx; + matrix.m32 = zy; + matrix.m33 = zz; + matrix.m34 = 0; + + matrix.m41 = 0; + matrix.m42 = 0; + matrix.m43 = 0; + matrix.m44 = 1; + matrix.translate(-eyex, -eyey, -eyez); + + this.multRight(matrix); +}; + +// Support functions +CanvasMatrix4.prototype._determinant2x2 = function(a, b, c, d) +{ + return a * d - b * c; +}; + +CanvasMatrix4.prototype._determinant3x3 = function(a1, a2, a3, b1, b2, b3, c1, c2, c3) +{ + return a1 * this._determinant2x2(b2, b3, c2, c3) - + b1 * this._determinant2x2(a2, a3, c2, c3) + + c1 * this._determinant2x2(a2, a3, b2, b3); +}; + +CanvasMatrix4.prototype._determinant4x4 = function() +{ + var a1 = this.m11; + var b1 = this.m12; + var c1 = this.m13; + var d1 = this.m14; + + var a2 = this.m21; + var b2 = this.m22; + var c2 = this.m23; + var d2 = this.m24; + + var a3 = this.m31; + var b3 = this.m32; + var c3 = this.m33; + var d3 = this.m34; + + var a4 = this.m41; + var b4 = this.m42; + var c4 = this.m43; + var d4 = this.m44; + + return a1 * this._determinant3x3(b2, b3, b4, c2, c3, c4, d2, d3, d4) - + b1 * this._determinant3x3(a2, a3, a4, c2, c3, c4, d2, d3, d4) + + c1 * this._determinant3x3(a2, a3, a4, b2, b3, b4, d2, d3, d4) - + d1 * this._determinant3x3(a2, a3, a4, b2, b3, b4, c2, c3, c4); +}; + +CanvasMatrix4.prototype._makeAdjoint = function() +{ + var a1 = this.m11; + var b1 = this.m12; + var c1 = this.m13; + var d1 = this.m14; + + var a2 = this.m21; + var b2 = this.m22; + var c2 = this.m23; + var d2 = this.m24; + + var a3 = this.m31; + var b3 = this.m32; + var c3 = this.m33; + var d3 = this.m34; + + var a4 = this.m41; + var b4 = this.m42; + var c4 = this.m43; + var d4 = this.m44; + + // Row column labeling reversed since we transpose rows & columns + this.m11 = this._determinant3x3(b2, b3, b4, c2, c3, c4, d2, d3, d4); + this.m21 = - this._determinant3x3(a2, a3, a4, c2, c3, c4, d2, d3, d4); + this.m31 = this._determinant3x3(a2, a3, a4, b2, b3, b4, d2, d3, d4); + this.m41 = - this._determinant3x3(a2, a3, a4, b2, b3, b4, c2, c3, c4); + + this.m12 = - this._determinant3x3(b1, b3, b4, c1, c3, c4, d1, d3, d4); + this.m22 = this._determinant3x3(a1, a3, a4, c1, c3, c4, d1, d3, d4); + this.m32 = - this._determinant3x3(a1, a3, a4, b1, b3, b4, d1, d3, d4); + this.m42 = this._determinant3x3(a1, a3, a4, b1, b3, b4, c1, c3, c4); + + this.m13 = this._determinant3x3(b1, b2, b4, c1, c2, c4, d1, d2, d4); + this.m23 = - this._determinant3x3(a1, a2, a4, c1, c2, c4, d1, d2, d4); + this.m33 = this._determinant3x3(a1, a2, a4, b1, b2, b4, d1, d2, d4); + this.m43 = - this._determinant3x3(a1, a2, a4, b1, b2, b4, c1, c2, c4); + + this.m14 = - this._determinant3x3(b1, b2, b3, c1, c2, c3, d1, d2, d3); + this.m24 = this._determinant3x3(a1, a2, a3, c1, c2, c3, d1, d2, d3); + this.m34 = - this._determinant3x3(a1, a2, a3, b1, b2, b3, d1, d2, d3); + this.m44 = this._determinant3x3(a1, a2, a3, b1, b2, b3, c1, c2, c3); +}; diff --git a/inst/gitbook/_book/libs/gitbook-2.6.7/css/style.css b/inst/gitbook/_book/libs/gitbook-2.6.7/css/style.css index ed272033..cba69b23 100644 --- a/inst/gitbook/_book/libs/gitbook-2.6.7/css/style.css +++ b/inst/gitbook/_book/libs/gitbook-2.6.7/css/style.css @@ -1,15 +1,13 @@ -/*! normalize.css v2.1.0 | MIT License | git.io/normalize 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8px;white-space:nowrap}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-overflow-scrolling:auto;-webkit-tap-highlight-color:transparent;-webkit-text-size-adjust:none;-webkit-touch-callout:none}a{text-decoration:none}body,html{height:100%}html{font-size:62.5%}body{text-rendering:optimizeLegibility;font-smoothing:antialiased;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;letter-spacing:.2px;text-size-adjust:100%} .book .book-summary ul.summary li a span {display:inline;padding:initial;overflow:visible;cursor:auto;opacity:1;} /* show arrow before summary tag as in bootstrap */ details > summary {display:list-item;cursor:pointer;} -/*add whatsapp icon from FA 5.1.1 -TODO: remove when updating fontawesome*/ -.fa-whatsapp:before{content:"\f232"} diff --git a/inst/gitbook/_book/libs/gitbook-2.6.7/js/plugin-clipboard.js b/inst/gitbook/_book/libs/gitbook-2.6.7/js/plugin-clipboard.js index 9a7d2e75..f0880be6 100644 --- a/inst/gitbook/_book/libs/gitbook-2.6.7/js/plugin-clipboard.js +++ b/inst/gitbook/_book/libs/gitbook-2.6.7/js/plugin-clipboard.js @@ -9,7 +9,9 @@ gitbook.require(["gitbook", "jQuery"], function(gitbook, $) { // the page.change event is thrown twice: before and after the page changes if (clipboard) { - // clipboard is already defined + // clipboard is already defined but we are on the same page + if (clipboard._prevPage === window.location.pathname) return; + // clipboard is already defined and url path change // we can deduct that we are before page changes clipboard.destroy(); // destroy the previous events listeners clipboard = undefined; // reset the clipboard object @@ -24,6 +26,8 @@ gitbook.require(["gitbook", "jQuery"], function(gitbook, $) { } }); + clipboard._prevPage = window.location.pathname + }); }); diff --git a/inst/gitbook/_book/libs/htmlwidgets-1.6.2/htmlwidgets.js b/inst/gitbook/_book/libs/htmlwidgets-1.6.2/htmlwidgets.js new file mode 100644 index 00000000..1067d029 --- /dev/null +++ b/inst/gitbook/_book/libs/htmlwidgets-1.6.2/htmlwidgets.js @@ -0,0 +1,901 @@ +(function() { + // If window.HTMLWidgets is already defined, then use it; otherwise create a + // new object. This allows preceding code to set options that affect the + // initialization process (though none currently exist). + window.HTMLWidgets = window.HTMLWidgets || {}; + + // See if we're running in a viewer pane. If not, we're in a web browser. + var viewerMode = window.HTMLWidgets.viewerMode = + /\bviewer_pane=1\b/.test(window.location); + + // See if we're running in Shiny mode. If not, it's a static document. + // Note that static widgets can appear in both Shiny and static modes, but + // obviously, Shiny widgets can only appear in Shiny apps/documents. + var shinyMode = window.HTMLWidgets.shinyMode = + typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings; + + // We can't count on jQuery being available, so we implement our own + // version if necessary. + function querySelectorAll(scope, selector) { + if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) { + return scope.find(selector); + } + if (scope.querySelectorAll) { + return scope.querySelectorAll(selector); + } + } + + function asArray(value) { + if (value === null) + return []; + if ($.isArray(value)) + return value; + return [value]; + } + + // Implement jQuery's extend + function extend(target /*, ... */) { + if (arguments.length == 1) { + return target; + } + for (var i = 1; i < arguments.length; i++) { + var source = arguments[i]; + for (var prop in source) { + if (source.hasOwnProperty(prop)) { + target[prop] = source[prop]; + } + } + } + return target; + } + + // IE8 doesn't support Array.forEach. + function forEach(values, callback, thisArg) { + if (values.forEach) { + values.forEach(callback, thisArg); + } else { + for (var i = 0; i < values.length; i++) { + callback.call(thisArg, values[i], i, values); + } + } + } + + // Replaces the specified method with the return value of funcSource. + // + // Note that funcSource should not BE the new method, it should be a function + // that RETURNS the new method. funcSource receives a single argument that is + // the overridden method, it can be called from the new method. The overridden + // method can be called like a regular function, it has the target permanently + // bound to it so "this" will work correctly. + function overrideMethod(target, methodName, funcSource) { + var superFunc = target[methodName] || function() {}; + var superFuncBound = function() { + return superFunc.apply(target, arguments); + }; + target[methodName] = funcSource(superFuncBound); + } + + // Add a method to delegator that, when invoked, calls + // delegatee.methodName. If there is no such method on + // the delegatee, but there was one on delegator before + // delegateMethod was called, then the original version + // is invoked instead. + // For example: + // + // var a = { + // method1: function() { console.log('a1'); } + // method2: function() { console.log('a2'); } + // }; + // var b = { + // method1: function() { console.log('b1'); } + // }; + // delegateMethod(a, b, "method1"); + // delegateMethod(a, b, "method2"); + // a.method1(); + // a.method2(); + // + // The output would be "b1", "a2". + function delegateMethod(delegator, delegatee, methodName) { + var inherited = delegator[methodName]; + delegator[methodName] = function() { + var target = delegatee; + var method = delegatee[methodName]; + + // The method doesn't exist on the delegatee. Instead, + // call the method on the delegator, if it exists. + if (!method) { + target = delegator; + method = inherited; + } + + if (method) { + return method.apply(target, arguments); + } + }; + } + + // Implement a vague facsimilie of jQuery's data method + function elementData(el, name, value) { + if (arguments.length == 2) { + return el["htmlwidget_data_" + name]; + } else if (arguments.length == 3) { + el["htmlwidget_data_" + name] = value; + return el; + } else { + throw new Error("Wrong number of arguments for elementData: " + + arguments.length); + } + } + + // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex + function escapeRegExp(str) { + return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&"); + } + + function hasClass(el, className) { + var re = new RegExp("\\b" + escapeRegExp(className) + "\\b"); + return re.test(el.className); + } + + // elements - array (or array-like object) of HTML elements + // className - class name to test for + // include - if true, only return elements with given className; + // if false, only return elements *without* given className + function filterByClass(elements, className, include) { + var results = []; + for (var i = 0; i < elements.length; i++) { + if (hasClass(elements[i], className) == include) + results.push(elements[i]); + } + return results; + } + + function on(obj, eventName, func) { + if (obj.addEventListener) { + obj.addEventListener(eventName, func, false); + } else if (obj.attachEvent) { + obj.attachEvent(eventName, func); + } + } + + function off(obj, eventName, func) { + if (obj.removeEventListener) + obj.removeEventListener(eventName, func, false); + else if (obj.detachEvent) { + obj.detachEvent(eventName, func); + } + } + + // Translate array of values to top/right/bottom/left, as usual with + // the "padding" CSS property + // https://developer.mozilla.org/en-US/docs/Web/CSS/padding + function unpackPadding(value) { + if (typeof(value) === "number") + value = [value]; + if (value.length === 1) { + return {top: value[0], right: value[0], bottom: value[0], left: value[0]}; + } + if (value.length === 2) { + return {top: value[0], right: value[1], bottom: value[0], left: value[1]}; + } + if (value.length === 3) { + return {top: value[0], right: value[1], bottom: value[2], left: value[1]}; + } + if (value.length === 4) { + return {top: value[0], right: value[1], bottom: value[2], left: value[3]}; + } + } + + // Convert an unpacked padding object to a CSS value + function paddingToCss(paddingObj) { + return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px"; + } + + // Makes a number suitable for CSS + function px(x) { + if (typeof(x) === "number") + return x + "px"; + else + return x; + } + + // Retrieves runtime widget sizing information for an element. + // The return value is either null, or an object with fill, padding, + // defaultWidth, defaultHeight fields. + function sizingPolicy(el) { + var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']"); + if (!sizingEl) + return null; + var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}"); + if (viewerMode) { + return sp.viewer; + } else { + return sp.browser; + } + } + + // @param tasks Array of strings (or falsy value, in which case no-op). + // Each element must be a valid JavaScript expression that yields a + // function. Or, can be an array of objects with "code" and "data" + // properties; in this case, the "code" property should be a string + // of JS that's an expr that yields a function, and "data" should be + // an object that will be added as an additional argument when that + // function is called. + // @param target The object that will be "this" for each function + // execution. + // @param args Array of arguments to be passed to the functions. (The + // same arguments will be passed to all functions.) + function evalAndRun(tasks, target, args) { + if (tasks) { + forEach(tasks, function(task) { + var theseArgs = args; + if (typeof(task) === "object") { + theseArgs = theseArgs.concat([task.data]); + task = task.code; + } + var taskFunc = tryEval(task); + if (typeof(taskFunc) !== "function") { + throw new Error("Task must be a function! Source:\n" + task); + } + taskFunc.apply(target, theseArgs); + }); + } + } + + // Attempt eval() both with and without enclosing in parentheses. + // Note that enclosing coerces a function declaration into + // an expression that eval() can parse + // (otherwise, a SyntaxError is thrown) + function tryEval(code) { + var result = null; + try { + result = eval("(" + code + ")"); + } catch(error) { + if (!(error instanceof SyntaxError)) { + throw error; + } + try { + result = eval(code); + } catch(e) { + if (e instanceof SyntaxError) { + throw error; + } else { + throw e; + } + } + } + return result; + } + + function initSizing(el) { + var sizing = sizingPolicy(el); + if (!sizing) + return; + + var cel = document.getElementById("htmlwidget_container"); + if (!cel) + return; + + if (typeof(sizing.padding) !== "undefined") { + document.body.style.margin = "0"; + document.body.style.padding = paddingToCss(unpackPadding(sizing.padding)); + } + + if (sizing.fill) { + document.body.style.overflow = "hidden"; + document.body.style.width = "100%"; + document.body.style.height = "100%"; + document.documentElement.style.width = "100%"; + document.documentElement.style.height = "100%"; + cel.style.position = "absolute"; + var pad = unpackPadding(sizing.padding); + cel.style.top = pad.top + "px"; + cel.style.right = pad.right + "px"; + cel.style.bottom = pad.bottom + "px"; + cel.style.left = pad.left + "px"; + el.style.width = "100%"; + el.style.height = "100%"; + + return { + getWidth: function() { return cel.getBoundingClientRect().width; }, + getHeight: function() { return cel.getBoundingClientRect().height; } + }; + + } else { + el.style.width = px(sizing.width); + el.style.height = px(sizing.height); + + return { + getWidth: function() { return cel.getBoundingClientRect().width; }, + getHeight: function() { return cel.getBoundingClientRect().height; } + }; + } + } + + // Default implementations for methods + var defaults = { + find: function(scope) { + return querySelectorAll(scope, "." + this.name); + }, + renderError: function(el, err) { + var $el = $(el); + + this.clearError(el); + + // Add all these error classes, as Shiny does + var errClass = "shiny-output-error"; + if (err.type !== null) { + // use the classes of the error condition as CSS class names + errClass = errClass + " " + $.map(asArray(err.type), function(type) { + return errClass + "-" + type; + }).join(" "); + } + errClass = errClass + " htmlwidgets-error"; + + // Is el inline or block? If inline or inline-block, just display:none it + // and add an inline error. + var display = $el.css("display"); + $el.data("restore-display-mode", display); + + if (display === "inline" || display === "inline-block") { + $el.hide(); + if (err.message !== "") { + var errorSpan = $("").addClass(errClass); + errorSpan.text(err.message); + $el.after(errorSpan); + } + } else if (display === "block") { + // If block, add an error just after the el, set visibility:none on the + // el, and position the error to be on top of the el. + // Mark it with a unique ID and CSS class so we can remove it later. + $el.css("visibility", "hidden"); + if (err.message !== "") { + var errorDiv = $("
    ").addClass(errClass).css("position", "absolute") + .css("top", el.offsetTop) + .css("left", el.offsetLeft) + // setting width can push out the page size, forcing otherwise + // unnecessary scrollbars to appear and making it impossible for + // the element to shrink; so use max-width instead + .css("maxWidth", el.offsetWidth) + .css("height", el.offsetHeight); + errorDiv.text(err.message); + $el.after(errorDiv); + + // Really dumb way to keep the size/position of the error in sync with + // the parent element as the window is resized or whatever. + var intId = setInterval(function() { + if (!errorDiv[0].parentElement) { + clearInterval(intId); + return; + } + errorDiv + .css("top", el.offsetTop) + .css("left", el.offsetLeft) + .css("maxWidth", el.offsetWidth) + .css("height", el.offsetHeight); + }, 500); + } + } + }, + clearError: function(el) { + var $el = $(el); + var display = $el.data("restore-display-mode"); + $el.data("restore-display-mode", null); + + if (display === "inline" || display === "inline-block") { + if (display) + $el.css("display", display); + $(el.nextSibling).filter(".htmlwidgets-error").remove(); + } else if (display === "block"){ + $el.css("visibility", "inherit"); + $(el.nextSibling).filter(".htmlwidgets-error").remove(); + } + }, + sizing: {} + }; + + // Called by widget bindings to register a new type of widget. The definition + // object can contain the following properties: + // - name (required) - A string indicating the binding name, which will be + // used by default as the CSS classname to look for. + // - initialize (optional) - A function(el) that will be called once per + // widget element; if a value is returned, it will be passed as the third + // value to renderValue. + // - renderValue (required) - A function(el, data, initValue) that will be + // called with data. Static contexts will cause this to be called once per + // element; Shiny apps will cause this to be called multiple times per + // element, as the data changes. + window.HTMLWidgets.widget = function(definition) { + if (!definition.name) { + throw new Error("Widget must have a name"); + } + if (!definition.type) { + throw new Error("Widget must have a type"); + } + // Currently we only support output widgets + if (definition.type !== "output") { + throw new Error("Unrecognized widget type '" + definition.type + "'"); + } + // TODO: Verify that .name is a valid CSS classname + + // Support new-style instance-bound definitions. Old-style class-bound + // definitions have one widget "object" per widget per type/class of + // widget; the renderValue and resize methods on such widget objects + // take el and instance arguments, because the widget object can't + // store them. New-style instance-bound definitions have one widget + // object per widget instance; the definition that's passed in doesn't + // provide renderValue or resize methods at all, just the single method + // factory(el, width, height) + // which returns an object that has renderValue(x) and resize(w, h). + // This enables a far more natural programming style for the widget + // author, who can store per-instance state using either OO-style + // instance fields or functional-style closure variables (I guess this + // is in contrast to what can only be called C-style pseudo-OO which is + // what we required before). + if (definition.factory) { + definition = createLegacyDefinitionAdapter(definition); + } + + if (!definition.renderValue) { + throw new Error("Widget must have a renderValue function"); + } + + // For static rendering (non-Shiny), use a simple widget registration + // scheme. We also use this scheme for Shiny apps/documents that also + // contain static widgets. + window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || []; + // Merge defaults into the definition; don't mutate the original definition. + var staticBinding = extend({}, defaults, definition); + overrideMethod(staticBinding, "find", function(superfunc) { + return function(scope) { + var results = superfunc(scope); + // Filter out Shiny outputs, we only want the static kind + return filterByClass(results, "html-widget-output", false); + }; + }); + window.HTMLWidgets.widgets.push(staticBinding); + + if (shinyMode) { + // Shiny is running. Register the definition with an output binding. + // The definition itself will not be the output binding, instead + // we will make an output binding object that delegates to the + // definition. This is because we foolishly used the same method + // name (renderValue) for htmlwidgets definition and Shiny bindings + // but they actually have quite different semantics (the Shiny + // bindings receive data that includes lots of metadata that it + // strips off before calling htmlwidgets renderValue). We can't + // just ignore the difference because in some widgets it's helpful + // to call this.renderValue() from inside of resize(), and if + // we're not delegating, then that call will go to the Shiny + // version instead of the htmlwidgets version. + + // Merge defaults with definition, without mutating either. + var bindingDef = extend({}, defaults, definition); + + // This object will be our actual Shiny binding. + var shinyBinding = new Shiny.OutputBinding(); + + // With a few exceptions, we'll want to simply use the bindingDef's + // version of methods if they are available, otherwise fall back to + // Shiny's defaults. NOTE: If Shiny's output bindings gain additional + // methods in the future, and we want them to be overrideable by + // HTMLWidget binding definitions, then we'll need to add them to this + // list. + delegateMethod(shinyBinding, bindingDef, "getId"); + delegateMethod(shinyBinding, bindingDef, "onValueChange"); + delegateMethod(shinyBinding, bindingDef, "onValueError"); + delegateMethod(shinyBinding, bindingDef, "renderError"); + delegateMethod(shinyBinding, bindingDef, "clearError"); + delegateMethod(shinyBinding, bindingDef, "showProgress"); + + // The find, renderValue, and resize are handled differently, because we + // want to actually decorate the behavior of the bindingDef methods. + + shinyBinding.find = function(scope) { + var results = bindingDef.find(scope); + + // Only return elements that are Shiny outputs, not static ones + var dynamicResults = results.filter(".html-widget-output"); + + // It's possible that whatever caused Shiny to think there might be + // new dynamic outputs, also caused there to be new static outputs. + // Since there might be lots of different htmlwidgets bindings, we + // schedule execution for later--no need to staticRender multiple + // times. + if (results.length !== dynamicResults.length) + scheduleStaticRender(); + + return dynamicResults; + }; + + // Wrap renderValue to handle initialization, which unfortunately isn't + // supported natively by Shiny at the time of this writing. + + shinyBinding.renderValue = function(el, data) { + Shiny.renderDependencies(data.deps); + // Resolve strings marked as javascript literals to objects + if (!(data.evals instanceof Array)) data.evals = [data.evals]; + for (var i = 0; data.evals && i < data.evals.length; i++) { + window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]); + } + if (!bindingDef.renderOnNullValue) { + if (data.x === null) { + el.style.visibility = "hidden"; + return; + } else { + el.style.visibility = "inherit"; + } + } + if (!elementData(el, "initialized")) { + initSizing(el); + + elementData(el, "initialized", true); + if (bindingDef.initialize) { + var rect = el.getBoundingClientRect(); + var result = bindingDef.initialize(el, rect.width, rect.height); + elementData(el, "init_result", result); + } + } + bindingDef.renderValue(el, data.x, elementData(el, "init_result")); + evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]); + }; + + // Only override resize if bindingDef implements it + if (bindingDef.resize) { + shinyBinding.resize = function(el, width, height) { + // Shiny can call resize before initialize/renderValue have been + // called, which doesn't make sense for widgets. + if (elementData(el, "initialized")) { + bindingDef.resize(el, width, height, elementData(el, "init_result")); + } + }; + } + + Shiny.outputBindings.register(shinyBinding, bindingDef.name); + } + }; + + var scheduleStaticRenderTimerId = null; + function scheduleStaticRender() { + if (!scheduleStaticRenderTimerId) { + scheduleStaticRenderTimerId = setTimeout(function() { + scheduleStaticRenderTimerId = null; + window.HTMLWidgets.staticRender(); + }, 1); + } + } + + // Render static widgets after the document finishes loading + // Statically render all elements that are of this widget's class + window.HTMLWidgets.staticRender = function() { + var bindings = window.HTMLWidgets.widgets || []; + forEach(bindings, function(binding) { + var matches = binding.find(document.documentElement); + forEach(matches, function(el) { + var sizeObj = initSizing(el, binding); + + var getSize = function(el) { + if (sizeObj) { + return {w: sizeObj.getWidth(), h: sizeObj.getHeight()} + } else { + var rect = el.getBoundingClientRect(); + return {w: rect.width, h: rect.height} + } + }; + + if (hasClass(el, "html-widget-static-bound")) + return; + el.className = el.className + " html-widget-static-bound"; + + var initResult; + if (binding.initialize) { + var size = getSize(el); + initResult = binding.initialize(el, size.w, size.h); + elementData(el, "init_result", initResult); + } + + if (binding.resize) { + var lastSize = getSize(el); + var resizeHandler = function(e) { + var size = getSize(el); + if (size.w === 0 && size.h === 0) + return; + if (size.w === lastSize.w && size.h === lastSize.h) + return; + lastSize = size; + binding.resize(el, size.w, size.h, initResult); + }; + + on(window, "resize", resizeHandler); + + // This is needed for cases where we're running in a Shiny + // app, but the widget itself is not a Shiny output, but + // rather a simple static widget. One example of this is + // an rmarkdown document that has runtime:shiny and widget + // that isn't in a render function. Shiny only knows to + // call resize handlers for Shiny outputs, not for static + // widgets, so we do it ourselves. + if (window.jQuery) { + window.jQuery(document).on( + "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets", + resizeHandler + ); + window.jQuery(document).on( + "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets", + resizeHandler + ); + } + + // This is needed for the specific case of ioslides, which + // flips slides between display:none and display:block. + // Ideally we would not have to have ioslide-specific code + // here, but rather have ioslides raise a generic event, + // but the rmarkdown package just went to CRAN so the + // window to getting that fixed may be long. + if (window.addEventListener) { + // It's OK to limit this to window.addEventListener + // browsers because ioslides itself only supports + // such browsers. + on(document, "slideenter", resizeHandler); + on(document, "slideleave", resizeHandler); + } + } + + var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']"); + if (scriptData) { + var data = JSON.parse(scriptData.textContent || scriptData.text); + // Resolve strings marked as javascript literals to objects + if (!(data.evals instanceof Array)) data.evals = [data.evals]; + for (var k = 0; data.evals && k < data.evals.length; k++) { + window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]); + } + binding.renderValue(el, data.x, initResult); + evalAndRun(data.jsHooks.render, initResult, [el, data.x]); + } + }); + }); + + invokePostRenderHandlers(); + } + + + function has_jQuery3() { + if (!window.jQuery) { + return false; + } + var $version = window.jQuery.fn.jquery; + var $major_version = parseInt($version.split(".")[0]); + return $major_version >= 3; + } + + /* + / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's + / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now + / really means $(setTimeout(fn)). + / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous + / + / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny + / one tick later than it did before, which means staticRender() is + / called renderValue() earlier than (advanced) widget authors might be expecting. + / https://github.com/rstudio/shiny/issues/2630 + / + / For a concrete example, leaflet has some methods (e.g., updateBounds) + / which reference Shiny methods registered in initShiny (e.g., setInputValue). + / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to + / delay execution of those methods (until Shiny methods are ready) + / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268 + / + / Ideally widget authors wouldn't need to use this setTimeout() hack that + / leaflet uses to call Shiny methods on a staticRender(). In the long run, + / the logic initShiny should be broken up so that method registration happens + / right away, but binding happens later. + */ + function maybeStaticRenderLater() { + if (shinyMode && has_jQuery3()) { + window.jQuery(window.HTMLWidgets.staticRender); + } else { + window.HTMLWidgets.staticRender(); + } + } + + if (document.addEventListener) { + document.addEventListener("DOMContentLoaded", function() { + document.removeEventListener("DOMContentLoaded", arguments.callee, false); + maybeStaticRenderLater(); + }, false); + } else if (document.attachEvent) { + document.attachEvent("onreadystatechange", function() { + if (document.readyState === "complete") { + document.detachEvent("onreadystatechange", arguments.callee); + maybeStaticRenderLater(); + } + }); + } + + + window.HTMLWidgets.getAttachmentUrl = function(depname, key) { + // If no key, default to the first item + if (typeof(key) === "undefined") + key = 1; + + var link = document.getElementById(depname + "-" + key + "-attachment"); + if (!link) { + throw new Error("Attachment " + depname + "/" + key + " not found in document"); + } + return link.getAttribute("href"); + }; + + window.HTMLWidgets.dataframeToD3 = function(df) { + var names = []; + var length; + for (var name in df) { + if (df.hasOwnProperty(name)) + names.push(name); + if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") { + throw new Error("All fields must be arrays"); + } else if (typeof(length) !== "undefined" && length !== df[name].length) { + throw new Error("All fields must be arrays of the same length"); + } + length = df[name].length; + } + var results = []; + var item; + for (var row = 0; row < length; row++) { + item = {}; + for (var col = 0; col < names.length; col++) { + item[names[col]] = df[names[col]][row]; + } + results.push(item); + } + return results; + }; + + window.HTMLWidgets.transposeArray2D = function(array) { + if (array.length === 0) return array; + var newArray = array[0].map(function(col, i) { + return array.map(function(row) { + return row[i] + }) + }); + return newArray; + }; + // Split value at splitChar, but allow splitChar to be escaped + // using escapeChar. Any other characters escaped by escapeChar + // will be included as usual (including escapeChar itself). + function splitWithEscape(value, splitChar, escapeChar) { + var results = []; + var escapeMode = false; + var currentResult = ""; + for (var pos = 0; pos < value.length; pos++) { + if (!escapeMode) { + if (value[pos] === splitChar) { + results.push(currentResult); + currentResult = ""; + } else if (value[pos] === escapeChar) { + escapeMode = true; + } else { + currentResult += value[pos]; + } + } else { + currentResult += value[pos]; + escapeMode = false; + } + } + if (currentResult !== "") { + results.push(currentResult); + } + return results; + } + // Function authored by Yihui/JJ Allaire + window.HTMLWidgets.evaluateStringMember = function(o, member) { + var parts = splitWithEscape(member, '.', '\\'); + for (var i = 0, l = parts.length; i < l; i++) { + var part = parts[i]; + // part may be a character or 'numeric' member name + if (o !== null && typeof o === "object" && part in o) { + if (i == (l - 1)) { // if we are at the end of the line then evalulate + if (typeof o[part] === "string") + o[part] = tryEval(o[part]); + } else { // otherwise continue to next embedded object + o = o[part]; + } + } + } + }; + + // Retrieve the HTMLWidget instance (i.e. the return value of an + // HTMLWidget binding's initialize() or factory() function) + // associated with an element, or null if none. + window.HTMLWidgets.getInstance = function(el) { + return elementData(el, "init_result"); + }; + + // Finds the first element in the scope that matches the selector, + // and returns the HTMLWidget instance (i.e. the return value of + // an HTMLWidget binding's initialize() or factory() function) + // associated with that element, if any. If no element matches the + // selector, or the first matching element has no HTMLWidget + // instance associated with it, then null is returned. + // + // The scope argument is optional, and defaults to window.document. + window.HTMLWidgets.find = function(scope, selector) { + if (arguments.length == 1) { + selector = scope; + scope = document; + } + + var el = scope.querySelector(selector); + if (el === null) { + return null; + } else { + return window.HTMLWidgets.getInstance(el); + } + }; + + // Finds all elements in the scope that match the selector, and + // returns the HTMLWidget instances (i.e. the return values of + // an HTMLWidget binding's initialize() or factory() function) + // associated with the elements, in an array. If elements that + // match the selector don't have an associated HTMLWidget + // instance, the returned array will contain nulls. + // + // The scope argument is optional, and defaults to window.document. + window.HTMLWidgets.findAll = function(scope, selector) { + if (arguments.length == 1) { + selector = scope; + scope = document; + } + + var nodes = scope.querySelectorAll(selector); + var results = []; + for (var i = 0; i < nodes.length; i++) { + results.push(window.HTMLWidgets.getInstance(nodes[i])); + } + return results; + }; + + var postRenderHandlers = []; + function invokePostRenderHandlers() { + while (postRenderHandlers.length) { + var handler = postRenderHandlers.shift(); + if (handler) { + handler(); + } + } + } + + // Register the given callback function to be invoked after the + // next time static widgets are rendered. + window.HTMLWidgets.addPostRenderHandler = function(callback) { + postRenderHandlers.push(callback); + }; + + // Takes a new-style instance-bound definition, and returns an + // old-style class-bound definition. This saves us from having + // to rewrite all the logic in this file to accomodate both + // types of definitions. + function createLegacyDefinitionAdapter(defn) { + var result = { + name: defn.name, + type: defn.type, + initialize: function(el, width, height) { + return defn.factory(el, width, height); + }, + renderValue: function(el, x, instance) { + return instance.renderValue(x); + }, + resize: function(el, width, height, instance) { + return instance.resize(width, height); + } + }; + + if (defn.find) + result.find = defn.find; + if (defn.renderError) + result.renderError = defn.renderError; + if (defn.clearError) + result.clearError = defn.clearError; + + return result; + } +})(); diff --git a/inst/gitbook/_book/libs/rglWebGL-binding-1.2.1/rglWebGL.js b/inst/gitbook/_book/libs/rglWebGL-binding-1.2.1/rglWebGL.js new file mode 100644 index 00000000..52c8fe64 --- /dev/null +++ b/inst/gitbook/_book/libs/rglWebGL-binding-1.2.1/rglWebGL.js @@ -0,0 +1,79 @@ +/* el is the div, holding the rgl object as el.rglinstance, + which holds x as el.rglinstance.scene + x is the JSON encoded rglwidget. +*/ + + +HTMLWidgets.widget({ + + name: 'rglWebGL', + + type: 'output', + + factory: function(el, width, height) { + el.width = width; + el.height = height; + var rgl = new rglwidgetClass(), + onchangeselection = function(e) { + for (var i = 0; i < rgl.scene.crosstalk.sel_handle.length; i++) + rgl.clearBrush(except = e.rglSubsceneId); + rgl.selection(e, false); + }, + onchangefilter = function(e) { + rgl.selection(e, true); + }; + + return { + renderValue: function(x) { + var i, pel, player, groups, + inShiny = (typeof Shiny !== "undefined"); + + x.crosstalk.group = groups = [].concat(x.crosstalk.group); + x.crosstalk.id = [].concat(x.crosstalk.id); + x.crosstalk.key = [].concat(x.crosstalk.key); + x.crosstalk.sel_handle = new Array(groups.length); + x.crosstalk.fil_handle = new Array(groups.length); + x.crosstalk.selection = []; + for (i = 0; i < groups.length; i++) { + x.crosstalk.sel_handle[i] = new crosstalk.SelectionHandle(groups[i], {sharedId: x.crosstalk.id[i]}); + x.crosstalk.sel_handle[i].on("change", onchangeselection); + x.crosstalk.fil_handle[i] = new crosstalk.FilterHandle(groups[i], {sharedId: x.crosstalk.id[i]}); + x.crosstalk.fil_handle[i].on("change", onchangefilter); + } + if (inShiny) { + // Shiny calls this multiple times, so we need extra cleanup + // between + rgl.sphere = undefined; + } + rgl.initialize(el, x); + rgl.initGL(); + + /* We might have been called after (some of) the players were rendered. + We need to make sure we respond to their initial values. */ + + if (typeof x.players !== "undefined") { + var players = [].concat(x.players); + for (i = 0; i < players.length; i++) { + pel = document.getElementById(players[i]); + if (pel) { + player = pel.rglPlayer; + if (player && (!player.initialized || inShiny)) { + rgl.Player(pel, player); + player.initialized = true; + } + } + } + } + rgl.drag = 0; + rgl.drawScene(); + }, + + resize: function(width, height) { + el.width = width; + el.height = height; + el.rglinstance.resize(el); + el.rglinstance.drawScene(); + } + }; + } +}); diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/animation.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/animation.src.js new file mode 100644 index 00000000..cdca347d --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/animation.src.js @@ -0,0 +1,153 @@ +/** + * Methods related to animations + * @name ___METHODS_FOR_ANIMATION___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + /** + * Binary search + * @param x - x coordinates in increasing order + * @param newx - value to find, assumed to be in the range of x + * @result index of largest x value below newx + */ + rglwidgetClass.bisect = function(x, newx) { + var lo = 0, hi = x.length - 1, mid; + while (lo < hi - 1) { + mid = Math.round((lo + hi)/2); + if (x[mid] < newx) + lo = mid; + else + hi = mid; + } + return lo; + }; + + /** + * Step interpolation (constant outside bounds) + * @param x - x coordinates in increasing order + * @param v - values at x; either a vector or matrix + * @param newx - value at which to evaluate + */ + rglwidgetClass.step = function(x, v, newx) { + var n, lo; + if (newx <= x[0]) + return v[0]; + n = x.length; + if (newx >= x[n-1]) + return v[n-1]; + lo = this.bisect(x, newx); + return v[lo]; + }; + + /** + * Linear interpolation (constant outside bounds) + * @param x - x coordinates in increasing order + * @param v - values at x; either a vector or matrix + * @param newx - value at which to evaluate + */ + rglwidgetClass.lerp = function(x, v, newx) { + var i, n, lo, hi, alpha, result; + if (newx <= x[0]) + return v[0]; + n = x.length; + if (newx >= x[n-1]) + return v[n-1]; + lo = this.bisect(x, newx); + if (newx === x[lo]) + return v[lo]; + hi = lo + 1; + if (newx === x[hi]) + return v[hi]; + alpha = (newx - x[lo])/(x[hi] - x[lo]); + result = v[lo]; + n = result.length; + if (typeof n !== "undefined") { + for (i = 0; i < n; i++) + result[i] = (1 - alpha)*result[i] + alpha*v[hi][i]; + } else + result = (1 - alpha)*result + alpha*v[hi]; + return result; + }; + + /** + * Spherical linear interpolation (constant outside bounds) + * @param x - x coordinates in increasing order + * @param v - a matrix of unit quaternions + * @param newx - value at which to evaluate + */ + rglwidgetClass.slerp = function(x, v, newx) { + var n, lo, hi, alpha, result, + p0, p1, dot, Omega, alpha0, alpha1, len; + if (newx <= x[0]) + return v[0]; + if (newx >= x[n-1]) + return v[n-1]; + lo = this.bisect(x, newx); + if (newx === x[lo]) + return v[lo]; + hi = lo + 1; + if (newx === x[hi]) + return v[hi]; + p0 = v[lo]; + p1 = v[hi]; + dot = p0[0]*p1[0] + + p0[1]*p1[1] + + p0[2]*p1[2] + + p0[3]*p1[3]; + if (dot < 0) { + p1 = [-p1[0], -p1[1], -p1[2], -p1[3]]; + dot = -dot; + } + if (dot >= 1) + result = p1; + else { + alpha = (newx - x[lo])/(x[hi] - x[lo]); + Omega = Math.acos(dot); + alpha0 = Math.sin((1 - alpha)*Omega); + alpha1 = Math.sin(alpha*Omega); + result = [alpha0*p0[0] + alpha1*p1[0], + alpha0*p0[1] + alpha1*p1[1], + alpha0*p0[2] + alpha1*p1[2], + alpha0*p0[3] + alpha1*p1[3]]; + } + len = Math.sqrt(result[0]*result[0] + + result[1]*result[1] + + result[2]*result[2] + + result[3]*result[3]); + return [result[0]/len, + result[1]/len, + result[2]/len, + result[3]/len]; + }; + + /** + * Rotate using unit quaternion + * @param q - a single unit quaternion + */ + rglwidgetClass.rotateByQuaternion = function(M, q) { + + var xx = q[0]*q[0], + xy = q[0]*q[1], + xz = q[0]*q[2], + xw = q[0]*q[3], + yy = q[1]*q[1], + yz = q[1]*q[2], + yw = q[1]*q[3], + zz = q[2]*q[2], + zw = q[2]*q[3], + matrix = new CanvasMatrix4(); + matrix.m11 = 1 - 2*(yy + zz); + matrix.m12 = 2*(xy + zw); + matrix.m13 = 2*(xz - yw); + + matrix.m21 = 2*(xy - zw); + matrix.m22 = 1 - 2*(xx + zz); + matrix.m23 = 2*(yz + xw); + + matrix.m31 = 2*(xz + yw); + matrix.m32 = 2*(yz - xw); + matrix.m33 = 1 - 2*(xx + yy); + + M.multRight(matrix); + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/axes.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/axes.src.js new file mode 100644 index 00000000..485fa13c --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/axes.src.js @@ -0,0 +1,441 @@ + /** + * Methods related to axes + * @name ___METHODS_FOR_AXES___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + /** + * Choose edges for ticks + * @param { Matrix } prmv - projection-model-view matrix + */ + rglwidgetClass.prototype.getTickEdges = function(prmv){ + var vertices = [[0,0,0,1], [0,0,1,1], + [0,1,0,1], [0,1,1,1], + [1,0,0,1], [1,0,1,1], + [1,1,0,1], [1,1,1,1]], + dim, i, j, k, edges, hull, step, result = [], proj = [], + + // Filter to edges that are on sides that would + // be shown with a filled backing. + + has_back = function(edge) { + var normals = [[], []], + verts = [vertices[edge[0]], + vertices[edge[1]]], + normal, m, n; + n = 0; + for (m=0; m<3; m++) { + if (verts[0][m] === verts[1][m]) { + normals[n] = [0,0,0,1]; + normals[n][m] = 2*verts[0][m] - 1; + n++; + } + } + for (n=0; n<2; n++) { + normal = rglwidgetClass.multVM(normals[n], self.normMatrix); + if (normal[2] < 0 || + (normal[2] === 0 && normal[0] < 0)) + return true; + } + return false; + }, self = this; + + for (i = 0; i < vertices.length; i++) { + proj[i] = rglwidgetClass.multVM(vertices[i], prmv); + proj[i][0] = proj[i][0]/proj[i][3]; + proj[i][1] = proj[i][1]/proj[i][3]; + proj[i][2] = i; + } + hull = rglwidgetClass.chull(proj.slice()); + for (i = 0; i < hull.length; i++) + hull[i] = hull[i][2]; + hull.push(hull[0]); + for (dim = 0; dim < 3; dim++) { + edges = []; + step = Math.pow(2, 2-dim); + for (i = 0; i < 4; i++) { + j = (dim === 0) ? i : (dim === 1) ? i + 2*(i>1) : 2*i; + for (k = 0; k < hull.length - 1; k++) { + if ((hull[k] === j && hull[k+1] === j + step) || + (hull[k] === j+step && hull[k+1] === j)) + + edges.push([j, j+step], [j+step, j]); + } + } + + edges = edges.filter(has_back); + + // Find the edge with a vertex closest + // to the bottom left corner + if (edges.length) { + var best, best2, val = Infinity, newval; + for (i = 0; i < edges.length; i++) { + j = edges[i][0]; + newval = proj[j][0] + proj[j][1]; + if (newval < val) { + best = j; + best2 = edges[i][1]; + val = newval; + } + } + if (typeof best !== "undefined") { + result[dim] = vertices[best].slice(0,3); + result[dim][dim] = undefined; + } else + result[dim] = undefined; + } + } + return result; + }; + + /** + * Choose tick locations + * @param { Object } obj - The bboxdeco + */ + rglwidgetClass.prototype.getTickLocations = function(obj){ + var dim, i, limits, locations = [], result = [[],[],[]], value, + len, delta, range, bbox = obj.bbox; + obj.needsAxisCallback = false; + for (dim = 0; dim < 3; dim++) { + limits = bbox.slice(2*dim, 2*dim + 2); + range = limits[1] - limits[0]; + switch(obj.axes.mode[dim]) { + case "custom": + for (i=0; i < obj.vertices.length; i++) { + value = (obj.vertices[i][dim] - limits[0])/range; + if (typeof value !== "undefined" && + !isNaN(value)) + result[dim].push(value); + } + break; + case "fixedstep": + len = Math.floor(range/obj.axes.step[dim]); + delta = obj.axes.step[dim]; + for (i = 0; i < len; i++) + result[dim].push(i*delta); + break; + case "fixednum": + len = obj.axes.nticks[dim]; + delta = (len > 1) ? range/(len-1) : 0; + for (i = 0; i < len; i++) + result[dim].push(i*delta/range); + break; + case "pretty": + locations = this.R_pretty(limits[0], limits[1], 5, + 2, // min_n + 0.75, // shrink_sml + [1.5, 2.75], // high_u_fact + 0, // eps_correction + 0); // return_bounds) + for (i = locations.lo; i <= locations.up; i++) { + value = (i*locations.unit - limits[0])/range; + if (0 < value && value < 1) + result[dim].push(value); + } + break; + case "user": + obj.needsAxisCallback = true; + break; + } + } + return result; + }; + + /** + * Set tick vertices + * @param { Object } ticks - the tick object + * @param { Array } edges - Which edges get the ticks? + */ + rglwidgetClass.prototype.getTickVertices = function(ticks) { + var dim, i, j, vertices = [], locations, + edges = ticks.edges, edge; + for (dim = 0; dim < 3; dim++) { + locations = ticks.locations[dim]; + if (locations.length) + for (i = 0; i < locations.length; i++) + if (typeof edges[dim] !== "undefined") { + edge = edges[dim].slice(); + edge[dim] = locations[i]; + vertices.push(edge); + edge = edge.slice(); + for (j = 0; j < 3; j++) + if ((dim < 2 && j === 1 - dim) || + (dim === 2 && j === 0)) + edge[j] += 2*(edge[j] - 0.5)/ticks.axes.marklen[dim]; + vertices.push(edge); + } + } + ticks.vertices = vertices; + ticks.vertexCount = vertices.length; + ticks.values = new Float32Array(rglwidgetClass.flatten(vertices)); + ticks.initialized = false; + }; + + /** + * Set tick label positions + * @param { Object } obj - the bbox object + */ + rglwidgetClass.prototype.placeTickLabels = function(obj) { + var ticks = obj.ticks, labels = obj.labels, i,j,k, + vertices = [], tickvertices = ticks.vertices, + vertex, locations, dim, edges = obj.ticks.edges; + j = 0; + for (dim = 0; dim < 3; dim++) { + if (typeof edges[dim] === "undefined") + continue; + locations = ticks.locations[dim]; + if (locations.length) + for (i = 0; i < locations.length; i++) { + if (isNaN(locations[i])) + continue; + while (j < tickvertices.length && + tickvertices[j][dim] !== locations[i]) j++; + if (j >= tickvertices.length) + break; + vertex = tickvertices[j].slice(); + for (k = 0; k < 3; k++) + vertex[k] += 2*(tickvertices[j+1][k] - vertex[k]); + vertices.push(vertex); + j += 2; + } + } + labels.vertices = vertices; + labels.centers = labels.vertices; + labels.initialized = false; + }; + + /** + * Set tick labels + * @param { Object } obj - the bbox object + */ + rglwidgetClass.prototype.setTickLabels = function(obj) { + var ticks = obj.ticks, mode, locations, labels = [], + start = 0, nticks, dim, i, limits, range, values, max, + edges = obj.ticks.edges; + for (dim = 0; dim < 3; dim++) { + if (typeof edges[dim] === "undefined") + continue; + mode = obj.axes.mode[dim]; + nticks = obj.axes.nticks[dim]; // used on input only for custom! + if (mode === "custom") + labels = labels.concat(obj.texts.slice(start, start + nticks)); + else { + limits = obj.bbox.slice(2*dim, 2*(dim+1)); + range = limits[1] - limits[0]; + locations = ticks.locations[dim]; + max = -Infinity; + values = []; + for (i = 0; i < locations.length; i++) { + values.push(limits[0] + range*locations[i]); + max = Math.max(max, Math.abs(values[i])); + } + for (i = 0; i < locations.length; i++) { + if (Math.abs(values[i])/max < Math.pow(10, -5)) + values[i] = 0; + labels.push(rglwidgetClass.signif(values[i], 4).toString()); + } + obj.axes.nticks[dim] = locations.length; + } + start += nticks; + } + obj.labels.texts = labels; + }; + + /** + * Set bboxdeco bbox and center vector + * @param { Object } obj - the bbox object + */ + rglwidgetClass.prototype.setBbox = function(obj, subscene) { + var i, expand, center = [], bbox; + if (!obj.initialized) + this.initBBox(obj); + + bbox = [].concat(subscene.par3d.bbox); + for (i = 0; i < 3; i++) { + expand = obj.axes.expand[i]; + center[i] = (bbox[2*i] + bbox[2*i + 1])/2; + bbox[2*i] = center[i] - expand*(bbox[2*i + 1] - center[i]); + bbox[2*i+1] = center[i] + expand*(bbox[2*i + 1] - center[i]); + } + obj.bbox = bbox; + obj.center = center; + }; + + rglwidgetClass.prototype.setBBoxMatrices = function(obj) { + var saved = {normMatrix: new CanvasMatrix4(this.normMatrix), + mvMatrix: new CanvasMatrix4(this.mvMatrix)}, + bboxNorm, bboxMV, bbox = obj.bbox, scale; + + bboxNorm = new CanvasMatrix4(); + scale = [bbox[1]-bbox[0], bbox[3]-bbox[2], bbox[5]-bbox[4]]; + bboxNorm.scale(1/scale[0], 1/scale[1], 1/scale[2]); + bboxNorm.multRight(saved.normMatrix); + this.normMatrix = bboxNorm; + + bboxMV = new CanvasMatrix4(); + bboxMV.scale(scale[0], scale[1], scale[2]); + bboxMV.translate(bbox[0], bbox[2], bbox[4]); + bboxMV.multRight(saved.mvMatrix); + this.mvMatrix = obj.mvMatrix = bboxMV; + + if (this.prmvMatrix === null) + saved.prmvMatrix = null; + else + saved.prmvMatrix = new CanvasMatrix4(this.prmvMatrix); + + this.setprmvMatrix(); + obj.prmvMatrix = this.prmvMatrix; + + return saved; + }; + + rglwidgetClass.prototype.restoreBBoxMatrices = function(saved) { + this.normMatrix = saved.normMatrix; + this.mvMatrix = saved.mvMatrix; + this.prmvMatrix = saved.prmvMatrix; + }; + + rglwidgetClass.prototype.getMarginParameters = function(bboxdeco, material) { + // Assume we've run this.setBbox(bboxdeco, subscene); + var bbox = bboxdeco.bbox, + edge = [].concat(material.edge), + saved, edges, i, + at = material.margin, line, level, trans, scale; + + if (material.floating) { + saved = this.setBBoxMatrices(bboxdeco); + edges = this.getTickEdges(this.prmvMatrix)[at]; + this.restoreBBoxMatrices(saved); + if (typeof edges !== "undefined") + for (i = 0; i < 3; i++) { + if (edges[i] < 1) edges[i] = -1; + edge[i] = edge[i]*edges[i]; + } else + return undefined; + } + switch(at) { + case 0: line = 1; + level = 2; + break; + case 1: line = 0; + level = 2; + break; + case 2: line = 0; + level = 1; + break; + } + scale = [edge[0]*(bbox[1]-bbox[0])/bboxdeco.axes.marklen[0], + edge[1]*(bbox[3]-bbox[2])/bboxdeco.axes.marklen[1], + edge[2]*(bbox[5]-bbox[4])/bboxdeco.axes.marklen[2]]; + trans = [edge[0] === 1 ? bbox[1] : bbox[0], + edge[1] === 1 ? bbox[3] : bbox[2], + edge[2] === 1 ? bbox[5] : bbox[4]]; + return {at: at, line: line, level: level, trans: trans, scale: scale}; + }; + + rglwidgetClass.prototype.fixVertex = function(orig, parms, center, bbox) { + var vertex = [0,0,0]; + if (rglwidgetClass.missing(orig[0])) + vertex[parms.at] = center[parms.at]; + else if (orig[0] === "-Inf") + vertex[parms.at] = bbox[2*parms.at]; + else if (orig[0] === "Inf") + vertex[parms.at] = bbox[2*parms.at + 1]; + else + vertex[parms.at] = orig[0]; + vertex[parms.line] = parms.scale[parms.line]*orig[1] + + parms.trans[parms.line]; + vertex[parms.level] = parms.scale[parms.level]*orig[2] + + parms.trans[parms.level]; + return vertex; + }; + + rglwidgetClass.prototype.fixNormal = function(orig, parms) { + var vertex = [0,0,0]; + vertex[parms.at] = orig[0]; + vertex[parms.line] = orig[1]/parms.scale[parms.line]; + vertex[parms.level] = orig[2]/parms.scale[parms.level]; + return vertex; + }; + + rglwidgetClass.prototype.marginVecToDataVec = function(obj, subscene) { + var bboxdeco = this.getBBoxDeco(subscene), + center, bbox, parms, parmsjson, + orig = obj.orig, + vertices = [], normals = [], + centers = [], i, vertex; + if (typeof orig === "undefined") { + orig = {vert: obj.vertices, + norm: obj.normals, + cent: obj.centers, + doNormals: typeof obj.normals !== "undefined", + doCenters: typeof obj.centers !== "undefined", + parms: "" + }; + obj.orig = orig; + } + + if (typeof bboxdeco !== "undefined") { + this.setBbox(bboxdeco, subscene); + center = bboxdeco.center; + bbox = bboxdeco.bbox; + parms = this.getMarginParameters(bboxdeco, obj.material); + if (typeof parms === "undefined") + return false; /* axis is not currently shown */ + + parmsjson = JSON.stringify(parms); + if (parmsjson === orig.parms) + return true; /* nothing has changed */ + + orig.parms = parmsjson; + + for (i=0; i < orig.vert.length; i++) { + vertex = this.fixVertex(orig.vert[i], parms, center, bbox); + vertices.push(vertex); + } + obj.vertices = vertices; + if (orig.doNormals) { + for (i=0; i < orig.norm.length; i++) { + vertex = this.fixNormal(orig.norm[i], parms); + normals.push(vertex); + } + obj.normals = normals; + } + if (orig.doCenters) { + for (i=0; i < orig.cent.length; i++) { + vertex = this.fixVertex(orig.cent[i], parms, center, bbox); + centers.push(vertex); + } + obj.centers = centers; + } + + obj.initialized = false; + return true; + } else { + console.warn("bboxdeco not found"); + return false; + } + }; + + rglwidgetClass.prototype.doAxisCallback = function(obj, edges) { + var i, j, code, axis, fn; + for (i = 0; i < 3; i++) { + if (obj.axes.mode[i] === "user") { + axis = ["x", "y", "z"][i]; + if (typeof obj.callbacks !== "undefined" && + typeof (code = obj.callbacks[axis]) !== "undefined") { + if (typeof edges[i] !== "undefined") + for (j = 0; j < 3; j++) + if (typeof edges[i][j] !== "undefined") + axis = axis + (edges[i][j] > 0 ? "+" : "-"); + + /* jshint evil:true */ + fn = Function('"use strict";return (' + code + ')')(); + /* jshint evil:false */ + fn.call(this, axis); + } + } + } + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/buffer.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/buffer.src.js new file mode 100644 index 00000000..13d2e1ae --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/buffer.src.js @@ -0,0 +1,182 @@ +/** + * Methods related to buffered data + * @name ___METHODS_FOR_BUFFERS___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + /** + * Detect rglBuffered object + * @param { Object } obj - vertices or similar + */ + rglwidgetClass.prototype.isBuffered = function(obj) { + return typeof obj === "string"; + }; + + /* The next two functions are taken from + + https://developer.mozilla.org/en-US/docs/Web/JavaScript/Base64_encoding_and_decoding + + They were written by Mozilla Contributors and dedicated + to the public domain under CC0. */ + + /* Array of bytes to Base64 string decoding */ + rglwidgetClass.prototype.b64ToUint6 = function(nChr) { + return nChr > 64 && nChr < 91 ? nChr - 65 : + nChr > 96 && nChr < 123 ? nChr - 71 : + nChr > 47 && nChr < 58 ? nChr + 4 : + nChr === 43 ? 62 : + nChr === 47 ? 63 : + 0; + }; + + /* jshint bitwise:false */ + rglwidgetClass.prototype.base64DecToArr = function(sBase64, nBlocksSize) { + var sB64Enc = sBase64.replace(/[^A-Za-z0-9\+\/]/g, ""), + nInLen = sB64Enc.length, + nOutLen = nBlocksSize ? Math.ceil((nInLen * 3 + 1 >> 2) / nBlocksSize) * nBlocksSize : nInLen * 3 + 1 >> 2, + taBytes = new Uint8Array(nOutLen); + for (var nMod3, nMod4, nUint24 = 0, nOutIdx = 0, nInIdx = 0; nInIdx < nInLen; nInIdx++) { + nMod4 = nInIdx & 3; + nUint24 |= this.b64ToUint6(sB64Enc.charCodeAt(nInIdx)) << 6 * (3 - nMod4); + if (nMod4 === 3 || nInLen - nInIdx === 1) { + for (nMod3 = 0; nMod3 < 3 && nOutIdx < nOutLen; nMod3++, nOutIdx++) { + taBytes[nOutIdx] = nUint24 >>> (16 >>> nMod3 & 24) & 255; + } + nUint24 = 0; + } + } + return taBytes; + }; + /* jshint bitwise:true */ + + rglwidgetClass.prototype.getArrayBuffer = function(base64) { + return this.base64DecToArr(base64, 4).buffer; + }; + + rglwidgetClass.prototype.getBufferedData = function(v) { + return this.readAccessor(parseInt(v, 10), this.scene.buffer); + }; + + rglwidgetClass.prototype.readAccessor = function(acc, buf) { + var typeSignedByte = 5120, + typeUnsignedByte = 5121, + typeSignedShort = 5122, + typeUnsignedShort = 5123, + typeSignedInt = 5124, + typeUnsignedInt = 5125, + typeFloat = 5126, + typeDouble = 5130, + accessor = buf.accessors[acc], + bufferView = buf.bufferViews[accessor.bufferView], + buffer = buf.buffers[bufferView.buffer], + bytes, + lens = { + SCALAR: 1, + VEC2: 2, + VEC3: 3, + VEC4: 4, + MAT2: 4, + MAT3: 9, + MAT4: 16 + }, + rowsizes = { + SCALAR: 1, + VEC2: 2, + VEC3: 3, + VEC4: 4, + MAT2: 2, + MAT3: 3, + MAT4: 4 + }, + offset = 0, + len = lens[accessor.type], + rowsize = rowsizes[accessor.type], + count = len * accessor.count, + nrows = count / rowsize, + values, arr = [], row, i, j, k; + + if (typeof buffer.bytes === "string") + buffer.bytes = this.getArrayBuffer(buffer.bytes); + + bytes = buffer.bytes; + + if (typeof accessor.byteOffset !== "undefined") + offset += accessor.byteOffset; + + if (typeof bufferView.byteOffset !== "undefined") + offset += bufferView.byteOffset; + + switch (accessor.componentType) { + case typeSignedByte: + values = new Int8Array(buffer.bytes, offset, count); + break; + + case typeUnsignedByte: + values = new Uint8Array(buffer.bytes, offset, count); + break; + + case typeSignedShort: + values = new Int16Array(buffer.bytes, offset, count); + break; + + case typeUnsignedShort: + values = new Uint16Array(buffer.bytes, offset, count); + break; + + case typeSignedInt: + values = new Int32Array(buffer.bytes, offset, count); + break; + + case typeUnsignedInt: + values = new Uint32Array(buffer.bytes, offset, count); + break; + + case typeFloat: + values = new Float32Array(buffer.bytes, offset, count); + break; + + case typeDouble: + values = new Float64Array(buffer.bytes, offset, count); + break; + } + + /* This is all very inefficient, but is convenient + to work with the old code. */ + k = 0; + for (i = 0; i < nrows; i++) { + row = []; + for (j = 0; j < rowsize; j++) { + if (accessor.normalized) { + switch(accessor.componentType) { + case typeSignedByte: + row.push(Math.max(values[k++]/127, -1.0)); + break; + case typeSignedShort: + row.push(Math.max(values[k++]/32767, -1.0)); + break; + case typeUnsignedByte: + row.push(values[k++]/255); + break; + case typeUnsignedShort: + row.push(values[k++]/65535); + break; + } + } else + row.push(values[k++]); + } + arr.push(row); + } + return arr; + }; + + rglwidgetClass.prototype.expandBufferedFields = function(obj) { + /* this list needs to match the one in convertScene.R */ + var fields = ["vertices", "normals", "indices", + "texcoords", "colors", "centers"], i, field; + for (i = 0; i < fields.length; i++) { + field = obj[fields[i]]; + if (this.isBuffered(field)) + obj[fields[i]] = this.getBufferedData(field); + } + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/controls.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/controls.src.js new file mode 100644 index 00000000..ffc7be8f --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/controls.src.js @@ -0,0 +1,591 @@ + + /** + * Change the displayed subset + * @param { Object } el - Element of the control; not used. + * @param { Object } control - The subset control data. + */ + rglwidgetClass.prototype.subsetSetter = function(el, control) { + if (typeof control.subscenes === "undefined" || + control.subscenes === null) + control.subscenes = this.scene.rootSubscene; + var value = Math.round(control.value), + subscenes = [].concat(control.subscenes), + fullset = [].concat(control.fullset), + i, j, subsceneid, + adds = [], deletes = []; + if (rglwidgetClass.missing(value)) + value = control.value = 0; + if (control.accumulate) + for (i=0; i <= value; i++) + adds = adds.concat(control.subsets[i]); + else + adds = adds.concat(control.subsets[value]); + deletes = fullset.filter(function(x) { return adds.indexOf(x) < 0; }); + for (i = 0; i < subscenes.length; i++) { + subsceneid = subscenes[i]; + if (typeof this.getObj(subsceneid) === "undefined") + this.alertOnce("typeof object is undefined"); + for (j = 0; j < adds.length; j++) + this.addToSubscene(adds[j], subsceneid); + for (j = 0; j < deletes.length; j++) + this.delFromSubscene(deletes[j], subsceneid); + } + }; + + /** + * Change the requested property + * @param { Object } el - Element of the control; not used. + * @param { Object } control - The property setter control data. + */ + rglwidgetClass.prototype.propertySetter = function(el, control) { + var value = control.value, + values = [].concat(control.values), + svals = [].concat(control.param), + direct = values[0] === null, + entries = [].concat(control.entries), + ncol = entries.length, + nrow = values.length/ncol, + properties = rglwidgetClass.repeatToLen(control.properties, ncol), + objids = rglwidgetClass.repeatToLen(control.objids, ncol), + property, objid = objids[0], + obj = this.getObj(objid), + propvals, i, j, v1, v2, p, entry, gl, needsBinding, + newprop, newid, + + getPropvals = function() { + if (property === "userMatrix") + return obj.par3d.userMatrix.getAsArray(); + else if (property === "scale" || property === "FOV" || property === "zoom") + return [].concat(obj.par3d[property]); + else + return [].concat(obj[property]); + }, + + putPropvals = function(newvals) { + if (newvals.length === 1) + newvals = newvals[0]; + if (property === "userMatrix") + obj.par3d.userMatrix.load(newvals); + else if (property === "scale" || property === "FOV" || property === "zoom") + obj.par3d[property] = newvals; + else + obj[property] = newvals; + }; + + if (direct && typeof value === "undefined") + return; + + if (control.interp) { + values = values.slice(0, ncol).concat(values). + concat(values.slice(ncol*(nrow-1), ncol*nrow)); + svals = [-Infinity].concat(svals).concat(Infinity); + for (i = 1; i < svals.length; i++) { + if (value <= svals[i]) { + if (svals[i] === Infinity) + p = 1; + else + p = (svals[i] - value)/(svals[i] - svals[i-1]); + break; + } + } + } else if (!direct) { + value = Math.round(value); + } + + for (j=0; j value - svals[j-1]) + j = j - 1; + } + break; + } + } + + obj = this.getObj(control.objid); + // First, make sure color attributes vary in original + if (typeof obj.vOffsets !== "undefined") { + varies = true; + for (k = 0; k < ncol; k++) { + attrib = attributes[k]; + if (typeof attrib !== "undefined") { + ofs = obj.vOffsets[ofss[attrib]]; + if (ofs < 0) { + switch(attrib) { + case "alpha": + case "red": + case "green": + case "blue": + obj.colors = [obj.colors[0], obj.colors[0]]; + break; + } + varies = false; + } + } + } + if (!varies) + this.initObjId(control.objid); + } + propvals = obj.values; + aliases = obj.alias; + if (typeof aliases === "undefined") + aliases = []; + for (k=0; k= 0) { + if (ofs < 3) { + if (obj.normals[vertex][ofs] !== newval) { // Assume no aliases here... + obj.normals[vertex][ofs] = newval; + obj.initialized = false; + } + } else { + if (obj.offsets[vertex][0] !== newval) { + obj.offsets[vertex][0] = newval; + obj.initialized = false; + } + } + continue; + } + } + // Not a plane setting... + ofs = obj.vOffsets[ofss[attrib]]; + if (ofs < 0) + this.alertOnce("Attribute '"+attrib+"' not found in object "+control.objid); + else { + stride = obj.vOffsets.stride; + ofs = ofs + pos[attrib]; + entry = vertex*stride + ofs; + propvals[entry] = newval; + if (typeof alias !== "undefined") + for (a = 0; a < alias.length; a++) + propvals[alias[a]*stride + ofs] = newval; + } + } + if (typeof obj.buf !== "undefined") { + var gl = this.gl || this.initGL(); + gl.bindBuffer(gl.ARRAY_BUFFER, obj.buf); + gl.bufferData(gl.ARRAY_BUFFER, propvals, gl.STATIC_DRAW); + } + }; + + /** + * Change the requested vertex properties by age + * @param { Object } el - Element of the control; not used. + * @param { Object } control - The age setter control data. + */ + rglwidgetClass.prototype.ageSetter = function(el, control) { + var objids = [].concat(control.objids), + nobjs = objids.length, + time = control.value, + births = [].concat(control.births), + ages = [].concat(control.ages), + steps = births.length, + j = Array(steps), + p = Array(steps), + i, k, l, age, j0, propvals, stride, ofs, objid, obj, + attrib, dim, varies, alias, aliases, a, d, + attribs = ["colors", "alpha", "radii", "vertices", + "normals", "origins", "texcoords", + "x", "y", "z", + "red", "green", "blue"], + ofss = ["cofs", "cofs", "radofs", "vofs", + "nofs", "oofs", "tofs", + "vofs", "vofs", "vofs", + "cofs", "cofs", "cofs"], + dims = [3,1,1,3, + 3,2,2, + 1,1,1, + 1,1,1], + pos = [0,3,0,0, + 0,0,0, + 0,1,2, + 0,1,2]; + /* Infinity doesn't make it through JSON */ + ages[0] = -Infinity; + ages[ages.length-1] = Infinity; + for (i = 0; i < steps; i++) { + if (births[i] !== null) { // NA in R becomes null + age = time - births[i]; + for (j0 = 1; age > ages[j0]; j0++); + if (ages[j0] === Infinity) + p[i] = 1; + else if (ages[j0] > ages[j0-1]) + p[i] = (ages[j0] - age)/(ages[j0] - ages[j0-1]); + else + p[i] = 0; + j[i] = j0; + } + } + // First, make sure color attributes vary in original + for (l = 0; l < nobjs; l++) { + objid = objids[l]; + obj = this.getObj(objid); + varies = true; + if (typeof obj.vOffsets === "undefined") + continue; + for (k = 0; k < attribs.length; k++) { + attrib = control[attribs[k]]; + if (typeof attrib !== "undefined") { + ofs = obj.vOffsets[ofss[k]]; + if (ofs < 0) { + switch(attribs[k]) { + case "colors": + case "alpha": + case "red": + case "green": + case "blue": + obj.colors = [obj.colors[0], obj.colors[0]]; + break; + } + varies = false; + } + } + } + if (!varies) + this.initObjId(objid); + } + for (l = 0; l < nobjs; l++) { + objid = objids[l]; + obj = this.getObj(objid); + if (typeof obj.vOffsets === "undefined") + continue; + aliases = obj.alias; + if (typeof aliases === "undefined") + aliases = []; + propvals = obj.values; + stride = obj.vOffsets.stride; + for (k = 0; k < attribs.length; k++) { + attrib = control[attribs[k]]; + if (typeof attrib !== "undefined") { + ofs = obj.vOffsets[ofss[k]]; + if (ofs >= 0) { + dim = dims[k]; + ofs = ofs + pos[k]; + for (i = 0; i < steps; i++) { + alias = aliases[i]; + if (births[i] !== null) { + for (d=0; d < dim; d++) { + propvals[i*stride + ofs + d] = p[i]*attrib[dim*(j[i]-1) + d] + (1-p[i])*attrib[dim*j[i] + d]; + if (typeof alias !== "undefined") + for (a=0; a < alias.length; a++) + propvals[alias[a]*stride + ofs + d] = propvals[i*stride + ofs + d]; + } + } + } + } else + this.alertOnce("\'"+attribs[k]+"\' property not found in object "+objid); + } + } + obj.values = propvals; + if (typeof obj.buf !== "undefined") { + var gl = this.gl || this.initGL(); + gl.bindBuffer(gl.ARRAY_BUFFER, obj.buf); + gl.bufferData(gl.ARRAY_BUFFER, obj.values, gl.STATIC_DRAW); + } + } + }; + + /** + * Bridge to old style control + * @param { Object } el - Element of the control; not used. + * @param { Object } control - The bridge control data. + */ + rglwidgetClass.prototype.oldBridge = function(el, control) { + var attrname, global = window[control.prefix + "rgl"]; + if (global) + for (attrname in global) + this[attrname] = global[attrname]; + window[control.prefix + "rgl"] = this; + }; + + /** + * Set up a player control + * @param { Object } el - The player control element + * @param { Object } control - The player data. + */ + rglwidgetClass.prototype.Player = function(el, control) { + var + self = this, + components = [].concat(control.components), + buttonLabels = [].concat(control.buttonLabels), + + Tick = function() { /* "this" will be a timer */ + var i, + nominal = this.value, + slider = this.Slider, + labels = this.outputLabels, + output = this.Output, + step; + if (typeof slider !== "undefined" && nominal !== slider.value) + slider.value = nominal; + if (typeof output !== "undefined") { + step = Math.round((nominal - output.sliderMin)/output.sliderStep); + if (labels !== null) { + output.innerHTML = labels[step]; + } else { + step = step*output.sliderStep + output.sliderMin; + output.innerHTML = step.toPrecision(output.outputPrecision); + } + } + for (i=0; i < this.actions.length; i++) { + this.actions[i].value = nominal; + } + self.applyControls(el, this.actions, false); + self.drawScene(); + }, + + OnSliderInput = function() { /* "this" will be the slider */ + this.rgltimer.value = Number(this.value); + this.rgltimer.Tick(); + }, + + addSlider = function(min, max, step, value) { + var slider = document.createElement("input"); + slider.type = "range"; + slider.min = min; + slider.max = max; + slider.step = step; + slider.value = value; + slider.oninput = OnSliderInput; + slider.sliderActions = control.actions; + slider.sliderScene = this; + slider.className = "rgl-slider"; + slider.id = el.id + "-slider"; + el.rgltimer.Slider = slider; + slider.rgltimer = el.rgltimer; + el.appendChild(slider); + }, + + addLabel = function(labels, min, step, precision) { + var output = document.createElement("output"); + output.sliderMin = min; + output.sliderStep = step; + output.outputPrecision = precision; + output.className = "rgl-label"; + output.id = el.id + "-label"; + el.rgltimer.Output = output; + el.rgltimer.outputLabels = labels; + el.appendChild(output); + }, + + addButton = function(which, label, active) { + var button = document.createElement("input"), + onclicks = {Reverse: function() { this.rgltimer.reverse();}, + Play: function() { this.rgltimer.play(); + this.value = this.rgltimer.enabled ? this.inactiveValue : this.activeValue; }, + Slower: function() { this.rgltimer.slower(); }, + Faster: function() { this.rgltimer.faster(); }, + Reset: function() { this.rgltimer.reset(); }, + Step: function() { this.rgltimer.step(); } + }; + button.rgltimer = el.rgltimer; + button.type = "button"; + button.value = label; + button.activeValue = label; + button.inactiveValue = active; + if (which === "Play") + button.rgltimer.PlayButton = button; + button.onclick = onclicks[which]; + button.className = "rgl-button"; + button.id = el.id + "-" + which; + el.appendChild(button); + }; + + if (typeof control.reinit !== "undefined" && control.reinit !== null) { + control.actions.reinit = control.reinit; + } + el.rgltimer = new rgltimerClass(Tick, control.start, control.interval, control.stop, + control.step, control.value, control.rate, control.loop, control.actions); + for (var i=0; i < components.length; i++) { + switch(components[i]) { + case "Slider": addSlider(control.start, control.stop, + control.step, control.value); + break; + case "Label": addLabel(control.labels, control.start, + control.step, control.precision); + break; + default: + addButton(components[i], buttonLabels[i], control.pause); + } + } + el.rgltimer.Tick(); + }; + + /** + * Apply all registered controls + * @param { Object } el - DOM element of the control + * @param { Object } x - List of actions to apply + * @param { boolean } [draw=true] - Whether to redraw after applying + */ + rglwidgetClass.prototype.applyControls = function(el, x, draw) { + var self = this, reinit = x.reinit, i, control, type; + for (i = 0; i < x.length; i++) { + control = x[i]; + type = control.type; + self[type](el, control); + } + if (typeof reinit !== "undefined" && reinit !== null) { + reinit = [].concat(reinit); + for (i = 0; i < reinit.length; i++) + self.getObj(reinit[i]).initialized = false; + } + if (typeof draw === "undefined" || draw) + self.drawScene(); + }; + + /** + * Handler for scene change + * @param { Object } message - What sort of scene change to do? + */ + rglwidgetClass.prototype.sceneChangeHandler = function(message) { + var self = document.getElementById(message.elementId).rglinstance, + objs = message.objects, mat = message.material, + root = message.rootSubscene, + initSubs = message.initSubscenes, + redraw = message.redrawScene, + skipRedraw = message.skipRedraw, + deletes, subs, allsubs = [], i,j; + if (typeof message.delete !== "undefined") { + deletes = [].concat(message.delete); + if (typeof message.delfromSubscenes !== "undefined") + subs = [].concat(message.delfromSubscenes); + else + subs = []; + for (i = 0; i < deletes.length; i++) { + for (j = 0; j < subs.length; j++) { + self.delFromSubscene(deletes[i], subs[j]); + } + delete self.scene.objects[deletes[i]]; + } + } + if (typeof objs !== "undefined") { + Object.keys(objs).forEach(function(key){ + key = parseInt(key, 10); + self.scene.objects[key] = objs[key]; + self.initObjId(key); + var obj = self.getObj(key), + subs = [].concat(obj.inSubscenes), k; + allsubs = allsubs.concat(subs); + for (k = 0; k < subs.length; k++) + self.addToSubscene(key, subs[k]); + }); + } + if (typeof mat !== "undefined") { + self.scene.material = mat; + } + if (typeof root !== "undefined") { + self.scene.rootSubscene = root; + } + if (typeof initSubs !== "undefined") + allsubs = allsubs.concat(initSubs); + allsubs = self.unique(allsubs); + for (i = 0; i < allsubs.length; i++) { + self.initSubscene(allsubs[i]); + } + if (typeof skipRedraw !== "undefined") { + root = self.getObj(self.scene.rootSubscene); + root.par3d.skipRedraw = skipRedraw; + } + if (redraw) + self.drawScene(); + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/draw.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/draw.src.js new file mode 100644 index 00000000..90ce52e4 --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/draw.src.js @@ -0,0 +1,1370 @@ + /** + * Methods related to drawing + * @name ___METHODS_FOR_DRAWING___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + /** + * Start drawing + * @returns { boolean } Previous state + */ + rglwidgetClass.prototype.startDrawing = function() { + var value = this.drawing; + this.drawing = true; + return value; + }; + + /** + * Stop drawing and check for context loss + * @param { boolean } saved - Previous state + */ + rglwidgetClass.prototype.stopDrawing = function(saved) { + this.drawing = saved; + if (!saved && this.gl && this.gl.isContextLost()) + this.restartCanvas(); + }; + + /** + * Update the triangles used to display a plane + * @param { number } id - id of the plane + * @param { Object } bbox - bounding box in which to display the plane + */ + rglwidgetClass.prototype.planeUpdateTriangles = function(obj, bbox) { + var perms = [[0,0,1], [1,2,2], [2,1,0]], + x, xrow, elem, A, d, nhits, i, j, k, u, v, w, intersect, which, v0, v2, vx, reverse, + face1 = [], face2 = [], normals = [], + nPlanes = obj.normals.length, idx, center; + obj.bbox = bbox; + obj.vertices = []; + obj.centers = []; + obj.initialized = false; + for (elem = 0; elem < nPlanes; elem++) { +// Vertex Av = normal.getRecycled(elem); + x = []; + A = obj.normals[elem]; + d = obj.offsets[elem][0]; + nhits = 0; + for (i=0; i<3; i++) + for (j=0; j<2; j++) + for (k=0; k<2; k++) { + u = perms[0][i]; + v = perms[1][i]; + w = perms[2][i]; + if (A[w] !== 0.0) { + intersect = -(d + A[u]*bbox[j+2*u] + A[v]*bbox[k+2*v])/A[w]; + if (bbox[2*w] < intersect && intersect < bbox[1+2*w]) { + xrow = []; + xrow[u] = bbox[j+2*u]; + xrow[v] = bbox[k+2*v]; + xrow[w] = intersect; + x.push(xrow); + face1[nhits] = j + 2*u; + face2[nhits] = k + 2*v; + nhits++; + } + } + } + + if (nhits > 3) { + /* Re-order the intersections so the triangles work */ + for (i=0; i i+1) { + rglwidgetClass.swap(x, i+1, which); + rglwidgetClass.swap(face1, i+1, which); + rglwidgetClass.swap(face2, i+1, which); + } + } + } + if (nhits >= 3) { + /* Put in order so that the normal points out the FRONT of the faces */ + v0 = [x[0][0] - x[1][0] , x[0][1] - x[1][1], x[0][2] - x[1][2]]; + v2 = [x[2][0] - x[1][0] , x[2][1] - x[1][1], x[2][2] - x[1][2]]; + /* cross-product */ + vx = rglwidgetClass.xprod(v0, v2); + reverse = rglwidgetClass.dotprod(vx, A) > 0; + + for (i=0; i 0) { + clipplanedata = new Float32Array(4*n); + for (i=0; i < clipplaneids.length; i++) { + clip = this.getObj(clipplaneids[i]); + for (j=0; j < clip.offsets.length; j++) { + clipplanedata.set(clip.IMVClip[j], clipcheck); + clipcheck += 4; + } + } + + // Leftovers are initialized to zero, which is fine + gl.uniform4fv(obj.clipLoc, clipplanedata); + } + }; + + /** + * Do code for lighting + * @param { object } obj - Object to work with + * @param { object } subscene - Subscene to work with + */ + rglwidgetClass.prototype.doLighting = function(obj, subscene) { + var gl = this.gl, i, j, n, light, + ambient, specular, diffuse, lightDir, viewpoint, finite, + ambient0, specular0; + + gl.uniform3fv( obj.emissionLoc, obj.emission); + gl.uniform1f( obj.shininessLoc, obj.shininess); + while ((typeof subscene.lights === "undefined" || + subscene.lights.length === 0) && + typeof subscene.parent !== "undefined") + subscene = this.getObj(subscene.parent); + + if (typeof subscene.lights === "undefined") + return; + + n = subscene.lights.length; + + ambient = new Float32Array(3*n); + specular = new Float32Array(3*n); + diffuse = new Float32Array(3*n); + lightDir = new Float32Array(3*n); + viewpoint = new Int32Array(n); + finite = new Int32Array(n); + + for (i=0; i < n; i++) { + light = this.getObj(subscene.lights[i]); + if (!light.initialized) this.initObj(light); + ambient0 = this.componentProduct(light.ambient, obj.ambient); + specular0 = this.componentProduct(light.specular, obj.specular); + for (j=0; j < 3; j++) { + ambient[3*i + j] = ambient0[j]; + specular[3*i + j] = specular0[j]; + diffuse[3*i + j] = light.diffuse[j]; + lightDir[3*i + j] = light.lightDir[j]; + } + viewpoint[i] = light.viewpoint; + finite[i] = light.finite; + } + + for (i = n; i < obj.nlights; i++) { + for (j = 0; j < 3; j++) { + ambient[3*i + j] = 0.0; + specular[3*i + j] = 0.0; + diffuse[3*i + j] = 0.0; + } + } + + gl.uniform3fv( obj.ambientLoc, ambient); + gl.uniform3fv( obj.specularLoc, specular); + gl.uniform3fv( obj.diffuseLoc, diffuse); + gl.uniform3fv( obj.lightDirLoc, lightDir); + gl.uniform1iv( obj.viewpointLoc, viewpoint); + gl.uniform1iv( obj.finiteLoc, finite); + }; + + /** + * Do code for colors + * @param { object } obj - Object to work with + */ + rglwidgetClass.prototype.doColors = function(obj) { + var gl = this.gl; + if (obj.colorCount === 1) { + gl.disableVertexAttribArray( this.colLoc ); + gl.vertexAttrib4fv( this.colLoc, new Float32Array(obj.onecolor)); + return false; + } else { + gl.enableVertexAttribArray( this.colLoc ); + gl.vertexAttribPointer(this.colLoc, 4, gl.FLOAT, false, 4*obj.vOffsets.stride, 4*obj.vOffsets.cofs); + return true; + } + }; + + /** + * Do code for normals + * @param { object } obj - Object to work with + */ + rglwidgetClass.prototype.doNormals = function(obj) { + var gl = this.gl; + if (obj.vOffsets.nofs >= 0) { + gl.enableVertexAttribArray( obj.normLoc ); + gl.vertexAttribPointer(obj.normLoc, 3, gl.FLOAT, false, 4*obj.vOffsets.stride, 4*obj.vOffsets.nofs); + return true; + } else + return false; + }; + + /** + * Do code for vNormal + * @param { object } obj - Object to work with + */ + rglwidgetClass.prototype.doNormMat = function(obj) { + var gl = this.gl; + + gl.uniformMatrix4fv( obj.normMatLoc, false, new Float32Array(this.normMatrix.getAsArray()) ); + }; + + /** + * Do code for textures + * @param { object } obj - Object to work with + */ + rglwidgetClass.prototype.doTexture = function(obj) { + var gl = this.gl, + is_sphere = obj.type === "sphere"; + gl.enableVertexAttribArray( obj.texLoc ); + if (is_sphere) + gl.vertexAttribPointer(obj.texLoc, 2, gl.FLOAT, false, 4*this.sphere.vOffsets.stride, 4*this.sphere.vOffsets.tofs); + else + gl.vertexAttribPointer(obj.texLoc, 2, gl.FLOAT, false, 4*obj.vOffsets.stride, 4*obj.vOffsets.tofs); + gl.activeTexture(gl.TEXTURE0); + gl.bindTexture(gl.TEXTURE_2D, obj.texture); + gl.uniform1i( obj.sampler, 0); + return true; + }; + + /** + * Do code for user attributes + * @param { object } obj - Object to work with + */ + rglwidgetClass.prototype.doUserAttributes = function(obj) { + if (typeof obj.userAttributes !== "undefined") { + var gl = this.gl; + for (var attr in obj.userAttribSizes) { // Not all attributes may have been used + gl.enableVertexAttribArray( obj.userAttribLocations[attr] ); + gl.vertexAttribPointer( obj.userAttribLocations[attr], obj.userAttribSizes[attr], + gl.FLOAT, false, 4*obj.vOffsets.stride, 4*obj.userAttribOffsets[attr]); + } + } + }; + + /** + * Do code for user uniforms + * @param { object } obj - Object to work with + */ + rglwidgetClass.prototype.doUserUniforms = function(obj) { + var gl = this.gl, attr; + if (typeof obj.userUniforms !== "undefined") { + for (attr in obj.userUniformLocations) { + var loc = obj.userUniformLocations[attr]; + if (loc !== null) { + var uniform = obj.userUniforms[attr]; + if (typeof uniform !== "undefined") { + var dim = rglwidgetClass.arrayDim(uniform); + if (dim.length === 0) + gl.uniform1f(loc, uniform); + else if (dim.length === 1) { + uniform = new Float32Array(uniform); + switch(uniform.length) { + case 2: gl.uniform2fv(loc, uniform); break; + case 3: gl.uniform3fv(loc, uniform); break; + case 4: gl.uniform4fv(loc, uniform); break; + default: console.warn("bad uniform length"); + } + } else if (dim.length === 2 && dim[0] === 4 && dim[1] === 4) + gl.uniformMatrix4fv(loc, false, new Float32Array(rglwidgetClass.flatten(uniform))); + else if (dim.length === 2) { + uniform = new Float32Array(rglwidgetClass.flatten(uniform)); + switch(dim[[1]]) { + case 1: gl.uniform1fv(loc, uniform); break; + case 2: gl.uniform2fv(loc, uniform); break; + case 3: gl.uniform3fv(loc, uniform); break; + case 4: gl.uniform4fv(loc, uniform); break; + default: console.warn("bad uniform column count"); + } + } else + console.warn("unsupported uniform shape"); + } + } + } + } + if (typeof obj.userTextures !== "undefined") { + var has_texture = rglwidgetClass.isSet(obj.flags, rglwidgetClass.f_has_texture), + texnum = has_texture - 1; + for (attr in obj.userTextures) { + var texture = obj.userTextures[attr]; + if (texture.sampler !== null) { + texnum += 1; + gl.activeTexture(gl.TEXTURE0 + texnum); + gl.bindTexture(gl.TEXTURE_2D, texture.texture); + gl.uniform1i( texture.sampler, texnum); + } + } + } + }; + + /** + * Load indices for complex drawing + * @param { object } obj - Object to work with + * @param { numeric } pass - Which pass of drawing? + * @param { array } indices - Indices to draw + */ + rglwidgetClass.prototype.doLoadIndices = function(obj, pass, indices) { + var gl = this.gl, + f = obj.f[pass], + type = obj.type, + fat_lines = rglwidgetClass.isSet(obj.flags, rglwidgetClass.f_fat_lines), + fnew, step; + switch(type){ + case "points": + step = 1; + break; + case "abclines": + case "lines": + if (fat_lines) + step = 6; + else + step = 2; + break; + case "linestrip": + if (fat_lines) + step = 6; + else + step = 1; + break; + case "sphere": + case "planes": + case "triangles": + step = 3; + break; + case "text": + case "sprites": + case "quads": + case "surface": + step = 6; + break; + default: + console.error("loadIndices for "+type); + return 0; + } + if (obj.index_uint) + fnew = new Uint32Array(step * indices.length); + else + fnew = new Uint16Array(step * indices.length); + for (var i = 0; i < indices.length; i++) { + for (var j = 0; j < step; j++) { + fnew[step*i + j] = f[step*indices[i] + j]; + } + } + gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, fnew, gl.DYNAMIC_DRAW); + return fnew.length; + }; + + /** + * Do code for depth masking + * @param { boolean } mask - whether to mask + */ + rglwidgetClass.prototype.doMasking = function(mask) { + var gl = this.gl; + gl.depthMask(mask); + }; + + /** + * Do code for alpha blending + * @param { boolean } blend - Whether to blend. + * @param { integer } objid - Object id + */ + rglwidgetClass.prototype.doBlending = function(blend, objid) { + var gl = this.gl, blendfunc, obj, + blends = {zero: gl.ZERO, + one: gl.ONE, + src_color: gl.SRC_COLOR, + one_minus_src_color: gl.ONE_MINUS_SRC_COLOR, + dst_color: gl.DST_COLOR, + one_minus_dst_color: gl.ONE_MINUS_DST_COLOR, + src_alpha: gl.SRC_ALPHA, + one_minus_src_alpha: gl.ONE_MINUS_SRC_ALPHA, + dst_alpha: gl.DST_ALPHA, + one_minus_dst_alpha: gl.ONE_MINUS_DST_ALPHA, + constant_color: gl.CONSTANT_COLOR, + one_minus_constant_color: gl.ONE_MINUS_CONSTANT_COLOR, + constant_alpha: gl.CONSTANT_ALPHA, + one_minus_constant_alpha: gl.ONE_MINUS_CONSTANT_ALPHA, + src_alpha_saturate: gl.SRC_ALPHA_SATURATE}; + if (blend) { + obj = this.getObj(objid); + blendfunc = this.getMaterial(obj, "blend"); + gl.blendFuncSeparate(blends[blendfunc[0]], + blends[blendfunc[1]], + gl.ONE, gl.ONE); + gl.enable(gl.BLEND); + } else { + gl.disable(gl.BLEND); + } + }; + + /** + * Set up for fog in the subscene + * @param { object } obj - background object + * @param { object } subscene - which subscene + */ + rglwidgetClass.prototype.doFog = function(obj, subscene) { + var gl = this.gl, fogmode, color, + observer = subscene.par3d.observer[2], + sintheta = Math.sin(subscene.par3d.FOV*Math.PI/180/2), + parms = [this.frustum.near - 2*observer, + this.frustum.far - 2*observer, + this.fogScale, + (1-sintheta)/(1+sintheta)]; + if (typeof this.fogType === "undefined") + this.fogType = "none"; + if (typeof this.fogScale === "undefined") + parms[2] = 1; + if (sintheta === 0) + parms[3] = 1/3; + switch(this.fogType){ + case "none": fogmode = 0; break; + case "linear": + fogmode = 1; break; + case "exp": + fogmode = 2; break; + case "exp2": + fogmode = 3; + break; + default: console.error("Unknown fogtype "+this.fogType); + } + gl.uniform1i(obj.uFogMode, fogmode); + color = this.fogColor; + gl.uniform3f(obj.uFogColor, color[0], color[1], color[2]); + gl.uniform4f(obj.uFogParms, parms[0], parms[1], parms[2], parms[3]); + }; + + /* The draw methods are called twice. When + this.opaquePass is true, they should draw opaque parts + of the scene, and return the list of transparent + pieces. Here context is the context array on input, + modified when the matrices are changed. + When this.opaquePass is false, the context argument + contains a "piece", i.e. an ordered list of parts + of the object to draw. */ + + /** + * Draw simple object + * @param { object } obj - Object to draw + * @param { object } subscene - which subscene + * @param { array } context - Which context are we in? + */ + rglwidgetClass.prototype.drawSimple = function(obj, subscene, context) { + var + fl, + is_transparent, + type = obj.type, + gl = this.gl || this.initGL(), + count, + pass, mode, pmode, + enabled = {}; + + if (!obj.initialized) + this.initObj(obj); + + if (this.texturesLoading) + return[]; + + count = obj.vertexCount; + if (!count) + return []; + + fl = obj.defFlags; + is_transparent = fl.is_transparent || obj.someHidden; + + if (is_transparent && this.opaquePass) + return this.getPieces(context, obj.id, 0, obj); + + this.doDepthTest(obj); + + this.doMasking(this.getMaterial(obj, "depth_mask")); + + gl.useProgram(obj.prog); + + this.doPolygonOffset(obj); + + gl.bindBuffer(gl.ARRAY_BUFFER, obj.buf); + + gl.uniformMatrix4fv( obj.prMatLoc, false, new Float32Array(this.prMatrix.getAsArray()) ); + gl.uniformMatrix4fv( obj.mvMatLoc, false, new Float32Array(this.mvMatrix.getAsArray()) ); + + this.doClipping(obj, subscene); + + if (fl.needs_vnormal) + this.doNormMat(obj); + + if (fl.is_lit) + this.doLighting(obj, subscene); + + if (fl.has_fog) + this.doFog(obj, subscene); + + this.doUserAttributes(obj); + + this.doUserUniforms(obj); + + gl.enableVertexAttribArray( this.posLoc ); + enabled.posLoc = true; + + if (fl.has_texture || obj.type === "text") + enabled.texLoc = this.doTexture(obj); + + enabled.colLoc = this.doColors(obj); + enabled.normLoc = this.doNormals(obj); + + if (fl.fixed_size) { + gl.uniform3f( obj.textScaleLoc, 0.75/this.vp.width, 0.75/this.vp.height, 1.0); + } + + if (fl.fixed_quads) { + gl.enableVertexAttribArray( obj.ofsLoc ); + enabled.ofsLoc = true; + gl.vertexAttribPointer(obj.ofsLoc, 3, gl.FLOAT, false, 4*obj.vOffsets.stride, 4*obj.vOffsets.oofs); + } + + for (pass = 0; pass < obj.passes; pass++) { + pmode = obj.pmode[pass]; + if (pmode === "culled") + continue; + + mode = fl.fat_lines && (fl.is_lines || pmode === "lines") ? "TRIANGLES" : this.mode4type[type]; + + if (fl.is_twosided) { + gl.uniform1i(obj.frontLoc, pass !== 0); + if (fl.has_normals) { + gl.uniformMatrix4fv(obj.invPrMatLoc, false, new Float32Array(this.invPrMatrix.getAsArray())); + } + } + + gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, obj.ibuf[pass]); + if (!this.opaquePass) { + if (type === "sphere" && obj.fastTransparency) + count = this.doLoadIndices(obj, pass, this.sphere.fastpieces[0].indices); + else + count = this.doLoadIndices(obj, pass, context.indices); + } else { + count = obj.f[pass].length; + gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, obj.f[pass], gl.STATIC_DRAW); + } + if (!fl.is_lines && pmode === "lines" && !fl.fat_lines) { + mode = "LINES"; + } else if (pmode === "points") { + mode = "POINTS"; + } + + if ((fl.is_lines || pmode === "lines") && fl.fat_lines) { + gl.enableVertexAttribArray(obj.pointLoc); + enabled.pointLoc = true; + gl.vertexAttribPointer(obj.pointLoc, 2, gl.FLOAT, false, 4*obj.vOffsets.stride, 4*obj.vOffsets.pointofs); + gl.enableVertexAttribArray(obj.nextLoc ); + enabled.nextLoc = true; + gl.vertexAttribPointer(obj.nextLoc, 3, gl.FLOAT, false, 4*obj.vOffsets.stride, 4*obj.vOffsets.nextofs); + gl.uniform1f(obj.aspectLoc, this.vp.width/this.vp.height); + gl.uniform1f(obj.lwdLoc, this.getMaterial(obj, "lwd")/this.vp.height); + } + + gl.vertexAttribPointer(this.posLoc, 3, gl.FLOAT, false, 4*obj.vOffsets.stride, 4*obj.vOffsets.vofs); + + gl.drawElements(gl[mode], count, obj.index_uint ? gl.UNSIGNED_INT : gl.UNSIGNED_SHORT, 0); + } + this.disableArrays(obj, enabled); + return []; + }; + + /** + * Draw planes object + * @param { object } obj - Object to draw + * @param { object } subscene - which subscene + * @param { array } context - Which context are we in? + */ + rglwidgetClass.prototype.drawPlanes = function(obj, subscene, context) { + if (this.opaquePass && (obj.bbox !== subscene.par3d.bbox || !obj.initialized)) { + this.planeUpdateTriangles(obj, subscene.par3d.bbox); + } + return this.drawSimple(obj, subscene, context); + }; + + /** + * @param { object } obj - object to draw + * @param { object } subscene + * @param { array } context + * @description + * Draw spheres in a subscene
    + * + * Drawing spheres happens in six ways:
    + * 1 opaquepass, not transparent: transform and draw this.sphere count times
    + * 2 opaquepass, transparent, not fast: transform & collect sphere pieces count times
    + * 3 opaquepass, transparent, fast: order the centres into separate pieces, order this.sphere once
    + * 4 not opaquepass, not transparent: do nothing
    + * 5 not opaquepass, transparent, not fast: transform for one sphere, draw one merged piece
    + * 6 not opaquepass, transparent, fast: transform for one sphere, draw this.sphere in fixed order.
    + **/ + + rglwidgetClass.prototype.drawSpheres = function(obj, subscene, context) { + var flags = obj.flags, + is_transparent = rglwidgetClass.isSet(flags, rglwidgetClass.f_is_transparent), + sphereMV, baseofs, ofs, sscale, i, + count, nc, scount, scale, indices, sphereNorm, + enabled = {}, drawing, + saveNorm = new CanvasMatrix4(this.normMatrix), + saveMV = new CanvasMatrix4(this.mvMatrix), + savePRMV = null, + result = [], idx, margin = obj.material.margin; + + if (typeof margin !== "undefined") + if (!this.marginVecToDataVec(obj, subscene)) + return []; + + if (!obj.initialized) + this.initObj(obj); + + count = obj.vertexCount; + if (!count) + return []; + + is_transparent = is_transparent || obj.someHidden; + + if (!this.opaquePass && !is_transparent) + return []; + + if (this.prmvMatrix !== null) + savePRMV = new CanvasMatrix4(this.prmvMatrix); + + scale = subscene.par3d.scale; + sphereNorm = new CanvasMatrix4(); + sphereNorm.scale(scale[0], scale[1], scale[2]); + sphereNorm.multRight(saveNorm); + this.normMatrix = sphereNorm; + + if (this.opaquePass) { + context = context.slice(); + context.push(obj.id); + } + + drawing = this.opaquePass !== is_transparent; + if (drawing) { + nc = obj.colorCount; + if (nc === 1) { + this.sphere.onecolor = obj.onecolor; + } + } + + this.initShapeFromObj(this.sphere, obj); + + if (!this.opaquePass && obj.fastTransparency && typeof this.sphere.fastpieces === "undefined") { + this.sphere.fastpieces = this.getPieces(context.context, obj.id, 0, this.sphere); + this.sphere.fastpieces = this.sortPieces(this.sphere.fastpieces); + this.sphere.fastpieces = this.mergePieces(this.sphere.fastpieces); + } + + if (this.opaquePass) + scount = count; + else { + indices = context.indices; + if (obj.fastTransparency) + scount = indices.length; /* Each item gives the center of a whole sphere */ + else + scount = 1; /* Each item is a fragment of the sphere, at location subid */ + } + for (i = 0; i < scount; i++) { + sphereMV = new CanvasMatrix4(); + if (this.opaquePass) + idx = i; + else if (obj.fastTransparency) + idx = indices[i]; + else + idx = context.subid; + if (typeof idx === "undefined") + console.error("idx is undefined"); + baseofs = idx*obj.vOffsets.stride; + ofs = baseofs + obj.vOffsets.radofs; + sscale = obj.values[ofs]; + + sphereMV.scale(sscale/scale[0], sscale/scale[1], sscale/scale[2]); + sphereMV.translate(obj.values[baseofs], + obj.values[baseofs+1], + obj.values[baseofs+2]); + sphereMV.multRight(saveMV); + this.mvMatrix = sphereMV; + this.setnormMatrix2(); + this.setprmvMatrix(); + if (drawing) { + if (nc > 1) { + this.sphere.onecolor = obj.values.slice(baseofs + obj.vOffsets.cofs, baseofs + obj.vOffsets.cofs + 4); + } + this.drawSimple(this.sphere, subscene, context); + } else + result = result.concat(this.getSpherePieces(context, i, obj)); + } + if (drawing) + this.disableArrays(obj, enabled); + this.normMatrix = saveNorm; + this.mvMatrix = saveMV; + this.prmvMatrix = savePRMV; + + return result; + }; + + /** + * Prepare clipplanes for drawing + * @param { object } obj - clip planes object + */ + rglwidgetClass.prototype.drawClipplanes = function(obj) { + var count = obj.offsets.length, + IMVClip = []; + for (var i=0; i < count; i++) { + IMVClip[i] = rglwidgetClass.multMV(this.invMatrix, obj.vClipplane.slice(4*i, 4*(i+1))); + } + obj.IMVClip = IMVClip; + return []; + }; + + /** + * Prepare linestrip for drawing + * @param { object } obj - line strip object + * @param { object } subscene + * @param { array } context + */ + rglwidgetClass.prototype.drawLinestrip = function(obj, subscene, context) { + var origIndices, i, j, margin = obj.material.margin; + + if (typeof margin !== "undefined") + if (!this.marginVecToDataVec(obj, subscene)) + return []; + + if (this.opaquePass) + return this.drawSimple(obj, subscene, context); + origIndices = context.indices.slice(); + for (i=0; i < origIndices.length; i++) { + j = origIndices[i]; + if (j < obj.centers.length - 1) { + context.indices = [j, j+1]; + this.drawSimple(obj, subscene, context); + } + } + context.indices = origIndices; + return []; + }; + + /** + * Draw a sprites object in a subscene + * @param { object } obj - object to draw + * @param { object } subscene + * @param { object } context + */ + rglwidgetClass.prototype.drawSprites = function(obj, subscene, context) { + var flags = obj.flags, + is_transparent = rglwidgetClass.isSet(flags, rglwidgetClass.f_is_transparent), + sprites3d = rglwidgetClass.isSet(flags, rglwidgetClass.f_sprites_3d), + fixed_size = rglwidgetClass.isSet(flags, rglwidgetClass.f_fixed_size), + rotating = rglwidgetClass.isSet(flags, rglwidgetClass.f_rotating), + i,j, + origMV = new CanvasMatrix4( this.mvMatrix ), + origPRMV = null, + origPR, + pos, radius, userMatrix, + result = [], margin = obj.material.margin; + + if (typeof margin !== "undefined") + if (!this.marginVecToDataVec(obj, subscene)) + return []; + + if (!sprites3d) + return this.drawSimple(obj, subscene, context); + + if (!obj.initialized) + this.initObj(obj); + + if (!obj.vertexCount) + return []; + + is_transparent = is_transparent || obj.someHidden; + + var norigs = obj.vertices.length, + savenorm = new CanvasMatrix4(this.normMatrix), + iOrig, adj, offset; + + userMatrix = obj.userMatrix; + + if (this.opaquePass) { + context = context.slice(); + context.push(obj.id); + } else + norigs = 1; + + if (this.prmvMatrix !== null) + origPRMV = new CanvasMatrix4( this.prmvMatrix ); + + offset = obj.offset; + + if (fixed_size && !rotating) { + origPR = this.prMatrix; + this.prMatrix = new CanvasMatrix4(); + } + + for (iOrig=0; iOrig < norigs; iOrig++) { + if (this.opaquePass) + j = iOrig; + else + j = context.subid; + pos = [].concat(obj.vertices[j]).concat(1.0); + radius = obj.radii.length > 1 ? obj.radii[j][0] : obj.radii[0][0]; + this.mvMatrix = new CanvasMatrix4(userMatrix); + adj = this.getAdj(obj, j, offset); + this.mvMatrix.translate(1 - 2*adj[0], 1 - 2*adj[1], 1 - 2*adj[2]); + this.mvMatrix.scale(radius, radius, radius); + + if (fixed_size) { + var viewport = subscene.par3d.viewport, + winwidth = viewport.width*this.canvas.width, + winheight = viewport.height*this.canvas.height, + scalex = 27/winwidth, scaley = 27/winheight, + scale = Math.sqrt(scalex * scaley); + if (!rotating) { + pos = rglwidgetClass.multVM(pos, origMV); + pos = rglwidgetClass.multVM(pos, origPR); + this.mvMatrix.scale(scalex, scaley, scale); + } else { + scale = 4.0 * scale * subscene.par3d.zoom; + this.mvMatrix.scale(scale, scale, scale); + } + this.mvMatrix.translate(pos[0]/pos[3], pos[1]/pos[3], pos[2]/pos[3]); + if (rotating) + this.mvMatrix.multRight(origMV); + } else { + if (!rotating) { + pos = rglwidgetClass.multVM(pos, origMV); + this.mvMatrix.translate(pos[0]/pos[3], pos[1]/pos[3], pos[2]/pos[3]); + } else { + this.mvMatrix.translate(pos[0]/pos[3], pos[1]/pos[3], pos[2]/pos[3]); + this.mvMatrix.multRight(origMV); + } + } + this.setnormMatrix2(); + this.setprmvMatrix(); + + for (i=0; i < obj.objects.length; i++) + if (this.opaquePass) + result = result.concat(this.drawObjId(obj.objects[i], subscene.id, context.concat(j))); + else + this.drawObjId(obj.objects[i], subscene.id, context); + } + this.normMatrix = savenorm; + this.mvMatrix = origMV; + if (fixed_size && !rotating) + this.prMatrix = origPR; + if (origPRMV !== null) + this.prmvMatrix = origPRMV; + return result; + }; + + /** + * Draw object that might be in margin + * @param { Object } obj - text object to draw + * @param { Object } subscene - subscene holding it + * @param { Object } context - context for drawing + */ + rglwidgetClass.prototype.drawMarginal = function(obj, subscene, context) { + var margin = obj.material.margin; + + if (typeof margin !== "undefined") + if (!this.marginVecToDataVec(obj, subscene)) + return []; + + return this.drawSimple(obj, subscene, context); + }; + + /** + * Draw bounding box and decorations + * @param { Object } obj - bboxdeco to draw + * @param { Object } subscene - subscene holding it + * @param { Object } context - context for drawing + */ + rglwidgetClass.prototype.drawBBox = function(obj, subscene, context) { + var flags = obj.flags, + is_transparent = rglwidgetClass.isSet(flags, rglwidgetClass.f_is_transparent), + scale, bbox, indices, + enabled = {}, drawing, + result = [], idx, center, edges, + saved; + + if (!obj.initialized) + this.initBBox(obj); + + is_transparent = is_transparent || obj.someHidden; + + if (!this.opaquePass && !is_transparent) + return result; + + this.setBbox(obj, subscene); + + saved = this.setBBoxMatrices(obj); + + bbox = obj.bbox; + center = obj.center; + + scale = [bbox[1]-bbox[0], bbox[3]-bbox[2], bbox[5]-bbox[4]]; + + if (!obj.cube.initialized) { + this.initObj(obj.cube); + } + + if (this.opaquePass) { + context = context.slice(); + context.push(obj.id); + } + + drawing = this.opaquePass !== is_transparent; + this.cube.onecolor = obj.cube.onecolor; + this.initShapeFromObj(this.cube, obj.cube); + + if (!this.opaquePass) + indices = context.indices; + + if (this.opaquePass) + idx = 0; + else + idx = context.subid; + if (typeof idx === "undefined") + console.error("idx is undefined"); + + if (drawing) { + this.drawSimple(this.cube, subscene, context); + } else + result = result.concat(this.getCubePieces(context, obj)); + + if (!obj.ticks.initialized) { + obj.ticks.locations = this.getTickLocations(obj); + obj.ticks.edges = undefined; + } + edges = this.getTickEdges(this.prmvMatrix); + if (obj.needsAxisCallback) + this.doAxisCallback(obj, edges); + if (!obj.ticks.edges || edges.toString() !== obj.ticks.edges.toString()) { + obj.ticks.edges = edges; + this.getTickVertices(obj.ticks); + this.placeTickLabels(obj); + this.setTickLabels(obj); + } + if (!obj.ticks.initialized) { + this.initObj(obj.ticks); + this.initObj(obj.labels); + } + + if (drawing) { + this.drawSimple(obj.ticks, subscene, context); + this.drawSimple(obj.labels, subscene, context); + + this.disableArrays(obj, enabled); + } else { + result = result.concat(this.drawSimple(obj.ticks, subscene, context)); + result = result.concat(this.drawSimple(obj.labels, subscene, context)); + } + + this.restoreBBoxMatrices(saved); + + return result; + }; + + /** + * Use ids to choose object to draw + * @param { numeric } id - object to draw + * @param { numeric } subscene + * @param { array } context + */ + rglwidgetClass.prototype.drawObjId = function(id, subsceneid, context) { + if (typeof id !== "number") + this.alertOnce("drawObjId id is "+typeof id); + + return this.drawObj(this.getObj(id), this.getObj(subsceneid), context); + }; + + /** + * Draw an object in a subscene + * @param { object } obj - object to draw + * @param { object } subscene + * @param { array } context + */ + rglwidgetClass.prototype.drawObj = function(obj, subscene, context) { + switch(obj.type) { + case "abclines": + case "surface": + return this.drawSimple(obj, subscene, context); + case "points": + case "lines": + case "triangles": + case "quads": + case "text": + return this.drawMarginal(obj, subscene, context); + case "linestrip": + return this.drawLinestrip(obj, subscene, context); + case "planes": + return this.drawPlanes(obj, subscene, context); + case "spheres": + return this.drawSpheres(obj, subscene, context); + case "clipplanes": + return this.drawClipplanes(obj); + case "sprites": + return this.drawSprites(obj, subscene, context); + case "light": + return []; + case "bboxdeco": + return this.drawBBox(obj, subscene, context); + } + + console.error("drawObj for type = "+obj.type); + }; + + /** + * Draw the background for a subscene + * @param { number } id - id of background object + * @param { number } subsceneid - id of subscene + */ + rglwidgetClass.prototype.drawBackground = function(id, subsceneid, context) { + var gl = this.gl || this.initGL(), + obj = this.getObj(id), + subscene, + bg, i, savepr, saveinvpr, savemv, savenorm, m, bbox, result = [], + savedm = gl.getParameter(gl.DEPTH_WRITEMASK), + savedt = gl.isEnabled(gl.DEPTH_TEST), + saveblend = gl.isEnabled(gl.BLEND); + + if (!obj.initialized) + this.initObj(obj); + + if (obj.colors.length) { + bg = obj.colors[0]; + gl.depthMask(true); + gl.clear(gl.DEPTH_BUFFER_BIT); + gl.clearColor(bg[0], bg[1], bg[2], bg[3]); + gl.clear(gl.COLOR_BUFFER_BIT); + this.fogColor = bg; + } else { + this.fogColor = [0,0,0,0]; + obj.colors = [[0,0,0,0]]; + } + + this.fogType = obj.fogtype; + this.fogScale = obj.fogscale; + gl.disable(gl.BLEND); + gl.disable(gl.DEPTH_TEST); + gl.depthMask(false); + if (typeof obj.quad !== "undefined") { + savepr = this.prMatrix; + saveinvpr = this.invPrMatrix; + savemv = this.mvMatrix; + this.prMatrix = new CanvasMatrix4(); + this.invPrMatrix = new CanvasMatrix4(); + this.mvMatrix = new CanvasMatrix4(); + for (i=0; i < obj.quad.length; i++) + result = result.concat(this.drawObjId(obj.quad[i], subsceneid)); + this.prMatrix = savepr; + this.invPrMatrix = saveinvpr; + this.mvMatrix = savemv; + + } else if (obj.sphere) { + subscene = this.getObj(subsceneid); + savemv = this.mvMatrix; + savenorm = this.normMatrix; + bbox = subscene.par3d.bbox; + var center = [(bbox[0] + bbox[1])/2, + (bbox[2] + bbox[3])/2, + (bbox[4] + bbox[5])/2, 1], + scale = subscene.par3d.scale, + ranges = [bbox[1] - bbox[0], + bbox[3] - bbox[2], + bbox[5] - bbox[4]], + avgscale = rglwidgetClass.vlen(ranges)/Math.sqrt(3), + aspect = [ranges[0]*scale[0]/avgscale, + ranges[1]*scale[1]/avgscale, + ranges[2]*scale[2]/avgscale], + maxaspect = Math.max(aspect[0], aspect[1], aspect[2]), + zoom = subscene.par3d.zoom; + m = new CanvasMatrix4(); + m.rotate(90, 1, 0, 0); + m.scale(zoom*2.0*maxaspect*ranges[0]/aspect[0], + zoom*2.0*maxaspect*ranges[1]/aspect[1], + zoom*2.0*maxaspect*ranges[2]/aspect[2]); + m.translate(center[0], center[1], center[2]); + m.multRight(savemv); + center = rglwidgetClass.multVM(center, savemv); + m.translate(-center[0], -center[1], -center[2]); + m.scale(1, 1, 0.25/zoom); + m.translate(center[0], center[1], center[2]); + this.mvMatrix = m; + this.initShapeFromObj(this.sphere, obj); + this.sphere.onecolor = obj.colors.length > 1 ? obj.colors[1] : obj.colors[0]; + + this.normMatrix = new CanvasMatrix4(); + + this.setnormMatrix2(); + this.setprmvMatrix(); + + result = result.concat(this.drawSimple(this.sphere, subscene, context)); + this.mvMatrix = savemv; + this.normMatrix = savenorm; + } + gl.depthMask(savedm); + if (savedt) + gl.enable(gl.DEPTH_TEST); + if (saveblend) + gl.enable(gl.BLEND); + return result; + }; + + /** + * Draw a subscene + * @param { number } subsceneid - id of subscene + * @param { array } context + */ + rglwidgetClass.prototype.drawSubscene = function(subsceneid, context) { + var sub = this.getObj(subsceneid), + objects = this.scene.objects, + clipids = sub.clipplanes, + subids = sub.objects, + subscene_has_faces = false, + subscene_needs_sorting = false, + flags, i, obj, result = []; + + if (sub.par3d.skipRedraw) + return result; + + if (this.opaquePass) { + for (i=0; i < subids.length; i++) { + obj = objects[subids[i]]; + flags = obj.flags; + if (typeof flags !== "undefined") { + subscene_has_faces = subscene_has_faces || + (rglwidgetClass.isSet(flags, rglwidgetClass.f_is_lit) && + !rglwidgetClass.isSet(flags, rglwidgetClass.f_fixed_quads)); + obj.is_transparent = obj.someHidden || + rglwidgetClass.isSet(flags, rglwidgetClass.f_is_transparent); + subscene_needs_sorting = subscene_needs_sorting || + obj.is_transparent || + rglwidgetClass.isSet(flags, rglwidgetClass.f_depth_sort); + } + } + } + + this.setViewport(subsceneid); + + this.setprMatrix(subsceneid); + this.setInvPrMatrix(); + this.setmvMatrix(subsceneid); + this.setnormMatrix2(); + this.setprmvMatrix(); + this.invMatrix = new CanvasMatrix4(this.mvMatrix); + this.invMatrix.invert(); + + if (this.opaquePass) { + context = context.slice(); + context.push(subsceneid); + + this.doBlending(false); + this.subsceneid = subsceneid; + if (typeof this.sphere !== "undefined") // reset this.sphere.fastpieces; it will be recreated if needed + this.sphere.fastpieces = undefined; + if (typeof sub.backgroundId !== "undefined") + result = result.concat(this.drawBackground(sub.backgroundId, subsceneid, context)); + } + + if (subids.length) { + + if (clipids.length > 0) { + for (i = 0; i < clipids.length; i++) + this.drawObjId(clipids[i], subsceneid); + } + + subids = sub.opaque.concat(sub.transparent); + if (this.opaquePass) { + for (i = 0; i < subids.length; i++) + result = result.concat(this.drawObjId(subids[i], subsceneid, context)); + subids = sub.subscenes; + for (i = 0; i < subids.length; i++) + result = result.concat(this.drawSubscene(subids[i], context)); + } + } + return result; + }; + + /** + * Set the context for drawing transparently + * @param { array } context + */ + rglwidgetClass.prototype.setContext = function(context) { + var result = [], objid, obj, type; + context = context.slice(); + context.reverse(); + while (context.length > 0) { + objid = context.pop(); + obj = this.getObj(objid); + type = obj.type; + switch (type) { + case "subscene": + this.drawSubscene(objid, false); + break; + case "sprites": + result = result.concat(context.pop()); + break; + case "spheres": + // this.initSphereFromObj(obj); // FIXME: not needed? + break; + case "bboxdeco": + result = result.concat(context.pop()); + break; + default: + console.error("bad type '", type, "' in setContext"); + } + } + return result; + }; + + /** + * Draw the transparent pieces of a scene + * @param {object} pieces + */ + rglwidgetClass.prototype.drawPieces = function(pieces) { + var i, prevcontext = [], context; + for (i = 0; i < pieces.length; i++) { + context = pieces[i].context.slice(); + if (context !== prevcontext) { + prevcontext = context.slice(); + context = this.setContext(context); + this.doBlending(true, pieces[i].objid); + } + this.drawObjId(pieces[i].objid, this.subsceneid, + pieces[i]); + } + }; + + /** + * Draw the whole scene + */ + rglwidgetClass.prototype.drawScene = function() { + var wasDrawing = this.startDrawing(), + pieces; + if (!wasDrawing) { + if (this.select.state !== "inactive") + this.selectionChanged(); + + this.doStartScene(); + this.opaquePass = true; + pieces = this.drawSubscene(this.scene.rootSubscene, []); + this.opaquePass = false; + pieces = this.sortPieces(pieces); + pieces = this.mergePieces(pieces); + this.drawPieces(pieces); + } + this.stopDrawing(wasDrawing); + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/init.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/init.src.js new file mode 100644 index 00000000..4266ca5a --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/init.src.js @@ -0,0 +1,1316 @@ + /** + * Methods related to initialization + * @name ___METHODS_FOR_INITIALIZATION___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + /** + * Initial test for WebGL + */ + rglwidgetClass.prototype.initGL0 = function() { + if (!window.WebGLRenderingContext){ + this.alertOnce("Your browser does not support WebGL. See http://get.webgl.org"); + return; + } + }; + + /** + * Initialize WebGL + * @returns { Object } the WebGL context + */ + rglwidgetClass.prototype.initGL = function() { + var self = this, success = false; + if (this.gl) { + if (!this.drawing && this.gl.isContextLost()) + this.restartCanvas(); + else + return this.gl; + } + // if (!this.isInBrowserViewport()) return; Return what??? At this point we know this.gl is null. + this.canvas.addEventListener("webglcontextrestored", + this.onContextRestored, false); + this.canvas.addEventListener("webglcontextlost", + this.onContextLost, false); + this.gl = this.canvas.getContext("webgl", this.webGLoptions) || + this.canvas.getContext("experimental-webgl", this.webGLoptions); + success = !!(this.gl && this.gl instanceof WebGLRenderingContext); + if (!success) + this.alertOnce("Your browser does not support WebGL. See http://get.webgl.org"); + this.index_uint = this.gl.getExtension("OES_element_index_uint"); + var save = this.startDrawing(); + Object.keys(this.scene.objects).forEach(function(key){ + self.initObjId(parseInt(key, 10)); + }); + this.stopDrawing(save); + return this.gl; + }; + + /** + * Resize the display to match element + * @param { Object } el - DOM element to match + */ + rglwidgetClass.prototype.resize = function(el) { + this.canvas.width = el.width; + this.canvas.height = el.height; + }; + + /** + * Initialize the sphere object + */ + rglwidgetClass.prototype.initSphere = function(sections, segments) { + var v = [], phi = [], theta = [], it = [], centers = [], + i, j, k, ind, result = {}; + + for (i = 0; i <= sections; i++) { + phi.push(i/sections - 0.5); + } + + for (j = 0; j <= segments; j++) { + theta.push(2*j/segments); + for (i = 0; i <= sections; i++) { + /* These are [x,y,z,s,t]: */ + v.push([Math.sin(Math.PI*theta[j]) * Math.cos(Math.PI*phi[i]), + Math.sin(Math.PI*phi[i]), + Math.cos(Math.PI*theta[j]) * Math.cos(Math.PI*phi[i]), + theta[j]/2, + phi[i] + 0.5]); + // console.log("xyzst="+v[v.length-1]); + } + } + result.values = new Float32Array(rglwidgetClass.flatten(v)); + result.vertexCount = v.length; + + for (j = 0; j < segments; j++) { + for (i = 0; i < sections; i++) { + ind = i + (sections + 1)*j; + if (i > 0) // Not south pole + it.push([ind, + ind + sections + 1, + ind + 1]); + if (i < sections - 1) // Not north pole + it.push([ind + sections + 1, + ind + sections + 2, + ind + 1]); + } + } + result.it = new Uint16Array(rglwidgetClass.flatten(it)); + + for (i = 0; i < it.length; i++) { + centers.push([0,0,0]); + for (j = 0; j < 3; j++) { // x,y,z + for (k = 0; k < 3; k++) {// vertices + centers[i][j] += v[it[i][k]][j]/3; + } + } + } + result.centers = centers; + + result.vOffsets = {vofs:0, cofs:-1, nofs:0, radofs:-1, oofs:-1, + tofs:3, nextofs:-1, pointofs:-1, stride:5}; + + result.f = []; + result.indices = {}; + + result.colorCount = 1; + result.type = "sphere"; + this.sphere = result; + this.initShapeGL(this.sphere); + }; + + /** + * Initialize the cube object + */ + rglwidgetClass.prototype.initCube = function() { + var v = [[0, 0, 0], [1, 0, 0], + [0, 1, 0], [1, 1, 0], + [0, 0, 1], [1, 0, 1], + [0, 1, 1], [1, 1, 1]], + ib = [[0, 2, 3, 1], + [2, 6, 7, 3], + [1, 3, 7, 5], + [0, 4, 6, 2], + [0, 1, 5, 4], + [4, 5, 7, 6]], + centers = [], i, j, k, + i0, i1, i2, + normal, result = {}; + + for (i = 0; i < ib.length; i++) { + centers.push([0,0,0]); + for (j = 0; j < 3; j++) { // x,y,z + for (k = 0; k < 4; k++) {// vertices + centers[i][j] += v[ib[i][k]][j]/4; + } + } + } + result.centers = centers; + result.values = new Float32Array(6*4*3*2); + result.vertexCount = 24; + result.vertices = new Array(24); + result.normals = new Array(24); + for (i=0; i < 6; i++) { + for (j=0; j < 4; j++) { + i0 = ib[i][j]; + result.vertices[4*i + j] = v[i0]; + i1 = ib[i][(j + 1) % 4]; + i2 = ib[i][(j + 2) % 4]; + if (j === 0) + normal = rglwidgetClass.normalize(rglwidgetClass.xprod(rglwidgetClass.vdiff(v[i1], v[i0]), + rglwidgetClass.vdiff(v[i2], v[i0]))); + result.normals[4*i + j] = normal; + for (k=0; k < 3; k++) { + result.values[i*24 + j*6 + k] = v[i0][k]; + result.values[i*24 + j*6 + 3 + k] = normal[k]; + } + } + for (j=0; j<4; j++) + ib[i][j] = 4*i + j; + } + result.ib = new Uint16Array(rglwidgetClass.flatten(ib)); + + result.vOffsets = {vofs:0, cofs:-1, nofs:3, radofs:-1, oofs:-1, + tofs:-1, nextofs:-1, pointofs:-1, stride:6}; + + result.f = []; + result.indices = {}; + + result.colorCount = 1; + result.type = "quads"; + this.cube = result; + this.initShapeGL(this.cube); + }; + + + /** + * Do the gl part of initializing the sphere and cube + */ + rglwidgetClass.prototype.initShapeGL = function(shape) { + var gl = this.gl || this.initGL(); + if (gl.isContextLost()) return; + shape.buf = gl.createBuffer(); + gl.bindBuffer(gl.ARRAY_BUFFER, shape.buf); + gl.bufferData(gl.ARRAY_BUFFER, shape.values, gl.STATIC_DRAW); + shape.ibuf = [gl.createBuffer(), gl.createBuffer()]; + return; + }; + + /* Initialize common sphere object from spheres object + */ + rglwidgetClass.prototype.initShapeFromObj = function(shape, obj) { + var i, pass, f, mode, self = this, + /* This function selects things that would be + the back, ignoring perspective -- this is what + we want for the bounding box decoration. */ + is_back = function(i) { + var normal = [].concat(shape.normals[i]), + pt = shape.vertices[i]; + normal.push(-rglwidgetClass.dotprod(normal, pt)); + normal = rglwidgetClass.multVM(normal, self.normMatrix); + return normal[2] < 0 || (normal[2] === 0 && normal[0] < 0); + }; + shape.ofsLoc = obj.ofsLoc; + shape.texLoc = obj.texLoc; + shape.texture = obj.texture; + shape.sampler = obj.sampler; + shape.uFogMode = obj.uFogMode; + shape.uFogColor = obj.uFogColor; + shape.uFogParms = obj.uFogParms; + shape.userAttribLocations = obj.userAttribLocations; + shape.userUniformLocations = obj.userUniformLocations; + shape.normLoc = obj.normLoc; + shape.invPrMatLoc = obj.invPrMatLoc; + shape.clipLoc = obj.clipLoc; + shape.nextLoc = obj.nextLoc; + shape.pointLoc = obj.pointLoc; + shape.aspectLoc = obj.aspectLoc; + shape.lwdLoc = obj.lwdLoc; + shape.prog = obj.prog; + shape.material = obj.material; + shape.flags = obj.flags; + shape.defFlags = obj.defFlags; + shape.someHidden = obj.someHidden; + shape.fastTransparency = obj.fastTransparency; + shape.nlights = obj.nlights; + shape.emission = obj.emission; + shape.emissionLoc = obj.emissionLoc; + shape.shininess = obj.shininess; + shape.shininessLoc = obj.shininessLoc; + shape.ambient = obj.ambient; + shape.ambientLoc = obj.ambientLoc; + shape.specular = obj.specular; + shape.specularLoc = obj.specularLoc; + shape.diffuse = obj.diffuse; + shape.diffuseLoc = obj.diffuseLoc; + shape.lightDir = obj.lightDir; + shape.lightDirLoc = obj.lightDirLoc; + shape.viewpoint = obj.viewpoint; + shape.viewpointLoc = obj.viewpointLoc; + shape.finite = obj.finite; + shape.finiteLoc = obj.finiteLoc; + shape.prMatLoc = obj.prMatLoc; + shape.mvMatLoc = obj.mvMatLoc; + shape.normMatLoc = obj.normMatLoc; + shape.frontLoc = obj.frontLoc; + shape.index_uint = false; + shape.is_transparent = obj.is_transparent; + shape.ignoreExtent = obj.ignoreExtent; + if (shape.passes !== obj.passes || + JSON.stringify( shape.pmode) !== JSON.stringify(obj.pmode)) { + shape.passes = obj.passes; + shape.pmode = obj.pmode; + for (pass = 0; pass < obj.passes; pass++) { + mode = shape.pmode[pass]; + if (typeof shape.indices[mode] === "undefined") { + f = []; + switch (mode) { + case "culled": break; + case "points": + f.length = shape.vertexCount; + for (i=0; i < f.length; i++) + f[i] = i; + break; + case "lines": + if (typeof shape.it !== "undefined") { + f.length = 2* shape.it.length; + for (i=0; i < shape.it.length/3; i++) { + f[6*i] = shape.it[3*i]; + f[6*i + 1] = shape.it[3*i + 1]; + f[6*i + 2] = shape.it[3*i + 1]; + f[6*i + 3] = shape.it[3*i + 2]; + f[6*i + 4] = shape.it[3*i + 2]; + f[6*i + 5] = shape.it[3*i]; + } + } else { + f.length = 2*shape.ib.length; + for (i=0; i < shape.ib.length/4; i++) { + f[8*i] = shape.ib[4*i]; + f[8*i + 1] = shape.ib[4*i + 1]; + f[8*i + 2] = shape.ib[4*i + 1]; + f[8*i + 3] = shape.ib[4*i + 2]; + f[8*i + 4] = shape.ib[4*i + 2]; + f[8*i + 5] = shape.ib[4*i + 3]; + f[8*i + 6] = shape.ib[4*i + 3]; + f[8*i + 7] = shape.ib[4*i]; + } + } + break; + case "filled": + if (typeof shape.it !== "undefined") + f = shape.it; + else if (typeof shape.ib !== "undefined") { + f.length = 1.5*shape.ib.length; + for (i=0; i < shape.ib.length/4; i++) { + f[6*i] = shape.ib[4*i]; + f[6*i+1] = shape.ib[4*i + 1]; + f[6*i+2] = shape.ib[4*i + 2]; + f[6*i+3] = shape.ib[4*i]; + f[6*i+4] = shape.ib[4*i + 2]; + f[6*i+5] = shape.ib[4*i + 3]; + } + } + break; + } + shape.indices[mode] = new Uint16Array(f); + } + } + } + for (pass = 0; pass < obj.passes; pass++) { + mode = shape.pmode[pass]; + shape.f[pass] = shape.indices[mode]; + if (typeof obj.draw_front !== "undefined" && + !obj.draw_front) { + shape.f[pass] = shape.f[pass].filter(is_back); + } + } + // console.log("Names in shapes not in shape:"+JSON.stringify(rglwidgetClass.keydiff(obj, shape))); + shape.initialized = true; + }; + + /** + * Initialize a subscene + * @param { number } id - id of subscene. + */ + rglwidgetClass.prototype.initSubscene = function(id) { + var sub = this.getObj(id), + i, obj; + + if (sub.type !== "subscene") + return; + + sub.par3d.userMatrix = this.toCanvasMatrix4(sub.par3d.userMatrix); + sub.par3d.userProjection = this.toCanvasMatrix4(sub.par3d.userProjection); + sub.par3d.userProjection.transpose(); + sub.par3d.listeners = [].concat(sub.par3d.listeners); + sub.backgroundId = undefined; + sub.subscenes = []; + sub.clipplanes = []; + sub.transparent = []; + sub.opaque = []; + sub.lights = []; + sub.needsBegin = true; + if (typeof sub.objects !== "undefined") + sub.objects = [].concat(sub.objects); /* make sure it's an array */ + for (i=0; i < sub.objects.length; i++) { + obj = this.getObj(sub.objects[i]); + if (typeof obj === "undefined") { + sub.objects.splice(i, 1); + i--; + } else if (obj.type === "background") + sub.backgroundId = obj.id; + else + sub[this.whichList(obj.id)].push(obj.id); + } + }; + + rglwidgetClass.prototype.initBBox = function(obj) { + if (!this.cube) + this.initCube(); + obj.cube = {id: obj.id + 0.1, + type: "quads", + flags: obj.flags, + material: obj.material, + colors: [obj.colors[0]], + vertices: this.cube.vertices, + normals: this.cube.normals, + draw_front: obj.draw_front, + initialized: false + }; + if (this.getMaterial(obj.cube, "front") !== + this.getMaterial(obj.cube, "back")) + /* jshint bitwise: false */ + obj.cube.flags |= rglwidgetClass.f_is_twosided; + /* jshint bitwise: true */ + this.scene.objects[obj.cube.id] = obj.cube; + obj.ticks = {id: obj.id + 0.2, + type: "lines", + flags: rglwidgetClass.f_has_fog, + material: obj.material, + colors: (obj.colors.length > 1 ? [obj.colors[1]] : [obj.colors[0]]), + axes: obj.axes, + initialized: false + }; + this.scene.objects[obj.ticks.id] = obj.ticks; + obj.labels = {id: obj.id + 0.3, + type: "text", + flags: rglwidgetClass.f_has_fog + + rglwidgetClass.f_fixed_size + + rglwidgetClass.f_fixed_quads, + material: {lit: false}, + colors: (obj.colors.length > 1 ? [obj.colors[1]] : [obj.colors[0]]), + cex: [[1]], + family: [["sans"]], + font: [[1]], + adj: [[0.5, 0.5, 0.5]], + ignoreExtent: true, + initialized: false + }; + this.scene.objects[obj.labels.id] = obj.labels; + obj.initialized = true; + }; + + rglwidgetClass.prototype.initBackground = function(obj) { + var material, fl = obj.defFlags; + if (typeof obj.ids !== "undefined") + obj.quad = rglwidgetClass.flatten([].concat(obj.ids)); + else if (obj.sphere) { + fl.has_normals = true; + fl.needs_vnormal = true; + obj.defFlags = fl; + material = obj.material; + material.front = "culled"; + obj.vertices = [[0,0,0]]; + obj.texcoords = [[0,0]]; + } + }; + + /** + * Initialize object for display + * @param { number } id - id of object to initialize + */ + rglwidgetClass.prototype.initObjId = function(id) { + if (typeof id !== "number") { + this.alertOnce("initObj id is "+typeof id); + } + return this.initObj(this.getObj(id)); + }; + + /** + * Initialize object for display + * @param { Object } obj - object to initialize + */ + rglwidgetClass.prototype.initObj = function(obj) { + var type = obj.type, + flags = obj.flags, + normals = obj.normals, + round_points = (typeof obj.material === "undefined") ? + false : this.getMaterial(obj, "point_antialias"), + has_indices = typeof obj.indices !== "undefined", + has_spheres = type === "spheres" || + (type === "background" && obj.sphere), + sprites_3d = rglwidgetClass.isSet(flags, rglwidgetClass.f_sprites_3d), + depth_sort = rglwidgetClass.isSet(flags, rglwidgetClass.f_depth_sort), + gl = this.gl || this.initGL(), + fl, polygon_offset, + texinfo, drawtype, nclipplanes, f, nrows, oldrows, + i,j,v,v1,v2, mat, uri, matobj, pass, pmode, + dim, nx, nz, nrow, shaders; + + obj.initialized = true; + + obj.someHidden = false; // used in selection + + this.expandBufferedFields(obj); + + if (type === "subscene") + return; + + obj.defFlags = fl = rglwidgetClass.getDefFlags(flags, type, normals, round_points); + + obj.is_transparent = fl.is_transparent; + + if (type === "bboxdeco") + return this.initBBox(obj); + + if (has_spheres && typeof this.sphere === "undefined") + this.initSphere(16, 16); + + if (type === "light") { + obj.ambient = new Float32Array(obj.colors[0].slice(0,3)); + obj.diffuse = new Float32Array(obj.colors[1].slice(0,3)); + obj.specular = new Float32Array(obj.colors[2].slice(0,3)); + obj.lightDir = new Float32Array(obj.vertices[0]); + return; + } + + if (type === "clipplanes") { + obj.vClipplane = rglwidgetClass.flatten(rglwidgetClass.cbind(obj.normals, obj.offsets)); + return; + } + + if (type === "background") { + this.initBackground(obj); + if (!obj.sphere) + return; + } + + polygon_offset = this.getMaterial(obj, "polygon_offset"); + if (polygon_offset[0] !== 0 || polygon_offset[1] !== 0) + obj.polygon_offset = polygon_offset; + + if (fl.is_transparent) { + depth_sort = ["triangles", "quads", "surface", + "spheres", "sprites", "text", + "planes"].indexOf(type) >= 0; + } + + if (fl.is_brush) + this.initSelection(obj.id); + + if (typeof obj.vertices === "undefined") + obj.vertices = []; + + v = obj.vertices; + if (has_indices) + obj.vertexCount = obj.indices.length; + else + obj.vertexCount = v.length; + + if (!obj.vertexCount) return; + + if (fl.is_twosided && !fl.has_normals && type !== "background") { + if (typeof obj.userAttributes === "undefined") + obj.userAttributes = {}; + v1 = Array(v.length); + v2 = Array(v.length); + if (obj.type === "triangles" || obj.type === "quads") { + if (obj.type === "triangles") + nrow = 3; + else + nrow = 4; + for (i=0; i= 0) { + key = this.scene.crosstalk.key[j]; + options = this.scene.crosstalk.options[j]; + colors = colors.slice(0); + for (i = 0; i < v.length; i++) + colors[i] = obj.colors[i % obj.colors.length].slice(0); + if ( (selection = this.scene.crosstalk.selection) && + (selection.length || !options.selectedIgnoreNone) ) + for (i = 0; i < v.length; i++) { + if (!selection.includes(key[i])) { + if (options.deselectedColor) + colors[i] = options.deselectedColor.slice(0); + colors[i][3] = colors[i][3]*options.deselectedFade; /* default: mostly transparent if not selected */ + } else if (options.selectedColor) + colors[i] = options.selectedColor.slice(0); + } + if ( (filter = this.scene.crosstalk.filter) ) + for (i = 0; i < v.length; i++) + if (!filter.includes(key[i])) { + if (options.filteredColor) + colors[i] = options.filteredColor.slice(0); + colors[i][3] = colors[i][3]*options.filteredFade; /* default: completely hidden if filtered */ + } + } + + nc = obj.colorCount = colors.length; + if (nc > 1) { + cofs = stride; + stride = stride + 4; + v = rglwidgetClass.cbind(v, colors); + } else { + cofs = -1; + obj.onecolor = rglwidgetClass.flatten(colors); + } + + if (fl.has_normals && !has_spheres) { + nofs = stride; + stride = stride + 3; + v = rglwidgetClass.cbind(v, typeof obj.pnormals !== "undefined" ? obj.pnormals : obj.normals); + } else + nofs = -1; + + if (typeof obj.radii !== "undefined") { + radofs = stride; + stride = stride + 1; + // FIXME: always concat the radii? + if (obj.radii.length === v.length) { + v = rglwidgetClass.cbind(v, obj.radii); + } else if (obj.radii.length === 1) { + v = v.map(function(row) { return row.concat(obj.radii[0]);}); + } + } else + radofs = -1; + + // Add default indices + if (has_indices) { + f = Array(obj.indices.length); + for (i = 0; i < f.length; i++) + f[i] = obj.indices[i] - 1; + } else { + f = Array(v.length); + for (i = 0; i < v.length; i++) + f[i] = i; + } + obj.f = [f,f]; + + if (type === "sprites" && !sprites_3d) { + tofs = stride; + stride += 2; + oofs = stride; + stride += 3; + vnew = new Array(4*v.length); + fnew = new Array(4*v.length); + alias = new Array(v.length); + var rescale = fl.fixed_size ? 72 : 1, + size = obj.radii, s = rescale*size[0]/2; + last = v.length; + f = obj.f[0]; + obj.adj = rglwidgetClass.flatten(obj.adj); + if (typeof obj.pos !== "undefined") { + obj.pos = rglwidgetClass.flatten(obj.pos); + offset = obj.adj[0]; + } else + offset = 0; + for (i=0; i < v.length; i++) { + adj = this.getAdj(obj, i, offset); + if (size.length > 1) + s = rescale*size[i]/2; + adj[0] = 2*s*(adj[0] - 0.5); + adj[1] = 2*s*(adj[1] - 0.5); + adj[2] = 2*s*(adj[2] - 0.5); + vnew[i] = v[i].concat([0,0]).concat([-s-adj[0], + -s-adj[1], + -adj[2]]); + fnew[4*i] = f[i]; + vnew[last]= v[i].concat([1,0]).concat([s-adj[0], + -s-adj[1], + -adj[2]]); + fnew[4*i+1] = last++; + vnew[last]= v[i].concat([1,1]).concat([s-adj[0], + s-adj[1], + -adj[2]]); + fnew[4*i+2] = last++; + vnew[last]= v[i].concat([0,1]).concat([-s-adj[0], + s-adj[1], + -adj[2]]); + fnew[4*i+3] = last++; + alias[i] = [last-3, last-2, last-1]; + } + v = vnew; + obj.vertexCount = v.length; + obj.f = [fnew, fnew]; + } else if (type === "text") { + tofs = stride; + stride += 2; + oofs = stride; + stride += 3; + vnew = new Array(4*v.length); + f = obj.f[0]; + fnew = new Array(4*f.length); + alias = new Array(v.length); + last = v.length; + adj = rglwidgetClass.flatten(obj.adj); + if (typeof obj.pos !== "undefined") { + obj.pos = rglwidgetClass.flatten(obj.pos); + offset = adj[0]; + } else + offset = 0; + for (i=0; i < v.length; i++) { + adj = this.getAdj(obj, i, offset, obj.texts[i]); + vnew[i] = v[i].concat([0,-0.5]).concat(adj); + fnew[4*i] = f[i]; + vnew[last] = v[i].concat([1,-0.5]).concat(adj); + fnew[4*i+1] = last++; + vnew[last] = v[i].concat([1, 1.5]).concat(adj); + fnew[4*i+2] = last++; + vnew[last] = v[i].concat([0, 1.5]).concat(adj); + fnew[4*i+3] = last++; + alias[i] = [last-3, last-2, last-1]; + for (j=0; j < 4; j++) { + v1 = vnew[fnew[4*i+j]]; + v1[oofs] = 2*(v1[tofs]-v1[oofs])*texinfo.widths[i]; + v1[oofs+1] = 2*(v1[tofs+1]-v1[oofs+1])*texinfo.textHeights[i]; + v1[oofs+2] = 2*(0.5-v1[oofs+2])*texinfo.textHeights[i]/1000.0; + v1[tofs] = (texinfo.offsetsx[i] + v1[tofs]*texinfo.widths[i])/texinfo.canvasX; + v1[tofs+1] = 1.0-(texinfo.offsetsy[i] - + v1[tofs+1]*texinfo.textHeights[i])/texinfo.canvasY; + vnew[fnew[4*i+j]] = v1; + } + } + v = vnew; + obj.vertexCount = v.length; + obj.f = [fnew, fnew]; + } else if (typeof obj.texcoords !== "undefined") { + tofs = stride; + stride += 2; + oofs = -1; + v = rglwidgetClass.cbind(v, obj.texcoords); + } else { + tofs = -1; + oofs = -1; + } + + obj.alias = alias; + + if (typeof obj.userAttributes !== "undefined") { + obj.userAttribOffsets = {}; + obj.userAttribLocations = {}; + obj.userAttribSizes = {}; + for (attr in obj.userAttributes) { + obj.userAttribLocations[attr] = gl.getAttribLocation(obj.prog, attr); + if (obj.userAttribLocations[attr] >= 0) { // Attribute may not have been used + obj.userAttribOffsets[attr] = stride; + v = rglwidgetClass.cbind(v, obj.userAttributes[attr]); + stride = v[0].length; + obj.userAttribSizes[attr] = stride - obj.userAttribOffsets[attr]; + } else + console.warn("attribute '"+attr+"' not found in object "+obj.id+"."); + } + } + + if (typeof obj.userUniforms !== "undefined" || + typeof obj.userTextures !== "undefined") { + obj.userUniformLocations = {}; + for (attr in obj.userUniforms) { + obj.userUniformLocations[attr] = gl.getUniformLocation(obj.prog, attr); + if (obj.userUniformLocations[attr] === null) + console.warn("uniform '"+attr+"' not found in object "+obj.id+"."); + } + for (attr in obj.userTextures) { + var texture = obj.userTextures[attr]; + texture.texture = gl.createTexture(); + // This is a trick from https://stackoverflow.com/a/19748905/2554330 to avoid warnings + gl.bindTexture(gl.TEXTURE_2D, texture.texture); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, 1, 1, 0, gl.RGBA, gl.UNSIGNED_BYTE, + new Uint8Array([255,255,255, 255])); // white + texture.sampler = gl.getUniformLocation(obj.prog, attr); + if (texture.sampler === null) + console.warn("sampler '"+attr+"' not found in object "+obj.id+"."); + uri = texture.uri; + this.loadImageToTexture(uri, texture.texture); + } + } + + if (sprites_3d) { + obj.userMatrix = new CanvasMatrix4(); + obj.userMatrix.load(rglwidgetClass.flatten(obj.usermatrix)); + obj.objects = rglwidgetClass.flatten([].concat(obj.ids)); + fl.is_lit = false; + obj.adj = rglwidgetClass.flatten(obj.adj); + if (typeof obj.pos !== "undefined") { + obj.pos = rglwidgetClass.flatten(obj.pos); + obj.offset = obj.adj[0]; + } else + obj.offset = 0; + + for (i=0; i < obj.objects.length; i++) + this.initObjId(obj.objects[i]); + } + + nclipplanes = this.countClipplanes(); + if (nclipplanes && !sprites_3d) { + obj.clipLoc = gl.getUniformLocation(obj.prog,"vClipplane"); + } + + if (fl.is_lit) { + obj.emissionLoc = gl.getUniformLocation(obj.prog, "emission"); + obj.emission = new Float32Array(this.stringToRgb(this.getMaterial(obj, "emission"))); + obj.shininessLoc = gl.getUniformLocation(obj.prog, "shininess"); + obj.shininess = this.getMaterial(obj, "shininess"); + obj.nlights = this.countLights(); + if (obj.nlights > 0) { + obj.ambient = new Float32Array(this.stringToRgb(this.getMaterial(obj, "ambient"))); + obj.specular = new Float32Array(this.stringToRgb(this.getMaterial(obj, "specular"))); + obj.ambientLoc = gl.getUniformLocation(obj.prog, "ambient"); + obj.specularLoc = gl.getUniformLocation(obj.prog, "specular"); + obj.diffuseLoc = gl.getUniformLocation(obj.prog, "diffuse" ); + obj.lightDirLoc = gl.getUniformLocation(obj.prog, "lightDir"); + obj.viewpointLoc = gl.getUniformLocation(obj.prog, "viewpoint"); + obj.finiteLoc = gl.getUniformLocation(obj.prog, "finite" ); + } + } + + obj.passes = fl.is_twosided + 1; + obj.pmode = new Array(obj.passes); + for (pass = 0; pass < obj.passes; pass++) { + if (type === "triangles" || type === "quads" || type === "surface" || has_spheres) + pmode = this.getMaterial(obj, (pass === 0) ? "front" : "back"); + else pmode = "filled"; + obj.pmode[pass] = pmode; + } + if (!has_spheres) { + obj.f.length = obj.passes; + for (pass = 0; pass < obj.passes; pass++) { + f = fnew = obj.f[pass]; + pmode = obj.pmode[pass]; + if (pmode === "culled") + fnew = []; + else if (pmode === "points") { + // stay with default + } else if ((type === "quads" || type === "text" || + type === "sprites") && !sprites_3d) { + nrows = Math.floor(obj.vertexCount/4); + if (pmode === "filled") { + fnew = Array(6*nrows); + for (i=0; i < nrows; i++) { + fnew[6*i] = f[4*i]; + fnew[6*i+1] = f[4*i + 1]; + fnew[6*i+2] = f[4*i + 2]; + fnew[6*i+3] = f[4*i]; + fnew[6*i+4] = f[4*i + 2]; + fnew[6*i+5] = f[4*i + 3]; + } + } else { + fnew = Array(8*nrows); + for (i=0; i < nrows; i++) { + fnew[8*i] = f[4*i]; + fnew[8*i+1] = f[4*i + 1]; + fnew[8*i+2] = f[4*i + 1]; + fnew[8*i+3] = f[4*i + 2]; + fnew[8*i+4] = f[4*i + 2]; + fnew[8*i+5] = f[4*i + 3]; + fnew[8*i+6] = f[4*i + 3]; + fnew[8*i+7] = f[4*i]; + } + } + } else if (type === "triangles") { + nrows = Math.floor(obj.vertexCount/3); + if (pmode === "filled") { + fnew = Array(3*nrows); + for (i=0; i < fnew.length; i++) { + fnew[i] = f[i]; + } + } else if (pmode === "lines") { + fnew = Array(6*nrows); + for (i=0; i < nrows; i++) { + fnew[6*i] = f[3*i]; + fnew[6*i + 1] = f[3*i + 1]; + fnew[6*i + 2] = f[3*i + 1]; + fnew[6*i + 3] = f[3*i + 2]; + fnew[6*i + 4] = f[3*i + 2]; + fnew[6*i + 5] = f[3*i]; + } + } + } else if (has_spheres) { + // default + } else if (type === "surface") { + dim = obj.dim[0]; + nx = dim[0]; + nz = dim[1]; + if (pmode === "filled") { + fnew = []; + for (j=0; j 65535) { + if (this.index_uint) { + obj.f[pass] = new Uint32Array(obj.f[pass]); + obj.index_uint = true; + } else + this.alertOnce("Object has "+obj.vertexCount+" vertices, not supported in this browser."); + } else { + obj.f[pass] = new Uint16Array(obj.f[pass]); + obj.index_uint = false; + } + } + + if (stride !== v[0].length) { + this.alertOnce("problem in stride calculation"); + } + + obj.vOffsets = {vofs:0, cofs:cofs, nofs:nofs, radofs:radofs, oofs:oofs, tofs:tofs, + nextofs:nextofs, pointofs:pointofs, stride:stride}; + + obj.values = new Float32Array(rglwidgetClass.flatten(v)); + + if (!has_spheres && !sprites_3d) { + obj.buf = gl.createBuffer(); + gl.bindBuffer(gl.ARRAY_BUFFER, obj.buf); + gl.bufferData(gl.ARRAY_BUFFER, obj.values, gl.STATIC_DRAW); // + obj.ibuf = Array(obj.passes); + obj.ibuf[0] = gl.createBuffer(); + gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, obj.ibuf[0]); + gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, obj.f[0], gl[drawtype]); + if (fl.is_twosided) { + obj.ibuf[1] = gl.createBuffer(); + gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, obj.ibuf[1]); + gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, obj.f[1], gl[drawtype]); + } + } + + if (!sprites_3d) { + obj.mvMatLoc = gl.getUniformLocation(obj.prog, "mvMatrix"); + obj.prMatLoc = gl.getUniformLocation(obj.prog, "prMatrix"); + + if (fl.fixed_size) { + obj.textScaleLoc = gl.getUniformLocation(obj.prog, "textScale"); + } + } + + if (fl.needs_vnormal) { + obj.normLoc = gl.getAttribLocation(obj.prog, "aNorm"); + obj.normMatLoc = gl.getUniformLocation(obj.prog, "normMatrix"); + } + + if (fl.is_twosided) { + obj.frontLoc = gl.getUniformLocation(obj.prog, "front"); + if (fl.has_normals) + obj.invPrMatLoc = gl.getUniformLocation(obj.prog, "invPrMatrix"); + } + }; + + /** + * Initialize the DOM object + * @param { Object } el - the DOM object + * @param { Object } x - the scene data sent by JSON from R + */ + rglwidgetClass.prototype.initialize = function(el, x) { + this.textureCanvas = document.createElement("canvas"); + this.textureCanvas.style.display = "block"; + this.scene = x; + this.normMatrix = new CanvasMatrix4(); + this.invPrMatrix = new CanvasMatrix4(); + this.saveMat = {}; + this.distance = null; + this.posLoc = 0; + this.colLoc = 1; + if (el) { + el.rglinstance = this; + this.el = el; + this.webGLoptions = el.rglinstance.scene.webGLoptions; + this.initCanvas(); + } + if (typeof Shiny !== "undefined") { + var self = this; + Shiny.addCustomMessageHandler("shinyGetPar3d", + function(message) { + var i, param, + subscene = self.getObj(message.subscene), + parameters = [].concat(message.parameters), + result = {tag: message.tag, subscene: message.subscene}; + if (typeof subscene !== "undefined") { + for (i = 0; i < parameters.length; i++) { + param = parameters[i]; + result[param] = subscene.par3d[param]; + } + } else { + console.log("subscene "+message.subscene+" undefined."); + } + Shiny.setInputValue("par3d:shinyPar3d", result, {priority: "event"}); + }); + + Shiny.addCustomMessageHandler("shinySetPar3d", + function(message) { + var param = message.parameter, + subscene = self.getObj(message.subscene); + if (typeof subscene !== "undefined") { + subscene.par3d[param] = message.value; + subscene.initialized = false; + self.drawScene(); + } else { + console.log("subscene "+message.subscene+" undefined."); + } + }); + + Shiny.addCustomMessageHandler("resetBrush", + function(message) { + if (message === self.scene.selectionInput) { + self.clearBrush(null); + self.recordSelection(0); + } + }); + } + }; + + /** + * Restart the WebGL canvas + */ + rglwidgetClass.prototype.restartCanvas = function() { + var newcanvas = document.createElement("canvas"), + self = this, + labelid = this.el.getAttribute("aria-labelledby"); + newcanvas.width = this.el.width; + newcanvas.height = this.el.height; + newcanvas.setAttribute("aria-labelledby", + labelid); + + if (typeof this.scene.altText !== "undefined") + // We're in Shiny, so alter the label + document.getElementById(labelid).innerHTML = this.scene.altText; + + newcanvas.addEventListener("webglcontextrestored", + this.onContextRestored, false); + newcanvas.addEventListener("webglcontextlost", + this.onContextLost, false); + while (this.el.firstChild) { + this.el.removeChild(this.el.firstChild); + } + this.el.appendChild(newcanvas); + this.canvas = newcanvas; + if (this.scene.javascript) { + /* jshint evil:true */ + Function('"use strict";' + this.scene.javascript)(); + /* jshint evil:false */ + } + this.setMouseHandlers(); + if (this.gl) + Object.keys(this.scene.objects).forEach(function(key){ + self.getObj(parseInt(key, 10)).texture = undefined; + }); + this.gl = null; + }; + + /** + * Initialize the WebGL canvas + */ + rglwidgetClass.prototype.initCanvas = function() { + this.restartCanvas(); + var objs = this.scene.objects, + self = this; + + /* These hold context specific data. In Shiny, they + need to be deleted. Elsewhere, they don't exist + and these are no-ops. */ + + delete this.cube; + delete this.sphere; + + Object.keys(objs).forEach(function(key){ + self.initSubscene(parseInt(key, 10)); + }); + + this.onContextRestored = function() { + self.initGL(); + self.drawScene(); + }; + + this.onContextLost = function(event) { + if (!self.drawing) + this.gl = null; + event.preventDefault(); + }; + + this.initGL0(); + this.lazyLoadScene = function() { + if (typeof self.slide === "undefined") + self.slide = self.getSlide(); + if (self.isInBrowserViewport()) { + if (!self.gl || self.gl.isContextLost()) + self.initGL(); + self.drawScene(); + } + }; + window.addEventListener("DOMContentLoaded", this.lazyLoadScene, false); + window.addEventListener("load", this.lazyLoadScene, false); + window.addEventListener("resize", this.lazyLoadScene, false); + window.addEventListener("scroll", this.lazyLoadScene, false); + this.slide = this.getSlide(); + if (this.slide) { + if (typeof this.slide.rgl === "undefined") + this.slide.rgl = [this]; + else + this.slide.rgl.push(this); + if (this.scene.context.rmarkdown) + if (this.scene.context.rmarkdown === "ioslides_presentation") { + this.slide.setAttribute("slideenter", "this.rgl.forEach(function(scene) { scene.lazyLoadScene.call(window);})"); + } else if (this.scene.context.rmarkdown === "slidy_presentation") { + // This method would also work in ioslides, but it gets triggered + // something like 5 times per slide for every slide change, so + // you'd need a quicker function than lazyLoadScene. + var MutationObserver = window.MutationObserver || window.WebKitMutationObserver || window.MozMutationObserver, + observer = new MutationObserver(function(mutations) { + mutations.forEach(function() { + self.slide.rgl.forEach(function(scene) { scene.lazyLoadScene.call(window); });});}); + observer.observe(this.slide, { attributes: true, attributeFilter:["class"] }); + } + } + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/mouse.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/mouse.src.js new file mode 100644 index 00000000..ca992f83 --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/mouse.src.js @@ -0,0 +1,569 @@ + /** + * Methods related to mouse handling + * @name ___METHODS_FOR_MOUSE_HANDLING___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + rglwidgetClass.prototype.getCursor = function(mode) { + switch(mode) { + case "none": + return "none"; + case "trackball": + case "xAxis": + case "yAxis": + case "zAxis": + case "polar": + return "grab"; + case "selecting": + return "crosshair"; + case "fov": + case "zoom": + return "zoom-in"; + case "user": + return "default"; + } + return "dragging"; + }; + + /** + * Set mouse mode for a subscene + * @param { string } mode - name of mode + * @param { number } button - button number (0 to 4) + * @param { number } subscene - subscene id number + * @param { number } stayActive - if truthy, don't clear brush + */ + rglwidgetClass.prototype.setMouseMode = function(mode, button, subscene, stayActive) { + var sub = this.getObj(subscene), + which = ["none", "left", "right", "middle", "wheel"][button]; + if (!stayActive && sub.par3d.mouseMode[which] === "selecting") + this.clearBrush(null); + sub.par3d.mouseMode[which] = mode; + if (button === 1 || (button === 0 && mode !== "none")) + this.canvas.style.cursor = this.getCursor(mode); + if (button === 0 && mode !== "none") + sub.needsBegin = mode; + }; + + /** + * Compute mouse coordinates relative to current canvas + * @returns { Object } + * @param { Object } event - event object from mouse click + */ + rglwidgetClass.prototype.relMouseCoords = function(event) { + var rect = this.canvas.getBoundingClientRect(); + return {x:event.clientX-rect.left, y:event.clientY-rect.top}; + }; + + /** + * Send mouse selection to Shiny + */ + rglwidgetClass.prototype.recordSelection = function(subid) { + var result = {}; + if (typeof this.select !== "undefined" && + typeof this.select.state !== "undefined" && + this.select.state !== "inactive") { + result = { subscene: subid, + state: this.select.state, + region: this.select.region + }; + this.setmvMatrix(subid); + result.model = this.mvMatrix; + this.setprMatrix(subid); + result.proj = this.prMatrix; + this.getViewport(subid); + result.view = this.vp; + } else + result.state = "inactive"; + Shiny.setInputValue(this.scene.selectionInput + ":shinyMouse3d", result); + }; + + /** + * Set mouse handlers for the scene + */ + rglwidgetClass.prototype.setMouseHandlers = function() { + var self = this, activeSubscene, handler, + handlers = {}, drag = 0; + + handlers.rotBase = 0; + + self.screenToVector = function(x, y) { + var viewport = self.getObj(activeSubscene).par3d.viewport, + width = viewport.width*self.canvas.width, + height = viewport.height*self.canvas.height, + radius = Math.max(width, height)/2.0, + cx = width/2.0, + cy = height/2.0, + px = (x-cx)/radius, + py = (y-cy)/radius, + plen = Math.sqrt(px*px+py*py); + if (plen > 1.e-6) { + px = px/plen; + py = py/plen; + } + var angle = (Math.SQRT2 - plen)/Math.SQRT2*Math.PI/2, + z = Math.sin(angle), + zlen = Math.sqrt(1.0 - z*z); + px = px * zlen; + py = py * zlen; + return [px, py, z]; + }; + + handlers.trackballdown = function(x,y) { + var activeSub = self.getObj(activeSubscene), + activeModel = self.getObj(self.useid(activeSub.id, "model")), + i, l = activeModel.par3d.listeners; + handlers.rotBase = self.screenToVector(x, y); + self.saveMat = []; + for (i = 0; i < l.length; i++) { + activeSub = self.getObj(l[i]); + activeSub.saveMat = new CanvasMatrix4(activeSub.par3d.userMatrix); + } + self.canvas.style.cursor = "grabbing"; + }; + + handlers.trackballmove = function(x,y) { + var rotCurrent = self.screenToVector(x,y), + rotBase = handlers.rotBase, + dot = rotBase[0]*rotCurrent[0] + + rotBase[1]*rotCurrent[1] + + rotBase[2]*rotCurrent[2], + angle = Math.acos( dot/rglwidgetClass.vlen(rotBase)/rglwidgetClass.vlen(rotCurrent) )*180.0/Math.PI, + axis = rglwidgetClass.xprod(rotBase, rotCurrent), + objects = self.scene.objects, + activeSub = self.getObj(activeSubscene), + activeModel = self.getObj(self.useid(activeSub.id, "model")), + l = activeModel.par3d.listeners, + i; + if (angle === 0.0) + return; + for (i = 0; i < l.length; i++) { + activeSub = self.getObj(l[i]); + activeSub.par3d.userMatrix.load(objects[l[i]].saveMat); + activeSub.par3d.userMatrix.rotate(angle, axis[0], axis[1], axis[2]); + } + self.drawScene(); + }; + handlers.trackballend = 0; + + self.clamp = function(x, lo, hi) { + return Math.max(lo, Math.min(x, hi)); + }; + + self.screenToPolar = function(x,y) { + var viewport = self.getObj(activeSubscene).par3d.viewport, + width = viewport.width*self.canvas.width, + height = viewport.height*self.canvas.height, + r = Math.min(width, height)/2, + dx = self.clamp(x - width/2, -r, r), + dy = self.clamp(y - height/2, -r, r); + return [Math.asin(dx/r), Math.asin(-dy/r)]; + }; + + handlers.polardown = function(x,y) { + var activeSub = self.getObj(activeSubscene), + activeModel = self.getObj(self.useid(activeSub.id, "model")), + i, l = activeModel.par3d.listeners; + handlers.dragBase = self.screenToPolar(x, y); + self.saveMat = []; + for (i = 0; i < l.length; i++) { + activeSub = self.getObj(l[i]); + activeSub.saveMat = new CanvasMatrix4(activeSub.par3d.userMatrix); + activeSub.camBase = [-Math.atan2(activeSub.saveMat.m13, activeSub.saveMat.m11), + Math.atan2(activeSub.saveMat.m32, activeSub.saveMat.m22)]; + } + self.canvas.style.cursor = "grabbing"; + }; + + handlers.polarmove = function(x,y) { + var dragCurrent = self.screenToPolar(x,y), + activeSub = self.getObj(activeSubscene), + activeModel = self.getObj(self.useid(activeSub.id, "model")), + objects = self.scene.objects, + l = activeModel.par3d.listeners, + i, j, changepos = []; + for (i = 0; i < l.length; i++) { + activeSub = self.getObj(l[i]); + for (j=0; j<2; j++) + changepos[j] = -(dragCurrent[j] - handlers.dragBase[j]); + activeSub.par3d.userMatrix.makeIdentity(); + activeSub.par3d.userMatrix.rotate(changepos[0]*180/Math.PI, 0,-1,0); + activeSub.par3d.userMatrix.multRight(objects[l[i]].saveMat); + activeSub.par3d.userMatrix.rotate(changepos[1]*180/Math.PI, -1,0,0); + } + self.drawScene(); + }; + handlers.polarend = 0; + + handlers.axisdown = function(x) { + handlers.rotBase = self.screenToVector(x, self.canvas.height/2); + var activeSub = self.getObj(activeSubscene), + activeModel = self.getObj(self.useid(activeSub.id, "model")), + i, l = activeModel.par3d.listeners; + for (i = 0; i < l.length; i++) { + activeSub = self.getObj(l[i]); + activeSub.saveMat = new CanvasMatrix4(activeSub.par3d.userMatrix); + } + self.canvas.style.cursor = "grabbing"; + }; + + handlers.axismove = function(x) { + var rotCurrent = self.screenToVector(x, self.canvas.height/2), + rotBase = handlers.rotBase, + angle = (rotCurrent[0] - rotBase[0])*180/Math.PI, + rotMat = new CanvasMatrix4(); + rotMat.rotate(angle, handlers.axis[0], handlers.axis[1], handlers.axis[2]); + var activeSub = self.getObj(activeSubscene), + activeModel = self.getObj(self.useid(activeSub.id, "model")), + i, l = activeModel.par3d.listeners; + for (i = 0; i < l.length; i++) { + activeSub = self.getObj(l[i]); + activeSub.par3d.userMatrix.load(activeSub.saveMat); + activeSub.par3d.userMatrix.multLeft(rotMat); + } + self.drawScene(); + }; + handlers.axisend = 0; + + handlers.y0zoom = 0; + handlers.zoomdown = function(x, y) { + var activeSub = self.getObj(activeSubscene), + activeProjection = self.getObj(self.useid(activeSub.id, "projection")), + i, l = activeProjection.par3d.listeners; + handlers.y0zoom = y; + for (i = 0; i < l.length; i++) { + activeSub = self.getObj(l[i]); + activeSub.zoom0 = Math.log(activeSub.par3d.zoom); + } + self.canvas.style.cursor = "zoom-in"; + }; + handlers.zoommove = function(x, y) { + var activeSub = self.getObj(activeSubscene), + activeProjection = self.getObj(self.useid(activeSub.id, "projection")), + i, l = activeProjection.par3d.listeners; + for (i = 0; i < l.length; i++) { + activeSub = self.getObj(l[i]); + activeSub.par3d.zoom = Math.exp(activeSub.zoom0 + (y-handlers.y0zoom)/self.canvas.height); + } + self.drawScene(); + }; + handlers.zoomend = 0; + + handlers.y0fov = 0; + handlers.fovdown = function(x, y) { + handlers.y0fov = y; + var activeSub = self.getObj(activeSubscene), + activeProjection = self.getObj(self.useid(activeSub.id, "projection")), + i, l = activeProjection.par3d.listeners; + for (i = 0; i < l.length; i++) { + activeSub = self.getObj(l[i]); + activeSub.fov0 = activeSub.par3d.FOV; + } + self.canvas.style.cursor = "zoom-in"; + }; + handlers.fovmove = function(x, y) { + var activeSub = self.getObj(activeSubscene), + activeProjection = self.getObj(self.useid(activeSub.id, "projection")), + i, l = activeProjection.par3d.listeners; + for (i = 0; i < l.length; i++) { + activeSub = self.getObj(l[i]); + activeSub.par3d.FOV = Math.max(1, Math.min(179, activeSub.fov0 + + 180*(y-handlers.y0fov)/self.canvas.height)); + } + self.drawScene(); + }; + handlers.fovend = 0; + + handlers.selectingdown = function(x, y) { + var viewport = self.getObj(activeSubscene).par3d.viewport, + width = viewport.width*self.canvas.width, + height = viewport.height*self.canvas.height, + p = {x: 2.0*x/width - 1.0, y: 2.0*y/height - 1.0}; + self.select.region = {p1: p, p2: p}; + if (self.select.subscene && self.select.subscene !== activeSubscene) + self.delFromSubscene(self.scene.brushId, self.select.subscene); + self.select.subscene = activeSubscene; + self.addToSubscene(self.scene.brushId, activeSubscene); + self.select.state = "changing"; + if (typeof self.scene.brushId !== "undefined") + self.getObj(self.scene.brushId).initialized = false; + if (typeof self.scene.selectionInput !== "undefined") + self.recordSelection(activeSubscene); + self.drawScene(); + self.canvas.style.cursor = "crosshair"; + }; + + handlers.selectingmove = function(x, y) { + var viewport = self.getObj(activeSubscene).par3d.viewport, + width = viewport.width*self.canvas.width, + height = viewport.height*self.canvas.height; + if (self.select.state === "inactive") + return; + self.select.region.p2 = {x: 2.0*x/width - 1.0, y: 2.0*y/height - 1.0}; + if (typeof self.scene.brushId !== "undefined") + self.getObj(self.scene.brushId).initialized = false; + if (typeof self.scene.selectionInput !== "undefined") + self.recordSelection(activeSubscene); + self.drawScene(); + }; + + handlers.selectingend = 0; + /* jshint evil:true */ + handlers.userdown = function(x, y) { + var sub = self.getObj(activeSubscene), + code = sub.callbacks[drag].begin; + if (code) { + var fn = Function('"use strict";return (' + code + ')')(); + fn.call(self, x, y); + } + }; + + handlers.usermove = function(x, y) { + var sub = self.getObj(activeSubscene), + code = sub.callbacks[drag].update; + if (code) { + var fn = Function('"use strict";return (' + code + ')')(); + fn.call(self, x, y); + } + }; + + handlers.userend = function() { + var sub = self.getObj(activeSubscene), + code = sub.callbacks[drag].end; + if (code) { + var fn = Function('"use strict";return (' + code + ')')(); + fn.call(self); + } + }; + + self.canvas.onpointerdown = function ( ev ){ + // pointers and mice differ in capture rules; + // act like a mouse. + if (ev.target.hasPointerCapture(ev.pointerId)) + ev.target.releasePointerCapture(ev.pointerId); + + if (!ev.which) // Use w3c defns in preference to MS + switch (ev.button) { + case 0: ev.which = 1; break; + case 1: + case 4: ev.which = 2; break; + case 2: ev.which = 3; + } + drag = ["none", "left", "middle", "right", "wheel"][ev.which]; + var coords = self.relMouseCoords(ev); + coords.y = self.canvas.height-coords.y; + activeSubscene = self.whichSubscene(coords); + var sub = self.getObj(activeSubscene), f; + handler = sub.par3d.mouseMode[drag]; + switch (handler) { + case "xAxis": + handler = "axis"; + handlers.axis = [1.0, 0.0, 0.0]; + break; + case "yAxis": + handler = "axis"; + handlers.axis = [0.0, 1.0, 0.0]; + break; + case "zAxis": + handler = "axis"; + handlers.axis = [0.0, 0.0, 1.0]; + break; + } + f = handlers[handler + "down"]; + if (f) { + coords = self.translateCoords(activeSubscene, coords); + f.call(self, coords.x, coords.y); + ev.preventDefault(); + } else + console.warn("Mouse handler '" + handler + "' is not implemented."); + + }; + + self.canvas.onpointerup = function ( ev ){ + if ( !drag ) return; + var f = handlers[handler + "end"]; + if (f) { + f.call(self); + ev.preventDefault(); + } + drag = 0; + handlers.onpointermove( ev ); + }; + + self.canvas.onpointerout = self.canvas.onpointerup; + + handlers.onpointermove = function ( ev ) { + var coords = self.relMouseCoords(ev), sub, f; + coords.y = self.canvas.height - coords.y; + if (ev.buttons === 0) { + activeSubscene = self.whichSubscene(coords); + drag = "none"; + sub = self.getObj(activeSubscene); + handler = sub.par3d.mouseMode.none; + if (handler !== "none") { + if (sub.needsBegin) { + f = handlers[handler + "down"]; + if (f) { + coords = self.translateCoords(activeSubscene, coords); + f.call(self, coords.x, coords.y); + } + sub.needsBegin = 0; + } + self.canvas.style.cursor = self.getCursor(sub.par3d.mouseMode.none); + } else { + self.canvas.style.cursor = self.getCursor(sub.par3d.mouseMode.left); + return; + } + } + f = handlers[handler + "move"]; + if (f) { + coords = self.translateCoords(activeSubscene, coords); + f.call(self, coords.x, coords.y); + } + }; + + + self.canvas.onpointerenter = function() { + self.canvas.addEventListener("pointermove", handlers.onpointermove); + }; + + self.canvas.onpointerleave = function() { + self.canvas.removeEventListener("pointermove", + handlers.onpointermove); + }; + + handlers.setZoom = function(ds) { + var i; + if (typeof activeSubscene === "undefined") + activeSubscene = self.scene.rootSubscene; + var activeSub = self.getObj(activeSubscene), + activeProjection = self.getObj(self.useid(activeSub.id, "projection")), + l = activeProjection.par3d.listeners; + + for (i = 0; i < l.length; i++) { + activeSub = self.getObj(l[i]); + activeSub.par3d.zoom *= ds; + } + self.drawScene(); + }; + + handlers.pushwheel = function(ev) { + ev.deltaY = -ev.deltaY; + handlers.pullwheel(ev); + }; + + handlers.pullwheel = function(ev) { + var del = 1.05; + if (ev.shiftKey) del = 1.005; + var ds = ev.deltaY < 0 ? del : (1 / del); + handlers.setZoom(ds); + }; + + handlers.user2wheel = function(ev) { + var sub = self.getObj(activeSubscene), + code = sub.callbacks.wheel.rotate; + if (code) { + var fn = Function('"use strict";return (' + code + ')')(); + /* jshint evil:false */ + fn.call(self, ev.deltaY < 0 ? 1 : 2); + } + }; + + handlers.wheelHandler = function(ev) { + var coords = self.relMouseCoords(ev); + coords.y = self.canvas.height - coords.y; + activeSubscene = self.whichSubscene(coords); + var sub = self.getObj(activeSubscene), f, + handler = sub.par3d.mouseMode.wheel, + evlocal; + + ev.deltaY = ev.deltaY || ev.detail || ev.deltaX || ev.wheelDelta; + + switch(handler) { + case "none": break; + case "push": + case "pull": + case "user2": + f = handlers[handler + "wheel"]; + if (f) { + evlocal = {}; + evlocal.deltaY = ev.deltaY; + evlocal.shiftKey = ev.shiftKey; + evlocal.preventDefault = function() { ev.preventDefault(); }; + f.call(self, evlocal); + } + break; + default: + evlocal = {}; + evlocal.preventDefault = function() { ev.preventDefault(); }; + evlocal.which = 4; + evlocal.clientX = self.canvas.width/2; + evlocal.clientY = self.canvas.height/2; + self.canvas.onpointerdown(evlocal); + evlocal.clientX += ev.deltaX; + evlocal.clientY += ev.deltaY; + handlers.onpointermove(evlocal); + self.canvas.onpointerup(evlocal); + } + ev.preventDefault(); + }; + + handlers.get_finger_dist = function(ev) { + var diffX = ev.touches[0].clientX - ev.touches[1].clientX, + diffY = ev.touches[0].clientY - ev.touches[1].clientY; + return Math.sqrt(diffX * diffX + diffY * diffY); + }; + + handlers.touchstart = function(ev) { + var touch = ev.touches[0], + mouseEvent = new MouseEvent("pointerdown", + { + clientX: touch.clientX, + clientY: touch.clientY + }); + ev.preventDefault(); + if (ev.touches.length === 2) { + var coords = self.relMouseCoords(touch); + coords.y = self.canvas.height-coords.y; + activeSubscene = self.whichSubscene(coords); + handlers.finger_dist0 = handlers.get_finger_dist(ev); + handlers.zoomdown(coords.x, coords.y); + } + self.dispatchEvent(mouseEvent); + }; + + handlers.touchend = function(ev) { + var mouseEvent; + ev.preventDefault(); + if (ev.touches.length === 1) { + mouseEvent = new MouseEvent("pointerup", {}); + self.dispatchEvent(mouseEvent); + } + }; + + handlers.touchmove = function(ev) { + var touch = ev.touches[0], + mouseEvent; + ev.preventDefault(); + if (ev.touches.length > 1) { + var coords = self.relMouseCoords(touch), + new_dist = handlers.get_finger_dist(ev); + coords.y = self.canvas.height*Math.log(handlers.finger_dist0/new_dist) + handlers.y0zoom; + handlers.zoommove(coords.x, coords.y); + } else { + mouseEvent = new MouseEvent("pointermove", + { + clientX: touch.clientX, + clientY: touch.clientY + }); + self.dispatchEvent(mouseEvent); + } + }; + + self.canvas.addEventListener("DOMMouseScroll", handlers.wheelHandler, false); + self.canvas.addEventListener("mousewheel", handlers.wheelHandler, false); + self.canvas.addEventListener("touchstart", handlers.touchstart, {passive: false}); + self.canvas.addEventListener("touchend", handlers.touchend, {passive: false}); + self.canvas.addEventListener("touchmove", handlers.touchmove, {passive: false}); + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/pieces.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/pieces.src.js new file mode 100644 index 00000000..2ac8c91d --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/pieces.src.js @@ -0,0 +1,135 @@ +/** + * Methods related to drawing transparent objects + * @name ___METHODS_FOR_TRANSPARENCY___ + * @memberof rglwidgetClass + * @kind function + * @instance + + * These functions order the centers of displayed objects so they + * can be drawn using the painters algorithm, necessary to support + * transparency. + + * Note that objid is not obj.id when drawing spheres. + */ + +/** + * Break objects into pieces + * @returns { array } Array of pieces + */ + rglwidgetClass.prototype.getPieces = function(context, objid, subid, obj) { + var n = obj.centers.length, + depth, + result = new Array(n), + z, w, i; + context = context.slice(); + + for(i=0; i 0) { + var i, + thiscontext = pieces[0].context, + thisobjid = pieces[0].objid, + thissubid = pieces[0].subid, + indices = []; + for (i= 0; i < pieces.length; i++) { + if (pieces[i].context !== thiscontext || + pieces[i].objid !== thisobjid || + pieces[i].subid !== thissubid) { + result.push({context: thiscontext, objid: thisobjid, + subid: thissubid, indices: indices}); + thiscontext = pieces[i].context; + thisobjid = pieces[i].objid; + thissubid = pieces[i].subid; + indices = []; + } + indices.push(pieces[i].index); + } + result.push({context: thiscontext, objid: thisobjid, + subid: thissubid, + indices: indices}); + } + return result; + }; + + /** + * Sort pieces by depth + * @returns { array } + * @param { array } pieces - array of pieces + */ + rglwidgetClass.prototype.sortPieces = function(pieces) { + var compare = function(i,j) { + var diff = j.depth - i.depth; + // We want to avoid context or obj changes, + // so sort on those next. + if (diff === 0) { + var c1 = j.context.slice(), + c2 = i.context.slice(); + diff = c1.length - c2.length; + while (diff === 0 && c1.length > 0) { + diff = c1.pop() - c2.pop(); + } + if (diff === 0) + diff = j.objid - i.objid; + if (diff === 0) + diff = j.subid - i.subid; + } + return diff; + }, result = []; + if (pieces.length) + result = pieces.sort(compare); + return result; + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/pretty.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/pretty.src.js new file mode 100644 index 00000000..5f9145aa --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/pretty.src.js @@ -0,0 +1,163 @@ +/** + * Pretty function from R + * @name ___PRETTY_FROM_R___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + +/* This file is translated from pretty.c, which was + taken from the R sources, r61744 of src/appl/pretty.c, + with minimal changes */ + +/* + * R : A Computer Language for Statistical Data Analysis + * Copyright (C) 1995-2012 The R Core Team + * + * This program is free software; you can redistribute it and/or modify + * it under the terms of the GNU General Public License as published by + * the Free Software Foundation; either version 2 of the License, or + * (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program; if not, a copy is available at + * http://www.r-project.org/Licenses/ + */ + +/** + * Construct pretty values to cover an interval + * @param { number } lo - lower end of interval + * @param { number } up - upper end of interval + * @param { number } ndiv - requested number of divisions + * @param { number } min_n - minimum divisions + * @param { number } shrink_sml - if too many cells, amount to shrink by + * @param { number } high_u_fact - bias in favour of larger units + * @param { number } eps_correction - correction to bounds + * @param { Boolean } return_bounds - whether to return bounds + * @description + * Pretty Intervals + + * Constructs m "pretty" values which cover the given interval *lo <= *up + * m ~= *ndiv + 1 (i.e., ndiv := approximate number of INTERVALS) + * + * It is not quite clear what should happen for *lo = *up; + * S itself behaves quite funilly, then. + * + * In my opinion, a proper 'pretty' should always ensure + * *lo < *up, and hence *ndiv >=1 in the result. + * However, in S and here, we allow *lo == *up, and *ndiv = 0. + * Note however, that we are NOT COMPATIBLE to S. [Martin M.] + * + * NEW (0.63.2): ns, nu are double (==> no danger of integer overflow) + * + * We determine + * if the interval (up - lo) is ``small'' [<==> i_small == TRUE, below]. + * For the ``i_small'' situation, there is a parameter shrink_sml, + * the factor by which the "scale" is shrunk. ~~~~~~~~~~ + * It is advisable to set it to some (smaller) integer power of 2, + * since this enables exact floating point division. + */ +rglwidgetClass.prototype.R_pretty = function( + lo, up, ndiv, min_n, shrink_sml, high_u_fact, + eps_correction, return_bounds) { + /* From version 0.65 on, we had rounding_eps := 1e-5, before, r..eps = 0 + * 1e-7 is consistent with seq.default() */ + var rounding_eps = 1e-7, h = high_u_fact[0], + h5 = high_u_fact[1], + dx, cell, unit, base, U, ns, nu, k, i_small, + DBL_EPSILON = Number.EPSILON, + DBL_MIN = Number.MIN_VALUE, + DBL_MAX = Number.MAX_VALUE; + + dx = up - lo; + /* cell := "scale" here */ + if (dx === 0 && up === 0) { /* up == lo == 0 */ + cell = 1; + i_small = true; + } else { + cell = Math.max(Math.abs(lo), Math.abs(up)); + /* U = upper bound on cell/unit */ + U = (1 + (h5 >= 1.5*h+0.5)) ? 1/(1+h) : 1.5/(1+h5); + /* added times 3, as several calculations here */ + i_small = dx < cell * U * Math.max(1,ndiv) * DBL_EPSILON *3; + } + + /*OLD: cell = FLT_EPSILON+ dx / *ndiv; FLT_EPSILON = 1.192e-07 */ + if(i_small) { + if(cell > 10) + cell = 9 + cell/10; + cell *= shrink_sml; + if(min_n > 1) cell /= min_n; + } else { + cell = dx; + if(ndiv > 1) cell /= ndiv; + } + + if(cell < 20*DBL_MIN) { + /* warning(_("Internal(pretty()): very small range.. corrected")); */ + cell = 20*DBL_MIN; + } else if(cell * 10 > DBL_MAX) { + /* warning(_("Internal(pretty()): very large range.. corrected")); */ + cell = 0.1*DBL_MAX; + } + base = Math.pow(10, Math.floor(Math.log10(cell))); /* base <= cell < 10*base */ + + /* unit : from { 1,2,5,10 } * base + * such that |u - cell| is small, + * favoring larger (if h > 1, else smaller) u values; + * favor '5' more than '2' if h5 > h (default h5 = .5 + 1.5 h) */ + unit = base; + if((U = 2*base)-cell < h*(cell-unit)) { unit = U; + if((U = 5*base)-cell < h5*(cell-unit)) { unit = U; + if((U =10*base)-cell < h*(cell-unit)) unit = U; }} + /* Result: c := cell, u := unit, b := base + * c in [ 1, (2+ h) /(1+h) ] b ==> u= b + * c in ( (2+ h)/(1+h), (5+2h5)/(1+h5)] b ==> u= 2b + * c in ( (5+2h)/(1+h), (10+5h) /(1+h) ] b ==> u= 5b + * c in ((10+5h)/(1+h), 10 ) b ==> u=10b + * + * ===> 2/5 *(2+h)/(1+h) <= c/u <= (2+h)/(1+h) */ + + ns = Math.floor(lo/unit+rounding_eps); + nu = Math.ceil (up/unit-rounding_eps); + + if(eps_correction && (eps_correction > 1 || !i_small)) { + if(lo !== 0.0) lo *= (1- DBL_EPSILON); else lo = -DBL_MIN; + if(up !== 0.0) up *= (1+ DBL_EPSILON); else up = +DBL_MIN; + } + + while(ns*unit > lo + rounding_eps*unit) ns--; + + while(nu*unit < up - rounding_eps*unit) nu++; + + k = Math.floor(0.5 + nu - ns); + if(k < min_n) { + /* ensure that nu - ns == min_n */ + + k = min_n - k; + if(ns >= 0) { + nu += k/2; + ns -= k/2 + k%2;/* ==> nu-ns = old(nu-ns) + min_n -k = min_n */ + } else { + ns -= k/2; + nu += k/2 + k%2; + } + ndiv = min_n; + } else { + ndiv = k; + } + if(return_bounds) { /* if()'s to ensure that result covers original range */ + if(ns * unit < lo) lo = ns * unit; + if(nu * unit > up) up = nu * unit; + } else { + lo = ns; + up = nu; + } + return {lo:lo, up:up, ndiv:ndiv, unit:unit}; +}; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/projection.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/projection.src.js new file mode 100644 index 00000000..4fc59c79 --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/projection.src.js @@ -0,0 +1,148 @@ + /** + * Methods related to projections + * @name ___METHODS_FOR_PROJECTIONS___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + /** + * Get the viewport + */ + + rglwidgetClass.prototype.getViewport = function(id) { + var vp = this.getObj(id).par3d.viewport, + x = vp.x*this.canvas.width, + y = vp.y*this.canvas.height, + width = vp.width*this.canvas.width, + height = vp.height*this.canvas.height; + this.vp = {x:x, y:y, width:width, height:height}; + }; + + /** + * Set the gl viewport and scissor test + * @param { number } id - id of subscene + */ + rglwidgetClass.prototype.setViewport = function(id) { + var gl = this.gl || this.initGL(); + this.getViewport(id); + gl.viewport(this.vp.x, this.vp.y, this.vp.width, this.vp.height); + gl.scissor(this.vp.x, this.vp.y, this.vp.width, this.vp.height); + gl.enable(gl.SCISSOR_TEST); + }; + + /** + * Set the projection matrix for a subscene + * @param { number } id - id of subscene + */ + rglwidgetClass.prototype.setprMatrix = function(id) { + var subscene = this.getObj(id), + embedding = subscene.embeddings.projection; + if (embedding === "replace") + this.prMatrix.makeIdentity(); + else + this.setprMatrix(subscene.parent); + if (embedding === "inherit") + return; + // This is based on the Frustum::enclose code from geom.cpp + var bbox = subscene.par3d.bbox, + scale = subscene.par3d.scale, + ranges = [(bbox[1]-bbox[0])*scale[0]/2, + (bbox[3]-bbox[2])*scale[1]/2, + (bbox[5]-bbox[4])*scale[2]/2], + radius = Math.sqrt(this.sumsq(ranges))*1.1; // A bit bigger to handle labels + if (radius <= 0) radius = 1; + var observer = subscene.par3d.observer, + distance = observer[2], + FOV = subscene.par3d.FOV, ortho = FOV === 0, + t = ortho ? 1 : Math.tan(FOV*Math.PI/360), + near = distance - radius, + far = distance + radius, + hlen, + aspect = this.vp.width/this.vp.height, + z = subscene.par3d.zoom, + userProjection = subscene.par3d.userProjection; + if (far < 0.0) + far = 1.0; + if (near < far/100.0) + near = far/100.0; + this.frustum = {near:near, far:far}; + hlen = t*near; + if (ortho) { + if (aspect > 1) + this.prMatrix.ortho(-hlen*aspect*z, hlen*aspect*z, + -hlen*z, hlen*z, near, far); + else + this.prMatrix.ortho(-hlen*z, hlen*z, + -hlen*z/aspect, hlen*z/aspect, + near, far); + } else { + if (aspect > 1) + this.prMatrix.frustum(-hlen*aspect*z, hlen*aspect*z, + -hlen*z, hlen*z, near, far); + else + this.prMatrix.frustum(-hlen*z, hlen*z, + -hlen*z/aspect, hlen*z/aspect, + near, far); + } + this.prMatrix.multRight(userProjection); + }; + + /** + * Set the model-view matrix for a subscene + * @param { number } id - id of the subscene + */ + rglwidgetClass.prototype.setmvMatrix = function(id) { + var observer = this.getObj(id).par3d.observer; + this.mvMatrix.makeIdentity(); + this.setmodelMatrix(id); + this.mvMatrix.translate(-observer[0], -observer[1], -observer[2]); + + }; + + /** + * Set the model matrix for a subscene + * @param { number } id - id of the subscene + */ + rglwidgetClass.prototype.setmodelMatrix = function(id) { + var subscene = this.getObj(id), + embedding = subscene.embeddings.model; + if (embedding === "replace") { + var bbox = subscene.par3d.bbox, + center = [(bbox[0]+bbox[1])/2, + (bbox[2]+bbox[3])/2, + (bbox[4]+bbox[5])/2]; + this.mvMatrix.translate(-center[0], -center[1], -center[2]); + } + if (embedding !== "inherit") { + var scale = subscene.par3d.scale; + this.mvMatrix.scale(scale[0], scale[1], scale[2]); + this.mvMatrix.multRight( subscene.par3d.userMatrix ); + } + if (embedding !== "replace") + this.setmodelMatrix(subscene.parent); + }; + + /** + * Set the normals matrix for a subscene + * @param { number } subsceneid - id of the subscene + */ + rglwidgetClass.prototype.setnormMatrix2 = function() { + this.normMatrix = new CanvasMatrix4(this.mvMatrix); + this.normMatrix.invert(); + this.normMatrix.transpose(); + }; + + /** + * Set the combined projection-model-view matrix + */ + rglwidgetClass.prototype.setprmvMatrix = function() { + this.prmvMatrix = new CanvasMatrix4( this.mvMatrix ); + this.prmvMatrix.multRight( this.prMatrix ); + }; + + rglwidgetClass.prototype.setInvPrMatrix = function() { + this.invPrMatrix = new CanvasMatrix4( this.prMatrix ); + this.invPrMatrix.invert(); + this.invPrMatrix.transpose(); + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/rgl.css b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/rgl.css new file mode 100644 index 00000000..b22aaf65 --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/rgl.css @@ -0,0 +1,21 @@ +.rglPlayer { + width: auto; + height: auto; +} + +.rglPlayer .rgl-button { + width: auto; + display: inline-block; + font-size: 75%; +} + +.rglPlayer .rgl-slider { + display: inline-block; + width: 30%; +} + +.rglPlayer .rgl-label { + display: inline; + padding-left: 6px; + padding-right: 6px; +} diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/rglClass.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/rglClass.src.js new file mode 100644 index 00000000..475300ee --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/rglClass.src.js @@ -0,0 +1,71 @@ +//// To generate the help pages for this library, use + +// jsdoc --template /usr/local/lib/node_modules/foodoc/template *.src.js -R README.md -c JSDoc.json + +// To test, set environment variable RGL_DEBUGGING=true +// before building. + +/* globals rglwidgetClass: true */ + +/** + * The class of an rgl widget + * @class +*/ +rglwidgetClass = function() { + this.canvas = null; + this.userMatrix = new CanvasMatrix4(); + this.types = []; + this.prMatrix = new CanvasMatrix4(); + this.mvMatrix = new CanvasMatrix4(); + this.vp = null; + this.prmvMatrix = null; + this.origs = null; + this.gl = null; + this.scene = null; + this.select = {state: "inactive", subscene: null, region: {p1: {x:0, y:0}, p2: {x:0, y:0}}}; + this.drawing = false; +}; + + rglwidgetClass.f_is_lit = 1; + rglwidgetClass.f_is_smooth = 2; + rglwidgetClass.f_has_texture = 4; + rglwidgetClass.f_depth_sort = 8; + rglwidgetClass.f_fixed_quads = 16; + rglwidgetClass.f_is_transparent = 32; + rglwidgetClass.f_is_lines = 64; + rglwidgetClass.f_sprites_3d = 128; + rglwidgetClass.f_is_subscene = 256; + rglwidgetClass.f_is_clipplanes = 512; + rglwidgetClass.f_fixed_size = 1024; + rglwidgetClass.f_is_points = 2048; + rglwidgetClass.f_is_twosided = 4096; + rglwidgetClass.f_fat_lines = 8192; + rglwidgetClass.f_is_brush = 16384; + rglwidgetClass.f_has_fog = 32768; + rglwidgetClass.f_rotating = 65536; + + rglwidgetClass.prototype.fogNone = 0; + rglwidgetClass.prototype.fogLinear = 1; + rglwidgetClass.prototype.fogExp = 2; + rglwidgetClass.prototype.fogExp2 = 3; + + /** + * Methods related to obsolete approaches. + * @name ___OBSOLETE_METHODS___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + /** + * Start the writeWebGL scene. This is only used by writeWebGL; rglwidget has + no debug element. + */ + rglwidgetClass.prototype.start = function() { + if (typeof this.prefix !== "undefined") { + this.debugelement = document.getElementById(this.prefix + "debug"); + this.debug(""); + } + this.drag = 0; + this.drawScene(); + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/rglTimer.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/rglTimer.src.js new file mode 100644 index 00000000..10915569 --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/rglTimer.src.js @@ -0,0 +1,155 @@ + +/* globals rgltimerClass: true */ + +/** + * The class of an rgl timer object + * @class +*/ + +/** + * Construct an rgltimerClass object + * @constructor + * @param { function } Tick - action when timer fires + * @param { number } startTime - nominal start time in seconds + * @param { number } interval - seconds between updates + * @param { number } stopTime - nominal stop time in seconds + * @param { number } stepSize - nominal step size + * @param { number } value - current nominal time + * @param { number } rate - nominal units per second + * @param { string } loop - "none", "cycle" or "oscillate" + * @param { Object } actions - list of actions + */ +rgltimerClass = function(Tick, startTime, interval, stopTime, stepSize, value, rate, loop, actions) { + this.enabled = false; + this.timerId = 0; + /** nominal start time in seconds */ + this.startTime = startTime; + /** current nominal time */ + this.value = value; + /** seconds between updates */ + this.interval = interval; + /** nominal stop time */ + this.stopTime = stopTime; + /** nominal step size */ + this.stepSize = stepSize; + /** nominal units per second */ + this.rate = rate; + /** "none", "cycle", or "oscillate" */ + this.loop = loop; + /** real world start time */ + this.realStart = undefined; + /** multiplier for fast-forward or reverse */ + this.multiplier = 1; + this.actions = actions; + this.Tick = Tick; +}; + + + /** + * Methods related to players + * @name ___METHODS_FOR_PLAYERS___ + * @memberof rgltimerClass + * @kind function + * @instance + */ + + /** + * Start playing + * @memberof rgltimerClass + */ + rgltimerClass.prototype.play = function() { + if (this.enabled) { + this.enabled = false; + window.clearInterval(this.timerId); + this.timerId = 0; + return; + } + var tick = function(self) { + var now = new Date(); + self.value = self.multiplier*self.rate*(now - self.realStart)/1000 + self.startTime; + self.forceToRange(); + if (typeof self.Tick !== "undefined") { + self.Tick(self.value); + } + + }; + this.realStart = new Date() - 1000*(this.value - this.startTime)/this.rate/this.multiplier; + this.timerId = window.setInterval(tick, 1000*this.interval, this); + this.enabled = true; + }; + + /** + * Force value into legal range + */ + rgltimerClass.prototype.forceToRange = function() { + if (this.value > this.stopTime + this.stepSize/2 || this.value < this.startTime - this.stepSize/2) { + if (!this.loop) { + this.reset(); + } else { + var cycle = this.stopTime - this.startTime + this.stepSize, + newval = (this.value - this.startTime) % cycle + this.startTime; + if (newval < this.startTime) { + newval += cycle; + } + this.realStart += (this.value - newval)*1000/this.multiplier/this.rate; + this.value = newval; + } + } + }; + + /** + * Reset to start values + */ + rgltimerClass.prototype.reset = function() { + this.value = this.startTime; + this.newmultiplier(1); + if (typeof this.Tick !== "undefined") { + this.Tick(this.value); + } + if (this.enabled) + this.play(); /* really pause... */ + if (typeof this.PlayButton !== "undefined") + this.PlayButton.value = "Play"; + }; + + /** + * Increase the multiplier to play faster + */ + rgltimerClass.prototype.faster = function() { + this.newmultiplier(Math.SQRT2*this.multiplier); + }; + + /** + * Decrease the multiplier to play slower + */ + rgltimerClass.prototype.slower = function() { + this.newmultiplier(this.multiplier/Math.SQRT2); + }; + + /** + * Change sign of multiplier to reverse direction + */ + rgltimerClass.prototype.reverse = function() { + this.newmultiplier(-this.multiplier); + }; + + /** + * Set multiplier for play speed + * @param { number } newmult - new value + */ + rgltimerClass.prototype.newmultiplier = function(newmult) { + if (newmult !== this.multiplier) { + this.realStart += 1000*(this.value - this.startTime)/this.rate*(1/this.multiplier - 1/newmult); + this.multiplier = newmult; + } + }; + + /** + * Take one step + */ + rgltimerClass.prototype.step = function() { + this.value += this.rate*this.multiplier; + this.forceToRange(); + if (typeof this.Tick !== "undefined") + this.Tick(this.value); + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/selection.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/selection.src.js new file mode 100644 index 00000000..a45e09e2 --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/selection.src.js @@ -0,0 +1,129 @@ + /** + * Methods related to selection + * @name ___METHODS_FOR_SELECTION___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + /** + * Respond to brush change + */ + rglwidgetClass.prototype.selectionChanged = function() { + var i, j, k, id, subid = this.select.subscene, subscene, + objids, obj, + p1 = this.select.region.p1, p2 = this.select.region.p2, + filter, selection = [], handle, keys, xmin, x, xmax, ymin, y, ymax, z, v, + someHidden; + if (!subid) + return; + subscene = this.getObj(subid); + objids = subscene.objects; + filter = this.scene.crosstalk.filter; + this.setmvMatrix(subid); + this.setprMatrix(subid); + this.setprmvMatrix(); + xmin = Math.min(p1.x, p2.x); + xmax = Math.max(p1.x, p2.x); + ymin = Math.min(p1.y, p2.y); + ymax = Math.max(p1.y, p2.y); + for (i = 0; i < objids.length; i++) { + id = objids[i]; + j = this.scene.crosstalk.id.indexOf(id); + if (j >= 0) { + keys = this.scene.crosstalk.key[j]; + obj = this.getObj(id); + someHidden = false; + for (k = 0; k < keys.length; k++) { + if (filter && filter.indexOf(keys[k]) < 0) { + someHidden = true; + continue; + } + v = [].concat(obj.vertices[k]).concat(1.0); + v = rglwidgetClass.multVM(v, this.prmvMatrix); + x = v[0]/v[3]; + y = v[1]/v[3]; + z = v[2]/v[3]; + if (xmin <= x && x <= xmax && ymin <= y && y <= ymax && -1.0 <= z && z <= 1.0) { + selection.push(keys[k]); + } else + someHidden = true; + } + obj.someHidden = someHidden && (filter || selection.length); + obj.initialized = false; + /* Who should we notify? Only shared data in the current subscene, or everyone? */ + if (!this.equalArrays(selection, this.scene.crosstalk.selection)) { + handle = this.scene.crosstalk.sel_handle[j]; + handle.set(selection, {rglSubsceneId: this.select.subscene}); + } + } + } + }; + + /** + * Respond to selection or filter change from crosstalk + * @param { Object } event - crosstalk event + * @param { boolean } filter - filter or selection? + */ + rglwidgetClass.prototype.selection = function(event, filter) { + var i, j, ids, obj, keys, crosstalk = this.scene.crosstalk, + selection, someHidden; + + // Record the message and find out if this event makes some objects have mixed values: + + crosstalk = this.scene.crosstalk; + + if (filter) { + filter = crosstalk.filter = event.value; + selection = crosstalk.selection; + } else { + selection = crosstalk.selection = event.value; + filter = crosstalk.filter; + } + ids = crosstalk.id; + for (i = 0; i < ids.length ; i++) { + obj = this.getObj(ids[i]); + obj.initialized = false; + keys = crosstalk.key[i]; + someHidden = false; + for (j = 0; j < keys.length && !someHidden; j++) { + if ((filter && filter.indexOf(keys[j]) < 0) || + (selection.length && selection.indexOf(keys[j]) < 0)) + someHidden = true; + } + obj.someHidden = someHidden; + } + this.drawScene(); + }; + + /** + * Clear the selection brush + * @param { number } except - Subscene that should ignore this request + */ + rglwidgetClass.prototype.clearBrush = function(except) { + if (this.select.subscene !== except) { + this.select.region = {p1: {x:Infinity, y:Infinity}, + p2: {x:Infinity, y:Infinity}}; + this.selectionChanged(); + this.select.state = "inactive"; + this.delFromSubscene(this.scene.brushId, this.select.subscene); + } + this.drawScene(); + }; + + /** + * Set the vertices in the selection box object + */ + rglwidgetClass.prototype.initSelection = function(id) { + if (typeof this.select.region === "undefined") + return; + var obj = this.getObj(id), + p1 = this.select.region.p1, + p2 = this.select.region.p2; + + obj.vertices = [[p1.x, p1.y, 0.0], + [p2.x, p1.y, 0.0], + [p2.x, p2.y, 0.0], + [p1.x, p2.y, 0.0], + [p1.x, p1.y, 0.0]]; + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/shaders.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/shaders.src.js new file mode 100644 index 00000000..e6466787 --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/shaders.src.js @@ -0,0 +1,183 @@ + /** + * Methods related to shaders + * @name ___METHODS_FOR_SHADERS___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + /** + * Get flags that will end up as shader defines. + * Static method so it can be called from R + */ + rglwidgetClass.getDefFlags = function(flags, type, normals, round_points) { + var f = {}; + f.fat_lines = rglwidgetClass.isSet(flags, rglwidgetClass.f_fat_lines); + f.fixed_quads = rglwidgetClass.isSet(flags, rglwidgetClass.f_fixed_quads); + f.fixed_size = rglwidgetClass.isSet(flags, rglwidgetClass.f_fixed_size); + f.has_fog = rglwidgetClass.isSet(flags, rglwidgetClass.f_has_fog); + f.has_normals = (typeof normals !== "undefined") || + type === "spheres"; + f.has_texture = rglwidgetClass.isSet(flags, rglwidgetClass.f_has_texture); + f.is_brush = rglwidgetClass.isSet(flags, rglwidgetClass.f_is_brush); + f.is_lines = rglwidgetClass.isSet(flags, rglwidgetClass.f_is_lines); + f.is_lit = rglwidgetClass.isSet(flags, rglwidgetClass.f_is_lit); + f.is_points = rglwidgetClass.isSet(flags, rglwidgetClass.f_is_points); + f.is_transparent = rglwidgetClass.isSet(flags, rglwidgetClass.f_is_transparent); + f.is_twosided = rglwidgetClass.isSet(flags, rglwidgetClass.f_is_twosided); + f.needs_vnormal = !rglwidgetClass.isSet(flags, rglwidgetClass.f_sprites_3d) && + (f.is_lit && !f.fixed_quads && !f.is_brush) || (f.is_twosided && f.has_normals); + f.rotating = rglwidgetClass.isSet(flags, rglwidgetClass.f_rotating); + f.round_points = round_points; + return f; + }; + + + /** + * Generate the defines for the shader code for an object. + * + * This is a static method so it can be called from R. + * + * @returns {string} + * @param id - id of object + * @param type - type of object + * @param flags - object flags + * @param nclipplanes - number of clipping planes in scene + * (may not all be active) + * @param nlights - number of lights in scene (ditto) + * @param normals - normals for object + * @param pointSize - point size for object + * @param textype - texture type for object + * @param antialias - use antialiasing? + */ + rglwidgetClass.getDefines = function(id, type, flags, + nclipplanes, nlights, normals, pointSize, textype, + texmode, texenvmap, antialias, fl) { + var + title, defines; + + if (typeof fl === "undefined") + fl = rglwidgetClass.getDefFlags(flags, type, normals, antialias); + + title = " /* ****** "+type+" object "+id+" shader ****** */\n"; + + defines = "#define NCLIPPLANES " + nclipplanes + "\n"+ + "#define NLIGHTS " + nlights + "\n"; + + if (fl.fat_lines) + defines = defines + "#define FAT_LINES 1\n"; + + if (fl.fixed_quads) + defines = defines + "#define FIXED_QUADS 1\n"; + + if (fl.fixed_size) + defines = defines + "#define FIXED_SIZE 1\n"; + + if (fl.has_fog) + defines = defines + "#define HAS_FOG 1\n"; + + if (fl.has_normals) + defines = defines + "#define HAS_NORMALS 1\n"; + + if (fl.has_texture) { + defines = defines + "#define HAS_TEXTURE 1\n"; + defines = defines + "#define TEXTURE_" + textype + "\n"; + defines = defines + "#define TEXMODE_" + texmode + "\n"; + if (texenvmap) + defines = defines + "#define USE_ENVMAP 1\n"; + } + + if (fl.is_brush) + defines = defines + "#define IS_BRUSH 1\n"; + + if (type === "linestrip") + defines = defines + "#define IS_LINESTRIP 1\n"; + + if (fl.is_lit) + defines = defines + "#define IS_LIT 1\n"; + + if (fl.is_points) { + defines = defines + "#define IS_POINTS 1\n"; + defines = defines + "#define POINTSIZE " + Number.parseFloat(pointSize).toFixed(1) + "\n"; + } + + if (type === "sprites") + defines = defines + "#define IS_SPRITES 1\n"; + + if (type === "text") + defines = defines + "#define IS_TEXT 1\n"; + + if (fl.is_transparent) + defines = defines + "#define IS_TRANSPARENT 1\n"; + + if (fl.is_twosided) + defines = defines + "#define IS_TWOSIDED 1\n"; + + if (fl.needs_vnormal) + defines = defines + "#define NEEDS_VNORMAL 1\n"; + + if (fl.rotating) + defines = defines + "#define ROTATING 1\n"; + + if (fl.round_points) + defines = defines + "#define ROUND_POINTS 1\n"; + + // console.log(result); + return title + defines; + }; + + /** + * Create code for vertex and fragment shaders + * @returns {Object} + * @param { number } shaderType - gl code for shader type + * @param { string } code - code for the shader + */ + rglwidgetClass.prototype.getShaders = function(obj) { + var header, + vertex = obj.userVertexShader, + fragment = obj.userFragmentShader; + + header = rglwidgetClass.getDefines( + obj.id, obj.type, obj.flags, + this.countClipplanes(), this.countLights(), + obj.normals, + this.getMaterial(obj, "size"), + this.getMaterial(obj, "textype"), + this.getMaterial(obj, "texmode"), + this.getMaterial(obj, "texenvmap"), + this.getMaterial(obj, "point_antialias"), + obj.defFlags + ); + + if (typeof vertex === "undefined") + vertex = rglwidgetClass.rgl_vertex_shader(); + + if (typeof fragment === "undefined") + fragment = rglwidgetClass.rgl_fragment_shader(); + +// console.log("vertex:"); +// console.log(header + vertex); +// console.log("fragment:"); +// console.log(header + fragment); + + return {vertex: header + vertex, + fragment: header + fragment}; + }; + + + /** + * Call gl functions to create and compile shader from code + * @returns {Object} + * @param { number } shaderType - gl code for shader type + * @param { string } code - code for the shader + */ + rglwidgetClass.prototype.getShader = function(shaderType, code) { + var gl = this.gl, shader; + shader = gl.createShader(shaderType); + gl.shaderSource(shader, code); + gl.compileShader(shader); + if (!gl.getShaderParameter(shader, gl.COMPILE_STATUS) && !gl.isContextLost()) + alert(gl.getShaderInfoLog(shader)); + return shader; + }; + diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/shadersrc.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/shadersrc.src.js new file mode 100644 index 00000000..5dc11e94 --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/shadersrc.src.js @@ -0,0 +1,445 @@ +rglwidgetClass.rgl_vertex_shader = function() { +return "#line 2 1\n"+ +"// File 1 is the vertex shader\n"+ +"#ifdef GL_ES\n"+ +"#ifdef GL_FRAGMENT_PRECISION_HIGH\n"+ +"precision highp float;\n"+ +"#else\n"+ +"precision mediump float;\n"+ +"#endif\n"+ +"#endif\n"+ +"\n"+ +"attribute vec3 aPos;\n"+ +"attribute vec4 aCol;\n"+ +"uniform mat4 mvMatrix;\n"+ +"uniform mat4 prMatrix;\n"+ +"varying vec4 vCol;\n"+ +"varying vec4 vPosition;\n"+ +"\n"+ +"#ifdef NEEDS_VNORMAL\n"+ +"attribute vec3 aNorm;\n"+ +"uniform mat4 normMatrix;\n"+ +"varying vec4 vNormal;\n"+ +"#endif\n"+ +"\n"+ +"#if defined(HAS_TEXTURE) || defined (IS_TEXT)\n"+ +"attribute vec2 aTexcoord;\n"+ +"varying vec2 vTexcoord;\n"+ +"#endif\n"+ +"\n"+ +"#ifdef FIXED_SIZE\n"+ +"uniform vec3 textScale;\n"+ +"#endif\n"+ +"\n"+ +"#ifdef FIXED_QUADS\n"+ +"attribute vec3 aOfs;\n"+ +"#endif\n"+ +"\n"+ +"#ifdef IS_TWOSIDED\n"+ +"#ifdef HAS_NORMALS\n"+ +"varying float normz;\n"+ +"uniform mat4 invPrMatrix;\n"+ +"#else\n"+ +"attribute vec3 aPos1;\n"+ +"attribute vec3 aPos2;\n"+ +"varying float normz;\n"+ +"#endif\n"+ +"#endif // IS_TWOSIDED\n"+ +"\n"+ +"#ifdef FAT_LINES\n"+ +"attribute vec3 aNext;\n"+ +"attribute vec2 aPoint;\n"+ +"varying vec2 vPoint;\n"+ +"varying float vLength;\n"+ +"uniform float uAspect;\n"+ +"uniform float uLwd;\n"+ +"#endif\n"+ +"\n"+ +"#ifdef USE_ENVMAP\n"+ +"varying vec3 vReflection;\n"+ +"#endif\n"+ +"\n"+ +"void main(void) {\n"+ +" \n"+ +"#ifndef IS_BRUSH\n"+ +"#if defined(NCLIPPLANES) || !defined(FIXED_QUADS) || defined(HAS_FOG) || defined(USE_ENVMAP)\n"+ +" vPosition = mvMatrix * vec4(aPos, 1.);\n"+ +"#endif\n"+ +" \n"+ +"#ifndef FIXED_QUADS\n"+ +" gl_Position = prMatrix * vPosition;\n"+ +"#endif\n"+ +"#endif // !IS_BRUSH\n"+ +" \n"+ +"#ifdef IS_POINTS\n"+ +" gl_PointSize = POINTSIZE;\n"+ +"#endif\n"+ +" \n"+ +" vCol = aCol;\n"+ +" \n"+ +"// USE_ENVMAP implies NEEDS_VNORMAL\n"+ +"\n"+ +"#ifdef NEEDS_VNORMAL\n"+ +" vNormal = normMatrix * vec4(-aNorm, dot(aNorm, aPos));\n"+ +"#endif\n"+ +"\n"+ +"#ifdef USE_ENVMAP\n"+ +" vReflection = normalize(reflect(vPosition.xyz/vPosition.w, \n"+ +" normalize(vNormal.xyz/vNormal.w)));\n"+ +"#endif\n"+ +" \n"+ +"#ifdef IS_TWOSIDED\n"+ +"#ifdef HAS_NORMALS\n"+ +" /* normz should be calculated *after* projection */\n"+ +" normz = (invPrMatrix*vNormal).z;\n"+ +"#else\n"+ +" vec4 pos1 = prMatrix*(mvMatrix*vec4(aPos1, 1.));\n"+ +" pos1 = pos1/pos1.w - gl_Position/gl_Position.w;\n"+ +" vec4 pos2 = prMatrix*(mvMatrix*vec4(aPos2, 1.));\n"+ +" pos2 = pos2/pos2.w - gl_Position/gl_Position.w;\n"+ +" normz = pos1.x*pos2.y - pos1.y*pos2.x;\n"+ +"#endif\n"+ +"#endif // IS_TWOSIDED\n"+ +" \n"+ +"#ifdef NEEDS_VNORMAL\n"+ +" vNormal = vec4(normalize(vNormal.xyz/vNormal.w), 1);\n"+ +"#endif\n"+ +" \n"+ +"#if defined(HAS_TEXTURE) || defined(IS_TEXT)\n"+ +" vTexcoord = aTexcoord;\n"+ +"#endif\n"+ +" \n"+ +"#if defined(FIXED_SIZE) && !defined(ROTATING)\n"+ +" vec4 pos = prMatrix * mvMatrix * vec4(aPos, 1.);\n"+ +" pos = pos/pos.w;\n"+ +" gl_Position = pos + vec4(aOfs*textScale, 0.);\n"+ +"#endif\n"+ +" \n"+ +"#if defined(IS_SPRITES) && !defined(FIXED_SIZE)\n"+ +" vec4 pos = mvMatrix * vec4(aPos, 1.);\n"+ +" pos = pos/pos.w + vec4(aOfs, 0.);\n"+ +" gl_Position = prMatrix*pos;\n"+ +"#endif\n"+ +" \n"+ +"#ifdef FAT_LINES\n"+ +" /* This code was inspired by Matt Deslauriers' code in \n"+ +" https://mattdesl.svbtle.com/drawing-lines-is-hard */\n"+ +" vec2 aspectVec = vec2(uAspect, 1.0);\n"+ +" mat4 projViewModel = prMatrix * mvMatrix;\n"+ +" vec4 currentProjected = projViewModel * vec4(aPos, 1.0);\n"+ +" currentProjected = currentProjected/currentProjected.w;\n"+ +" vec4 nextProjected = projViewModel * vec4(aNext, 1.0);\n"+ +" vec2 currentScreen = currentProjected.xy * aspectVec;\n"+ +" vec2 nextScreen = (nextProjected.xy / nextProjected.w) * aspectVec;\n"+ +" float len = uLwd;\n"+ +" vec2 dir = vec2(1.0, 0.0);\n"+ +" vPoint = aPoint;\n"+ +" vLength = length(nextScreen - currentScreen)/2.0;\n"+ +" vLength = vLength/(vLength + len);\n"+ +" if (vLength > 0.0) {\n"+ +" dir = normalize(nextScreen - currentScreen);\n"+ +" }\n"+ +" vec2 normal = vec2(-dir.y, dir.x);\n"+ +" dir.x /= uAspect;\n"+ +" normal.x /= uAspect;\n"+ +" vec4 offset = vec4(len*(normal*aPoint.x*aPoint.y - dir), 0.0, 0.0);\n"+ +" gl_Position = currentProjected + offset;\n"+ +"#endif\n"+ +" \n"+ +"#ifdef IS_BRUSH\n"+ +" gl_Position = vec4(aPos, 1.);\n"+ +"#endif\n"+ +"}\n" ;}; +rglwidgetClass.rgl_fragment_shader = function() { +return "#line 2 2\n"+ +"// File 2 is the fragment shader\n"+ +"#ifdef GL_ES\n"+ +"#ifdef GL_FRAGMENT_PRECISION_HIGH\n"+ +"precision highp float;\n"+ +"#else\n"+ +"precision mediump float;\n"+ +"#endif\n"+ +"#endif\n"+ +"varying vec4 vCol; // carries alpha\n"+ +"varying vec4 vPosition;\n"+ +"#if defined(HAS_TEXTURE) || defined (IS_TEXT)\n"+ +"varying vec2 vTexcoord;\n"+ +"uniform sampler2D uSampler;\n"+ +"#endif\n"+ +"\n"+ +"#ifdef HAS_FOG\n"+ +"uniform int uFogMode;\n"+ +"uniform vec3 uFogColor;\n"+ +"uniform vec4 uFogParms;\n"+ +"#endif\n"+ +"\n"+ +"#if defined(IS_LIT) && !defined(FIXED_QUADS)\n"+ +"varying vec4 vNormal;\n"+ +"#endif\n"+ +"\n"+ +"#if NCLIPPLANES > 0\n"+ +"uniform vec4 vClipplane[NCLIPPLANES];\n"+ +"#endif\n"+ +"\n"+ +"#if NLIGHTS > 0\n"+ +"uniform mat4 mvMatrix;\n"+ +"#endif\n"+ +"\n"+ +"#ifdef IS_LIT\n"+ +"uniform vec3 emission;\n"+ +"uniform float shininess;\n"+ +"#if NLIGHTS > 0\n"+ +"uniform vec3 ambient[NLIGHTS];\n"+ +"uniform vec3 specular[NLIGHTS]; // light*material\n"+ +"uniform vec3 diffuse[NLIGHTS];\n"+ +"uniform vec3 lightDir[NLIGHTS];\n"+ +"uniform bool viewpoint[NLIGHTS];\n"+ +"uniform bool finite[NLIGHTS];\n"+ +"#endif\n"+ +"#endif // IS_LIT\n"+ +"\n"+ +"#ifdef IS_TWOSIDED\n"+ +"uniform bool front;\n"+ +"varying float normz;\n"+ +"#endif\n"+ +"\n"+ +"#ifdef FAT_LINES\n"+ +"varying vec2 vPoint;\n"+ +"varying float vLength;\n"+ +"#endif\n"+ +"\n"+ +"#ifdef USE_ENVMAP\n"+ +"varying vec3 vReflection;\n"+ +"#endif\n"+ +"\n"+ +"void main(void) {\n"+ +" vec4 fragColor;\n"+ +"#ifdef FAT_LINES\n"+ +" vec2 point = vPoint;\n"+ +" bool neg = point.y < 0.0;\n"+ +" point.y = neg ? (point.y + vLength)/(1.0 - vLength) :\n"+ +" -(point.y - vLength)/(1.0 - vLength);\n"+ +"#if defined(IS_TRANSPARENT) && defined(IS_LINESTRIP)\n"+ +" if (neg && length(point) <= 1.0) discard;\n"+ +"#endif\n"+ +" point.y = min(point.y, 0.0);\n"+ +" if (length(point) > 1.0) discard;\n"+ +"#endif // FAT_LINES\n"+ +" \n"+ +"#ifdef ROUND_POINTS\n"+ +" vec2 coord = gl_PointCoord - vec2(0.5);\n"+ +" if (length(coord) > 0.5) discard;\n"+ +"#endif\n"+ +" \n"+ +"#if NCLIPPLANES > 0\n"+ +" for (int i = 0; i < NCLIPPLANES; i++)\n"+ +" if (dot(vPosition, vClipplane[i]) < 0.0) discard;\n"+ +"#endif\n"+ +" \n"+ +"#ifdef FIXED_QUADS\n"+ +" vec3 n = vec3(0., 0., 1.);\n"+ +"#elif defined(IS_LIT)\n"+ +" vec3 n = normalize(vNormal.xyz);\n"+ +"#endif\n"+ +" \n"+ +"#ifdef IS_TWOSIDED\n"+ +" if ((normz <= 0.) != front) discard;\n"+ +"#endif\n"+ +"\n"+ +"#ifdef IS_LIT\n"+ +" vec3 eye = normalize(-vPosition.xyz/vPosition.w);\n"+ +" vec3 lightdir;\n"+ +" vec4 colDiff;\n"+ +" vec3 halfVec;\n"+ +" vec4 lighteffect = vec4(emission, 0.);\n"+ +" vec3 col;\n"+ +" float nDotL;\n"+ +"#ifdef FIXED_QUADS\n"+ +" n = -faceforward(n, n, eye);\n"+ +"#endif\n"+ +" \n"+ +"#if NLIGHTS > 0\n"+ +" for (int i=0;i 0) {\n"+ +" fogF = (uFogParms.y - vPosition.z/vPosition.w)/(uFogParms.y - uFogParms.x);\n"+ +" if (uFogMode > 1)\n"+ +" fogF = mix(uFogParms.w, 1.0, fogF);\n"+ +" fogF = fogF*uFogParms.z;\n"+ +" if (uFogMode == 2)\n"+ +" fogF = 1.0 - exp(-fogF);\n"+ +" // Docs are wrong: use (density*c)^2, not density*c^2\n"+ +" // https://gitlab.freedesktop.org/mesa/mesa/-/blob/master/src/mesa/swrast/s_fog.c#L58\n"+ +" else if (uFogMode == 3)\n"+ +" fogF = 1.0 - exp(-fogF*fogF);\n"+ +" fogF = clamp(fogF, 0.0, 1.0);\n"+ +" gl_FragColor = vec4(mix(fragColor.rgb, uFogColor, fogF), fragColor.a);\n"+ +" } else gl_FragColor = fragColor;\n"+ +"#else\n"+ +" gl_FragColor = fragColor;\n"+ +"#endif // HAS_FOG\n"+ +" \n"+ +"}\n" ;}; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/subscenes.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/subscenes.src.js new file mode 100644 index 00000000..fc6e05d0 --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/subscenes.src.js @@ -0,0 +1,179 @@ + /** + * Methods related to subscenes + * @name ___METHODS_FOR_SUBSCENES___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + /** + * Is a particular id in a subscene? + * @returns { boolean } + * @param {number} id Which id? + * @param {number} subscene Which subscene id? + */ + rglwidgetClass.prototype.inSubscene = function(id, subscene) { + return this.getObj(subscene).objects.indexOf(id) > -1; + }; + + /** + * Translate from window coordinates to viewport coordinates + * @returns { Object } translated coordinates + * @param { number } subsceneid - which subscene to use? + * @param { Object } coords - point to translate + */ + rglwidgetClass.prototype.translateCoords = function(subsceneid, coords) { + var viewport = this.getObj(subsceneid).par3d.viewport; + return {x: coords.x - viewport.x*this.canvas.width, + y: coords.y - viewport.y*this.canvas.height}; + }; + + /** + * Check whether point is in viewport of subscene + * @returns {boolean} + * @param { Object } coords - screen coordinates of point + * @param { number } subsceneid - subscene to check + */ + rglwidgetClass.prototype.inViewport = function(coords, subsceneid) { + var viewport = this.getObj(subsceneid).par3d.viewport, + x0 = coords.x - viewport.x*this.canvas.width, + y0 = coords.y - viewport.y*this.canvas.height; + return 0 <= x0 && x0 <= viewport.width*this.canvas.width && + 0 <= y0 && y0 <= viewport.height*this.canvas.height; + }; + + /** + * Find which subscene contains a point + * @returns { number } subscene id + * @param { Object } coords - coordinates of point + */ + rglwidgetClass.prototype.whichSubscene = function(coords) { + var self = this, + recurse = function(subsceneid) { + var subscenes = self.getChildSubscenes(subsceneid), i, id; + for (i=0; i < subscenes.length; i++) { + id = recurse(subscenes[i]); + if (typeof(id) !== "undefined") + return(id); + } + if (self.inViewport(coords, subsceneid)) + return(subsceneid); + else + return undefined; + }, + rootid = this.scene.rootSubscene, + result = recurse(rootid); + if (typeof(result) === "undefined") + result = rootid; + return result; + }; + + /** + * Add an id to a subscene. + * @param {number} id Which id? + * @param {number} subscene Which subscene id? + */ + rglwidgetClass.prototype.addToSubscene = function(id, subscene) { + var thelist, + thesub = this.getObj(subscene), + ids = [id], + obj = this.getObj(id), i; + if (typeof obj !== "undefined" && typeof (obj.newIds) !== "undefined") { + ids = ids.concat(obj.newIds); + } + thesub.objects = [].concat(thesub.objects); + for (i = 0; i < ids.length; i++) { + id = ids[i]; + if (thesub.objects.indexOf(id) === -1) { + thelist = this.whichList(id); + thesub.objects.push(id); + thesub[thelist].push(id); + } + } + }; + + /** + * Delete an id from a subscene + * @param { number } id - the id to add + * @param { number } subscene - the id of the subscene + */ + rglwidgetClass.prototype.delFromSubscene = function(id, subscene) { + var thelist, + thesub = this.getObj(subscene), + obj = this.getObj(id), + ids = [id], i, j; + if (typeof obj !== "undefined" && typeof (obj.newIds) !== "undefined") + ids = ids.concat(obj.newIds); + thesub.objects = [].concat(thesub.objects); // It might be a scalar + for (j=0; j -1) { + thesub.objects.splice(i, 1); + thelist = this.whichList(id); + i = thesub[thelist].indexOf(id); + thesub[thelist].splice(i, 1); + } + } + }; + + /** + * Set the ids in a subscene + * @param { number[] } ids - the ids to set + * @param { number } subsceneid - the id of the subscene + */ + rglwidgetClass.prototype.setSubsceneEntries = function(ids, subsceneid) { + var sub = this.getObj(subsceneid); + sub.objects = ids; + this.initSubscene(subsceneid); + }; + + /** + * Get the ids in a subscene + * @returns {number[]} + * @param { number } subscene - the id of the subscene + */ + rglwidgetClass.prototype.getSubsceneEntries = function(subscene) { + return this.getObj(subscene).objects; + }; + + /** + * Get the ids of the subscenes within a subscene + * @returns { number[] } + * @param { number } subscene - the id of the subscene + */ + rglwidgetClass.prototype.getChildSubscenes = function(subscene) { + return this.getObj(subscene).subscenes; + }; + + /** + * Find a particular subscene by inheritance + * @returns { number } id of subscene to use + * @param { number } subsceneid - child subscene + * @param { string } type - type of inheritance: "projection" or "model" + */ + rglwidgetClass.prototype.useid = function(subsceneid, type) { + var sub = this.getObj(subsceneid); + if (sub.embeddings[type] === "inherit") + return(this.useid(sub.parent, type)); + else + return subsceneid; + }; + + /** + * Find bboxdeco for a subscene + * @returns { number } id of bboxdeco, or undefined if none + * @param { number } sub- subscene + */ + rglwidgetClass.prototype.getBBoxDeco = function(sub) { + var objects = sub.objects, i, obj; + for (i = 0; i < objects.length; i++) { + obj = this.getObj(objects[i]); + if (obj.type === "bboxdeco") + return obj; + } + if (sub.parent) + return this.getBBoxDeco(this.getObj(sub.parent)); + else + return undefined; + }; diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/textures.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/textures.src.js new file mode 100644 index 00000000..3cf32d36 --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/textures.src.js @@ -0,0 +1,173 @@ + /** + * Methods related to textures + * @name ___METHODS_FOR_TEXTURES___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + rglwidgetClass.prototype.getTexFilter = function(filter) { + var gl = this.gl || this.initGL(); + switch(filter) { + case "nearest": return gl.NEAREST; + case "linear": return gl.LINEAR; + case "nearest.mipmap.nearest": return gl.NEAREST_MIPMAP_NEAREST; + case "linear.mipmap.nearest": return gl.LINEAR_MIPMAP_NEAREST; + case "nearest.mipmap.linear": return gl.NEAREST_MIPMAP_LINEAR; + case "linear.mipmap.linear": return gl.LINEAR_MIPMAP_LINEAR; + default: console.error("Unknown filter: "+filter); + } + }; + + /** + * Handle a texture after its image has been loaded + * @param { Object } texture - the gl texture object + * @param { Object } textureCanvas - the canvas holding the image + */ + rglwidgetClass.prototype.handleLoadedTexture = function(texture, textureCanvas) { + var gl = this.gl || this.initGL(); + gl.pixelStorei(gl.UNPACK_FLIP_Y_WEBGL, true); + + gl.bindTexture(gl.TEXTURE_2D, texture); + gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, textureCanvas); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); + gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR_MIPMAP_NEAREST); + gl.generateMipmap(gl.TEXTURE_2D); + + gl.bindTexture(gl.TEXTURE_2D, null); + }; + + /** + * Get maximum dimension of texture in current browser. + * @returns {number} + */ + rglwidgetClass.prototype.getMaxTexSize = function() { + var gl = this.gl || this.initGL(); + return Math.min(4096, gl.getParameter(gl.MAX_TEXTURE_SIZE)); + }; + + /** + * Load an image to a texture + * @param { string } uri - The image location + * @param { Object } texture - the gl texture object + */ + rglwidgetClass.prototype.loadImageToTexture = function(uri, texture) { + var canvas = this.textureCanvas, + ctx = canvas.getContext("2d"), + image = new Image(), + self = this; + + image.onload = function() { + + var w = image.width, + h = image.height, + canvasX = self.getPowerOfTwo(w), + canvasY = self.getPowerOfTwo(h), + maxTexSize = self.getMaxTexSize(); + while (canvasX > 1 && canvasY > 1 && (canvasX > maxTexSize || canvasY > maxTexSize)) { + canvasX /= 2; + canvasY /= 2; + } + canvas.width = canvasX; + canvas.height = canvasY; + ctx.imageSmoothingEnabled = true; + ctx.drawImage(image, 0, 0, canvasX, canvasY); + self.handleLoadedTexture(texture, canvas); + self.texturesLoading -= 1; + if (!self.texturesLoading) + self.drawScene(); + }; + if (!self.texturesLoading) + self.texturesLoading = 0; // may have been undefined + self.texturesLoading += 1; + image.src = uri; + }; + + /** + * Draw text to the texture canvas + * @returns { Object } object with text measurements + * @param { string } text - the text + * @param { number } cex - expansion + * @param { string } family - font family + * @param { number } font - font number + */ + rglwidgetClass.prototype.drawTextToCanvas = function(text, cex, family, font) { + var canvasX, canvasY, + scaling = 20, + textColour = "white", + + backgroundColour = "rgba(0,0,0,0)", + canvas = this.textureCanvas, + ctx = canvas.getContext("2d"), + i, textHeight = 0, textHeights = [], width, widths = [], + offsetx, offsety = 0, line, lines = [], offsetsx = [], + offsetsy = [], lineoffsetsy = [], fontStrings = [], + maxTexSize = this.getMaxTexSize(), + getFontString = function(i) { + textHeights[i] = scaling*cex[i]; + var fontString = textHeights[i] + "px", + family0 = family[i], + font0 = font[i]; + if (family0 === "sans") + family0 = "sans-serif"; + else if (family0 === "mono") + family0 = "monospace"; + fontString = fontString + " " + family0; + if (font0 === 2 || font0 === 4) + fontString = "bold " + fontString; + if (font0 === 3 || font0 === 4) + fontString = "italic " + fontString; + return fontString; + }; + cex = rglwidgetClass.repeatToLen(cex, text.length); + family = rglwidgetClass.repeatToLen(family, text.length); + font = rglwidgetClass.repeatToLen(font, text.length); + + canvasX = 1; + line = -1; + offsetx = maxTexSize; + for (i = 0; i < text.length; i++) { + ctx.font = fontStrings[i] = getFontString(i); + width = widths[i] = ctx.measureText(text[i]).width; + if (offsetx + width > maxTexSize) { + offsety = offsety + 2*textHeight; + if (line >= 0) + lineoffsetsy[line] = offsety; + line += 1; + if (offsety > maxTexSize) + console.error("Too many strings for texture."); + textHeight = 0; + offsetx = 0; + } + textHeight = Math.max(textHeight, textHeights[i]); + offsetsx[i] = offsetx; + offsetx += width; + canvasX = Math.max(canvasX, offsetx); + lines[i] = line; + } + offsety = lineoffsetsy[line] = offsety + 2*textHeight; + for (i = 0; i < text.length; i++) { + offsetsy[i] = lineoffsetsy[lines[i]]; + } + + canvasX = this.getPowerOfTwo(canvasX); + canvasY = this.getPowerOfTwo(offsety); + + canvas.width = canvasX; + canvas.height = canvasY; + + ctx.fillStyle = backgroundColour; + ctx.fillRect(0, 0, ctx.canvas.width, ctx.canvas.height); + + ctx.textBaseline = "alphabetic"; + for(i = 0; i < text.length; i++) { + ctx.font = fontStrings[i]; + ctx.fillStyle = textColour; + ctx.textAlign = "left"; + ctx.fillText(text[i], offsetsx[i], offsetsy[i]); + } + return {canvasX:canvasX, canvasY:canvasY, + widths:widths, textHeights:textHeights, + offsetsx:offsetsx, offsetsy:offsetsy}; + }; + diff --git a/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/utils.src.js b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/utils.src.js new file mode 100644 index 00000000..00bc5fc7 --- /dev/null +++ b/inst/gitbook/_book/libs/rglwidgetClass-1.2.1/utils.src.js @@ -0,0 +1,654 @@ + /** + * Utility methods + * @name ___UTILITY_METHODS___ + * @memberof rglwidgetClass + * @kind function + * @instance + */ + + /** + * Multiply matrix by vector + * @returns {number[]} + * @param M {number[][]} Left operand + * @param v {number[]} Right operand + */ + rglwidgetClass.multMV = function(M, v) { + return [ M.m11 * v[0] + M.m12 * v[1] + M.m13 * v[2] + M.m14 * v[3], + M.m21 * v[0] + M.m22 * v[1] + M.m23 * v[2] + M.m24 * v[3], + M.m31 * v[0] + M.m32 * v[1] + M.m33 * v[2] + M.m34 * v[3], + M.m41 * v[0] + M.m42 * v[1] + M.m43 * v[2] + M.m44 * v[3] + ]; + }; + + /** + * Multiply row vector by Matrix + * @returns {number[]} + * @param v {number[]} left operand + * @param M {number[][]} right operand + */ + rglwidgetClass.multVM = function(v, M) { + return [ M.m11 * v[0] + M.m21 * v[1] + M.m31 * v[2] + M.m41 * v[3], + M.m12 * v[0] + M.m22 * v[1] + M.m32 * v[2] + M.m42 * v[3], + M.m13 * v[0] + M.m23 * v[1] + M.m33 * v[2] + M.m43 * v[3], + M.m14 * v[0] + M.m24 * v[1] + M.m34 * v[2] + M.m44 * v[3] + ]; + }; + + /** + * Euclidean length of a vector + * @returns {number} + * @param v {number[]} + */ + rglwidgetClass.vlen = function(v) { + return Math.sqrt(rglwidgetClass.dotprod(v, v)); + }; + + /** + * Dot product of two vectors + * @instance rglwidgetClass + * @returns {number} + * @param a {number[]} + * @param b {number[]} + */ + rglwidgetClass.dotprod = function(a, b) { + return a[0]*b[0] + a[1]*b[1] + a[2]*b[2]; + }; + + /** + * Cross product of two vectors + * @returns {number[]} + * @param a {number[]} + * @param b {number[]} + */ + rglwidgetClass.xprod = function(a, b) { + return [a[1]*b[2] - a[2]*b[1], + a[2]*b[0] - a[0]*b[2], + a[0]*b[1] - a[1]*b[0]]; + }; + + /** + * Bind vectors or matrices by columns + * @returns {number[][]} + * @param a {number[][]} + * @param b {number[]|number[][]} + */ + rglwidgetClass.cbind = function(a, b) { + if (b.length < a.length) + b = rglwidgetClass.repeatToLen(b, a.length); + else if (a.length < b.length) + a = rglwidgetClass.repeatToLen(a, b.length); + return a.map(function(currentValue, index) { + return [].concat(currentValue).concat(b[index]); + }); + }; + + /** + * Swap elements + * @returns {any[]} + * @param a {any[]} + * @param i {number} Element to swap + * @param j {number} Other element to swap + */ + rglwidgetClass.swap = function(a, i, j) { + var temp = a[i]; + a[i] = a[j]; + a[j] = temp; + }; + + /** + * Flatten a matrix into a vector + * @returns {any[]} + * @param a {any[][]} + */ + rglwidgetClass.flatten = function(arr, result) { + var value; + if (typeof result === "undefined") result = []; + for (var i = 0, length = arr.length; i < length; i++) { + value = arr[i]; + if (Array.isArray(value)) { + rglwidgetClass.flatten(value, result); + } else { + result.push(value); + } + } + return result; + }; + + /** + * set element of 1d or 2d array as if it was flattened. + * Column major, zero based! + * @returns {any[]|any[][]} + * @param {any[]|any[][]} a - array + * @param {number} i - element + * @param {any} value + */ + rglwidgetClass.prototype.setElement = function(a, i, value) { + if (Array.isArray(a[0])) { + var dim = a.length, + col = Math.floor(i/dim), + row = i % dim; + a[row][col] = value; + } else { + a[i] = value; + } + }; + + /** + * Transpose an array + * @returns {any[][]} + * @param {any[][]} a + */ + rglwidgetClass.prototype.transpose = function(a) { + var newArray = [], + n = a.length, + m = a[0].length, + i; + for(i = 0; i < m; i++){ + newArray.push([]); + } + + for(i = 0; i < n; i++){ + for(var j = 0; j < m; j++){ + newArray[j].push(a[i][j]); + } + } + return newArray; + }; + + /** + * Calculate sum of squares of a numeric vector + * @returns {number} + * @param {number[]} x + */ + rglwidgetClass.prototype.sumsq = function(x) { + var result = 0, i; + for (i=0; i < x.length; i++) + result += x[i]*x[i]; + return result; + }; + + /** + * Convert a matrix to a CanvasMatrix4 + * @returns {CanvasMatrix4} + * @param {number[][]|number[]} mat + */ + rglwidgetClass.prototype.toCanvasMatrix4 = function(mat) { + if (mat instanceof CanvasMatrix4) + return mat; + var result = new CanvasMatrix4(); + mat = rglwidgetClass.flatten(this.transpose(mat)); + result.load(mat); + return result; + }; + + /** + * Convert an R-style numeric colour string to an rgb vector + * @returns {number[]} + * @param {string} s + */ + /* jshint bitwise:false */ + rglwidgetClass.prototype.stringToRgb = function(s) { + s = s.replace("#", ""); + var bigint = parseInt(s, 16); + return [((bigint >> 16) & 255)/255, + ((bigint >> 8) & 255)/255, + (bigint & 255)/255]; + }; + /* jshint bitwise:true */ + /** + * Which list does a particular id come from? + * @returns { string } + * @param {number} id The id to look up. + */ + rglwidgetClass.prototype.whichList = function(id) { + var obj = this.getObj(id), + flags = obj.flags; + if (obj.type === "light") + return "lights"; + if (rglwidgetClass.isSet(flags, rglwidgetClass.f_is_subscene)) + return "subscenes"; + if (rglwidgetClass.isSet(flags, rglwidgetClass.f_is_clipplanes)) + return "clipplanes"; + if (rglwidgetClass.isSet(flags, rglwidgetClass.f_is_transparent)) + return "transparent"; + return "opaque"; + }; + + /** + * Take a component-by-component product of two 3 vectors + * @returns {number[]} + * @param {number[]} x + * @param {number[]} y + */ + rglwidgetClass.prototype.componentProduct = function(x, y) { + if (typeof y === "undefined") { + this.alertOnce("Bad arg to componentProduct"); + } + var result = new Float32Array(3), i; + for (i = 0; i<3; i++) + result[i] = x[i]*y[i]; + return result; + }; + + /** + * Get next higher power of two + * @returns { number } + * @param { number } value - input value + */ + rglwidgetClass.prototype.getPowerOfTwo = function(value) { + var pow = 1; + while(pow= -windHeight && + rect.left >= -windWidth && + rect.bottom <= 2*windHeight && + rect.right <= 2*windWidth); + }; + + rglwidgetClass.keydiff = function(obj1, obj2) { + var keys = Object.keys(obj1), i, result = []; + for (i=0;i= 2 && cross(lower[lower.length - 2], lower[lower.length - 1], points[i]) <= 0) { + lower.pop(); + } + lower.push(points[i]); + } + + for (i = points.length - 1; i >= 0; i--) { + while (upper.length >= 2 && cross(upper[upper.length - 2], upper[upper.length - 1], points[i]) <= 0) { + upper.pop(); + } + upper.push(points[i]); + } + + upper.pop(); + lower.pop(); + return lower.concat(upper); + }; + + /** + * Round number to given precision + * @param { number } x + * @param { number } digits + * @returns { number } + */ + rglwidgetClass.signif = function(x, digits) { + return parseFloat(x.toPrecision(digits)); + }; + + /** + * Check for NA, NaN, undefined, or null + * @param x + * @returns { bool } + */ + rglwidgetClass.missing = function(x) { + return x !== "-Inf" && x !== "Inf" && + (isNaN(x) || x === null || typeof(x) === "undefined"); + }; + + /** + * Write matrix to log + * @param M + */ + rglwidgetClass.logMatrix = function(M) { + console.log("matrix(c("+M.m11+","+M.m12+","+M.m13+","+M.m14+",\n"+ + M.m21+","+M.m22+","+M.m23+","+M.m24+",\n"+ + M.m31+","+M.m32+","+M.m33+","+M.m34+",\n"+ + M.m41+","+M.m42+","+M.m43+","+M.m44+"), byrow=TRUE, ncol=4)"); + }; + + /** + * Write vector to log + * @param {vector} v + */ + + rglwidgetClass.logVec3 = function(v) { + console.log("c("+v[0]+","+v[1]+","+v[2]+")"); + }; + + /** + * Sum two vectors + * @param {vector} x + * @param {vector} y + */ + rglwidgetClass.vsum = function(x, y) { + var i, result = [].concat(x); + for (i = 0; i < y.length; i++) + result[i] += y[i]; + return result; + }; + + /** + * difference of two vectors + * @param {vector} x + * @param {vector} y + */ + rglwidgetClass.vdiff = function(x, y) { + return rglwidgetClass.vsum(x, rglwidgetClass.vscale(y, -1)); + }; + + /** + * Scale a vector + * @param {number} s + * @param {vector} x + */ + rglwidgetClass.vscale = function(x, s) { + var i, result = [].concat(x); + for (i = 0; i < x.length; i++) + result[i] *= s; + return result; + }; + + /** + * Normalize a vector + * @param {vector} v + */ + rglwidgetClass.normalize = function(v) { + return rglwidgetClass.vscale(v, 1/rglwidgetClass.vlen(v)); + }; + + /** + * Compute the dimensions of a regular array + * without checking that it is regular + */ + rglwidgetClass.arrayDim = function(arr) { + var result = []; + while (typeof arr.length !== "undefined") { + result = result.concat(arr.length); + arr = arr[0]; + } + return result; + }; diff --git a/inst/gitbook/_book/making-stuff-up.html b/inst/gitbook/_book/making-stuff-up.html index f8210f37..42cb6fd9 100644 --- a/inst/gitbook/_book/making-stuff-up.html +++ b/inst/gitbook/_book/making-stuff-up.html @@ -4,26 +4,26 @@ - 5 Making stuff up! | Morphometric geometric demo: a between group analysis - - + 5 Making stuff up! | dispRity R package manual + + - + - + - + - + - + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -350,17 +383,17 @@

    5.1 Simulating discrete morpholog ## The first few lines of the matrix my_matrix[1:5, 1:10]

    ##     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
-## t10 "1"  "0"  "1"  "1"  "1"  "0"  "0"  "1"  "0"  "1"  
-## t1  "0"  "0"  "1"  "1"  "0"  "0"  "0"  "1"  "0"  "1"  
-## t9  "0"  "0"  "1"  "1"  "0"  "0"  "0"  "1"  "0"  "1"  
-## t14 "1"  "0"  "1"  "1"  "0"  "0"  "0"  "1"  "0"  "1"  
-## t13 "1"  "0"  "1"  "1"  "0"  "0"  "0"  "1"  "0"  "1"
    +## t10 "1" "0" "1" "0" "1" "0" "0" "1" "0" "0" +## t1 "0" "0" "1" "0" "0" "0" "0" "1" "0" "0" +## t9 "0" "0" "1" "0" "0" "0" "0" "1" "0" "0" +## t14 "1" "0" "1" "0" "0" "0" "0" "1" "0" "0" +## t13 "1" "0" "1" "0" "0" "0" "0" "1" "0" "0" 
    ## Checking the matrix properties with a quick Maximum Parsimony tree search
 check.morpho(my_matrix, my_tree)
    ##                                     
-## Maximum parsimony        143.0000000
-## Consistency index          0.7482517
-## Retention index            0.9168591
+## Maximum parsimony        144.0000000
+## Consistency index          0.7430556
+## Retention index            0.9160998
 ## Robinson-Foulds distance   2.0000000

    Note that this example produces a tree with a great consistency index and an identical topology to the random coalescent tree! Nearly too good to be true…

    @@ -425,16 +458,16 @@

    5.1.1.3 Adding inapplicable chara ## The first few lines of the resulting matrix my_matrix_NA[1:10, 90:100]

    ##     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
-## t10 "1"  "0"  "1"  "1"  "1"  "0"  "0"  "0"  "1"  "0"   "1"  
-## t1  "1"  "0"  "0"  "0"  "1"  "0"  "0"  "0"  "0"  "0"   "1"  
-## t9  "1"  "2"  "1"  "0"  "1"  "0"  "0"  "0"  "0"  "0"   "1"  
-## t14 "1"  "0"  "0"  "0"  "1"  "0"  "0"  "0"  "0"  "0"   "1"  
-## t13 "1"  "0"  "0"  "0"  "1"  "0"  "0"  "0"  "0"  "0"   "1"  
-## t5  "1"  "0"  "0"  "0"  "1"  "0"  "0"  "0"  "0"  "0"   "1"  
-## t2  "1"  "2"  "-"  "0"  "1"  "0"  "0"  "0"  "0"  "0"   "1"  
-## t8  "1"  "1"  "-"  "0"  "1"  "0"  "0"  "0"  "0"  "0"   "1"  
-## t6  "1"  "2"  "1"  "0"  "0"  "1"  "1"  "2"  "0"  "1"   "0"  
-## t15 "1"  "2"  "1"  "0"  "0"  "1"  "1"  "2"  "0"  "1"   "0"
    +## t10 "-" "1" "1" "2" "1" "0" "0" "0" "1" "0" "0" +## t1 "-" "1" "0" "0" "1" "0" "0" "0" "-" "0" "0" +## t9 "-" "1" "1" "0" "1" "0" "0" "0" "-" "0" "0" +## t14 "-" "1" "0" "0" "1" "0" "0" "0" "-" "0" "0" +## t13 "-" "1" "0" "0" "1" "0" "0" "0" "-" "0" "0" +## t5 "-" "1" "0" "0" "1" "0" "0" "0" "-" "0" "0" +## t2 "1" "1" "0" "0" "1" "0" "0" "0" "0" "0" "0" +## t8 "2" "1" "0" "0" "1" "0" "0" "0" "0" "0" "0" +## t6 "-" "1" "1" "0" "0" "1" "1" "2" "0" "1" "1" +## t15 "-" "1" "1" "0" "0" "1" "1" "2" "0" "1" "1"

    @@ -491,7 +524,7 @@

    5.2 Simulating multidimensional s 
    ## Visualising the space
 plot(square_space, pch = 20, xlab = "", ylab = "",
      main = "Uniform 2D space")
    -

    +

    Of course, more complex spaces can be created by changing the distributions, their arguments or adding a correlation matrix or a cumulative variance vector:

    ## A plane space: uniform with one dimensions equal to 0
 plane_space <- space.maker(2500, 3, c(runif, runif, runif),
@@ -526,7 +559,7 @@ 
    5.2.1 Personalised dimensions dis
 
 ## Plotting the circle
 plot(circle, xlab = "x", ylab = "y", main = "A normal circle")
    -

    +

    ## Creating doughnut space (a spherical space with a hole)
 doughnut_space <- space.maker(5000, 3, c(rnorm, random.circle),
      arguments = list(list(mean = 0),
@@ -556,7 +589,7 @@ 
    5.2.2 Visualising the space## Plotting the doughnut space
 scatterplot3d(doughnut_space[,c(2,1,3)], pch = 20, xlab = "", ylab = "",
               zlab = "", main = "Doughnut space")
    -

    +

    par(op)

    @@ -594,7 +627,7 @@

    5.2.3 Generating realistic spaces points(BeckLee_mat50[,1:2], col = "red", pch = 20) legend("topleft", legend = c("observed", "simulated"), pch = c(20,21), col = c("red", "black"))

    -

    +

    It is now possible to simulate a space using these observed arguments to test several hypothesis:

    • Is the space uniform or normal?
      • @@ -626,13 +659,13 @@

      5.2.3 Generating realistic spaces plot(test_norm2, main = paste0("Normal (", round(obs_mu_sd_glob[[1]], digit = 3), ",", round(obs_mu_sd_glob[[2]], digit = 3), ")")) plot(test_norm3, main = "Normal (variable + correlation)")

    -

    +

    If we measure disparity as the median distance from the morphospace centroid, we can explain the distribution of the data as normal with the variable observed mean and standard deviation and with a correlation between the dimensions.

    -

    References

    +

    References

    Brazeau, Martin D, Thomas Guillerme, and Martin R Smith. 2018. “An algorithm for Morphological Phylogenetic Analysis with Inapplicable Data.” Systematic Biology 68 (4): 619–31. https://doi.org/10.1093/sysbio/syy083.

    @@ -728,7 +761,7 @@

    References - 10 Morphometric geometric demo: a between group analysis | Morphometric geometric demo: a between group analysis - - + 10 Morphometric geometric demo: a between group analysis | dispRity R package manual + + - + - + - + - + - + @@ -31,7 +31,7 @@ - + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -333,14 +366,14 @@

    10 Morphometric geometric demo: a

    10.1 Before starting

    Here we are going to use the geomorph plethodon dataset that is a set of 12 2D landmark coordinates for 40 specimens from two species of salamanders. This section will really quickly cover how to make a Procrustes sumperimposition analysis and create a geomorph data.frame to have data ready for the dispRity package.

    -
    ## Loading geomorph
-library(geomorph)
-
-## Loading the plethodon dataset
-data(plethodon)
-
-## Running a simple Procrustes superimposition
-gpa_plethodon <- gpagen(plethodon$land)
    +
    ## Loading geomorph
+library(geomorph)
+
+## Loading the plethodon dataset
+data(plethodon)
+
+## Running a simple Procrustes superimposition
+gpa_plethodon <- gpagen(plethodon$land)
    ## 
 ## Performing GPA
 ## 
@@ -354,13 +387,13 @@ 
    10.1 Before starting
    ## Making a geomorph data frame object with the species and sites attributes
-gdf_plethodon <- geomorph.data.frame(gpa_plethodon,
-                                     species = plethodon$species,
-                                     site = plethodon$site)
    +
    ## Making a geomorph data frame object with the species and sites attributes
+gdf_plethodon <- geomorph.data.frame(gpa_plethodon,
+                                     species = plethodon$species,
+                                     site = plethodon$site)

    You can of course use your very own landmark coordinates dataset (though you will have to do some modifications in the scripts that will come below - they will be easy though!).

    -
    ## You can replace the gdf_plethodon by your own geomorph data frame!
-my_geomorph_data <- gdf_plethodon
    +
    ## You can replace the gdf_plethodon by your own geomorph data frame!
+my_geomorph_data <- gdf_plethodon

    10.1.1 The morphospace

    The first step of every disparity analysis is to define your morphospace.

    @@ -371,18 +404,18 @@

    10.1.1 The morphospace
    ## The morphospace
-morphospace <- geomorph.ordination(gdf_plethodon)

    +
    ## The morphospace
+morphospace <- geomorph.ordination(gdf_plethodon)

    This automatically generates a dispRity object with the information of each groups. You can find more information about dispRity objects here but basically it summarises the content of your object without spamming your R console and is associated with many utility functions like summary or plot. For example here you can quickly visualise the two first dimensions of your space using the plot function:

    -
    ## The dispRity object
-morphospace
    +
    ## The dispRity object
+morphospace
    ##  ---- dispRity object ---- 
 ## 4 customised subsets for 40 elements in one matrix:
 ##     species.Jord, species.Teyah, site.Allo, site.Symp.
    -
    ## Plotting the morphospace
-plot(morphospace)
    -

    -
    ## Note that this only displays the two last groups (site.Allo and site.Symp) since they overlap!
    +
    ## Plotting the morphospace
+plot(morphospace)
    +

    +
    ## Note that this only displays the two last groups (site.Allo and site.Symp) since they overlap!

    The dispRity package function comes with a lot of documentation of examples so don’t hesitate to type plot.dispRity to check more plotting options.

    @@ -391,48 +424,48 @@

    10.2 Calculating disparityNow that we have our morphospace, we can think about what we want to measure. Two aspects of disparity that would be interesting for our question (is there a difference in disparity between the different species of salamanders and between the different sites?) would be the differences in size in the morphospace (do both groups occupy the same amount of morphospace) and position in the morphospace (do the do groups occupy the same position in the morphospace?).

    To choose which metric would cover best these two aspects, please check the Thomas Guillerme, Puttick, et al. (2020) paper and associated app. Here we are going to use the procrustes variance (geomorph::morphol.disparity) for measuring the size of the trait space and the average displacements (Thomas Guillerme, Puttick, et al. 2020) for the position in the trait space.

    -
    ## Defining a the procrustes variance metric
-## (as in geomorph::morphol.disparity)
-proc.var <- function(matrix) {sum(matrix^2)/nrow(matrix)}
    -
    ## The size metric
-test_size <- test.metric(morphospace, metric = proc.var,
-                         shifts = c("random", "size"))
-plot(test_size)
-summary(test_size)
-
-## The position metric
-test_position <- test.metric(morphospace, metric = c(mean, displacements),
-                         shifts = c("random", "position"))
-plot(test_position)
-summary(test_position)
    +
    ## Defining a the procrustes variance metric
+## (as in geomorph::morphol.disparity)
+proc.var <- function(matrix) {sum(matrix^2)/nrow(matrix)}
    +
    ## The size metric
+test_size <- test.metric(morphospace, metric = proc.var,
+                         shifts = c("random", "size"))
+plot(test_size)
+summary(test_size)
+
+## The position metric
+test_position <- test.metric(morphospace, metric = c(mean, displacements),
+                         shifts = c("random", "position"))
+plot(test_position)
+summary(test_position)

    You can see here for more details on the test.metric function but basically these graphs are showing that there is a relation between changes in size and in position for each metric. Note that there are some caveats here but the selection of the metric is just for the sake of the example!

    Note also the format of defining the disparity metrics here using metric = c(mean, displacements) or metric = proc.var. This is a core bit of the dispRity package were you can define your own metric as a function or a set of functions. You can find more info about this in the dispRity metric section but in brief, the dispRity package considers metrics by their “dimensions” level which corresponds to what they output. For example, the function mean is a dimension level 1 function because no matter the input it outputs a single value (the mean), displacements on the other hand is a dimension level 2 function because it will output the ratio between the distance from the centroid and from the centre of the trait space for each row in a matrix (an example of a dimensions level 3 would be the function var that outputs a matrix). The dispRity package always automatically sorts the dimensions levels: it will always run dimensions level 3 > dimensions level 2 > and dimensions level 1. In this case both c(mean, displacements) and c(mean, displacements) will result in actually running mean(displacements(matrix)). Alternatively you can define your metric prior to the disparity analysis like we did for the proc.var function.

    Anyways, we can measure disparity using these two metrics on all the groups as follows:

    -
    ## Bootstrapped disparity
-disparity_size <-  dispRity(boot.matrix(morphospace), metric = proc.var)
-disparity_position <- dispRity(boot.matrix(morphospace), metric = c(mean, displacements))
    +
    ## Bootstrapped disparity
+disparity_size <-  dispRity(boot.matrix(morphospace), metric = proc.var)
+disparity_position <- dispRity(boot.matrix(morphospace), metric = c(mean, displacements))

    Note that here we use the boot.matrix function for quickly bootstrapping the matrix. This is not an essential step in this kind of analysis but it allows to “reduce” the effect of outliers and create a distribution of disparity measures (rather than single point estimates).

    10.3 Analyse the results

    We can visualise the results using the plot function on the resulting disparity objects (or summarising them using summary):

    -
    ## Plotting the results
-par(mfrow = c(1,2))
-plot(disparity_size, main = "group sizes", las = 2, xlab = "")
-plot(disparity_position, main = "group positions", las = 2, xlab = "")
    -

    -
    ## Summarising the results
-summary(disparity_size)
    +
    ## Plotting the results
+par(mfrow = c(1,2))
+plot(disparity_size, main = "group sizes", las = 2, xlab = "")
+plot(disparity_position, main = "group positions", las = 2, xlab = "")
    +

    +
    ## Summarising the results
+summary(disparity_size)
    ##         subsets  n   obs bs.median  2.5%   25%   75% 97.5%
 ## 1  species.Jord 20 0.005     0.005 0.004 0.005 0.005 0.006
 ## 2 species.Teyah 20 0.005     0.005 0.004 0.005 0.005 0.006
 ## 3     site.Allo 20 0.004     0.004 0.003 0.003 0.004 0.004
 ## 4     site.Symp 20 0.006     0.006 0.006 0.006 0.006 0.007
    -
    summary(disparity_position)
    +
    summary(disparity_position)
    ##         subsets  n   obs bs.median  2.5%   25%   75% 97.5%
 ## 1  species.Jord 20 1.096     1.122 1.067 1.101 1.171 1.380
 ## 2 species.Teyah 20 1.070     1.105 1.033 1.065 1.143 1.345
@@ -440,8 +473,8 @@ 
    10.3 Analyse the results
    ## Testing the differences
-test.dispRity(disparity_size, test = wilcox.test, correction = "bonferroni")
    +
    ## Testing the differences
+test.dispRity(disparity_size, test = wilcox.test, correction = "bonferroni")
    ## [[1]]
 ##                              statistic: W
 ## species.Jord : species.Teyah         3803
@@ -459,7 +492,7 @@ 
    10.3 Analyse the results
    test.dispRity(disparity_position, test = wilcox.test, correction = "bonferroni")

    +
    test.dispRity(disparity_position, test = wilcox.test, correction = "bonferroni")
    ## [[1]]
 ##                              statistic: W
 ## species.Jord : species.Teyah         6536
@@ -481,7 +514,7 @@ 
    10.3 Analyse the results
    
+
     References
    
 
    
 
    
 
    Guillerme, Thomas, Mark N Puttick, Ariel E Marcy, and Vera Weisbecker. 2020. “Shifting Spaces: Which Disparity or Dissimilarity Measurement Best Summarize Occupancy in Multidimensional Spaces?” Ecology and Evolution.
    
@@ -493,7 +526,7 @@ 
    References
       
    
 
-
+
    @@ -553,7 +586,7 @@

    References - 6 Other functionalities | Morphometric geometric demo: a between group analysis - - + 6 Other functionalities | dispRity R package manual + + - + - + - + - + - + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -358,7 +391,7 @@

    6.1 char.diff 
    ## Visualising the matrix
 plot(differences)
    -

    +

    You can check all the numerous plotting options in the ?plot.char.diff manual (it won’t be developed here).

    The char.diff function has much more options however (see all of them in the ?char.diff manual) for example to measure different differences (via method) or making the comparison work per row (for a distance matrix between the rows):

    ## Euclidean distance between rows
@@ -555,7 +588,7 @@ 
    6.5 match.tip.edge
 ## Plotting the results
 plot(ladderize(my_tree), show.tip.label = FALSE, edge.color = clade_edges)
    -

    +

    But you can also use this option to only select some specific edges and modify them (for example making them all equal to one):

    ## Adding a fixed edge length to the green clade
 my_tree_modif <- my_tree
@@ -563,13 +596,13 @@ 
    6.5 match.tip.edgemy_tree_modif$edge.length[green_clade] <- 1
 plot(ladderize(my_tree_modif), show.tip.label = FALSE,
      edge.color = clade_edges)
    -

    +

    6.6 MCMCglmm utilities

    Since version 1.7, the dispRity package contains several utility functions for manipulating "MCMCglmm" (that is, objects returned by the function MCMCglmm::MCMCglmm). These objects are a modification of the mcmc object (from the package coda) and can be sometimes cumbersome to manipulate because of the huge amount of data in it. -You can use the functions MCMCglmm.traits for extracting the number of traits, MCMCglmm.levels for extracting the level names, MCMCglmm.sample for sampling posterior IDs and MCMCglmm.covars for extracting variance-covariance matrices.

    +You can use the functions MCMCglmm.traits for extracting the number of traits, MCMCglmm.levels for extracting the level names, MCMCglmm.sample for sampling posterior IDs and MCMCglmm.covars for extracting variance-covariance matrices. You can also quickly calculate the variance (or relative variance) for each terms in the model using MCMCglmm.variance (the variance is calculated as the sum of the diagonal of each variance-covariance matrix for each term).

    ## Loading the charadriiformes data that contains a MCMCglmm object
 data(charadriiformes)
 my_MCMCglmm <- charadriiformes$posteriors
@@ -595,8 +628,14 @@ 
    6.6 MCMCglmm utiliti
 
    ## Sampling 2 random posteriors samples IDs
 (random_samples <- MCMCglmm.sample(my_MCMCglmm, n = 2))
    
 
    ## [1] 749 901
    
-
    ## Extracting these two random amples
-my_covars <- MCMCglmm.covars(my_MCMCglmm, sample = random_samples)
    
+
    ## Extracting these two random samples
+my_covars <- MCMCglmm.covars(my_MCMCglmm, sample = random_samples)
+
+## Plotting the variance for each term in the model
+boxplot(MCMCglmm.variance(my_MCMCglmm), horizontal = TRUE, las = 1,
+        xlab = "Relative variance",
+        main = "Variance explained by each term")
    
+
    
 
    See more in the $covar section on what to do with these "MCMCglmm" objects.
    @@ -609,7 +648,7 @@

    6.7 pair.plot## Plotting the first column as a pairwise comparisons pair.plot(data, what = 1, col = c("orange", "blue"), legend = TRUE, diag = 1)

    -

    +

    Here blue squares are ones that have a high value and orange ones the ones that have low values. Note that the values plotted correspond the first column of the data as designated by what = 1.

    It is also possible to add some tokens or symbols to quickly highlight to specific cells, for example which elements in the data are below a certain value:

    @@ -620,7 +659,7 @@

    6.7 pair.plot## Highlighting with an asterisk which squares have a value ## below 0.2 pair.plot(data, what = 1, binary = 0.2, add = "*", cex = 2)

    -

    +

    This function can also be used as a binary display when running a series of pairwise t-tests. For example, the following script runs a wilcoxon test between the time-slices from the disparity example dataset and displays in black which pairs of slices have a p-value below 0.05:

    ## Loading disparity data
@@ -631,7 +670,7 @@ 
    6.7 pair.plot
 ## Plotting the significance
 pair.plot(as.data.frame(tests), what = "p.value", binary = 0.05)
    -

    +

    6.8 reduce.matrix

    @@ -648,7 +687,7 @@

    6.8 reduce.matrixna_matrix[sample(1:50, 25)] <- NA ## Illustrating the gappy matrix image(t(na_matrix), col = "black")

    -

    +

    We can use the reduce.matrix to double check whether any rows cannot be compared. The functions needs as an input the type of distance that will be used, say a "gower" distance:

    ## Reducing the matrix by row
@@ -661,7 +700,7 @@ 
    6.8 reduce.matrixWe can not remove the rows 1 and 9 and see if that improved the overlap:
    
 
    image(t(na_matrix[-as.numeric(reduction$rows.to.remove), ]),
       col = "black")
    
-
    
+

    6.9 select.axes

    @@ -710,7 +749,7 @@

    6.9 select.axes 
    ## Plotting it
 plot(selected)
    -

    +

    ## Extracting the dimensions
 ## (for the dispRity function for example)
 selected$dimensions
    @@ -733,7 +772,7 @@

    6.9 select.axesselected <- select.axes(ordination, group = states_groups, threshold = 0.9) ## Plotting the results plot(selected)

    -

    +

    As you can see here, the whole space requires the three first axes to explain at least 90% of the variance (in fact, 95% as seen before). However, different groups have a different story! The Group 1 and 3 requires 4 dimensions whereas Group 2 requires only 1 dimensions (note how for Group 3, there is actually nearly no variance explained on the second axes)! @@ -749,7 +788,7 @@

    6.9 select.axes
    ## Selecting axes on a dispRity object
 selected <- select.axes(demo_data$hopkins)
 plot(selected)
    -

    +

    ## Displaying which axes are necessary for which group
 selected$dim.list
    ## $adult
@@ -786,7 +825,7 @@ 
    6.10 slice.treeaxisPhylo() ; nodelabels(tree$node.label, cex = 0.8)
 abline(v = (max(tree.age(tree)$ages) - 0.75), col = "red")
 plot(tree_75, main = "sliced tree")
    -

    +

    6.11 slide.nodes and remove.zero.brlen

    @@ -809,7 +848,7 @@

    6.11 slide.nodes and plot(tree, main = "original tree") ; axisPhylo() ; nodelabels() plot(tree_slide_up, main = "slide up!") ; axisPhylo() ; nodelabels() plot(tree_slide_down, main = "slide down!") ; axisPhylo() ; nodelabels()

    -

    +

    The remove.zero.brlen is a “clever” wrapping function that uses the slide.nodes function to stochastically remove zero branch lengths across a whole tree. This function will slide nodes up or down in successive postorder traversals (i.e. going down the tree clade by clade) in order to minimise the number of nodes to slide while making sure there are no silly negative branch lengths produced! By default it is trying to slide the nodes using 1% of the minimum branch length to avoid changing the topology too much.

    @@ -838,7 +877,7 @@

    6.11 slide.nodes and plot(tree, main = "with zero edges") plot(tree_no_zero, main = "without zero edges!") plot(tree_exaggerated, main = "with longer edges") -

    +

    6.12 tree.age

    @@ -913,7 +952,215 @@

    6.12 tree.age +

    +
    +

    6.13 multi.ace

    +

    This function allows to run the ape::ace function (ancestral characters estimations) on multiple trees. +In it’s most basic structure (e.g. using all default arguments) this function is using a mix of ape::ace and castor::asr_mk_model depending on the data and the situation and is generally faster than both functions when applied to a list of trees. +However, this function provides also some more complex and modular functionalities, especially appropriate when using discrete morphological character data.

    +
    +

    6.13.1 Using different character tokens in different situations

    +

    This data can be often coded in non-standard way with different character tokens having different meanings. +For example, in some datasets the token - can mean “the trait is inapplicable” but this can be also coded by the more conventional NA or can mean “this trait is missing” (often coded ?). +This makes the meaning of specific tokens idiosyncratic to different matrices. +For example we can have the following discrete morphological matrix with all the data encoded:

    +
    set.seed(42)
+## A random tree with 10 tips
+tree <- rcoal(10)
+## Setting up the parameters
+my_rates = c(rgamma, rate = 10, shape = 5)
+
+## Generating a bunch of trees
+multiple_trees <- rmtree(5, 10)
+
+## A random Mk matrix (10*50)
+matrix_simple <- sim.morpho(tree, characters = 50, model = "ER", rates = my_rates,
+                            invariant = FALSE)
+matrix_simple[1:10, 1:10]
    +
    ##     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
+## t8  "1"  "1"  "1"  "1"  "0"  "0"  "0"  "0"  "0"  "1"  
+## t3  "1"  "1"  "1"  "1"  "0"  "0"  "0"  "0"  "0"  "1"  
+## t2  "1"  "1"  "1"  "1"  "0"  "1"  "1"  "1"  "0"  "1"  
+## t1  "1"  "1"  "1"  "1"  "0"  "0"  "1"  "1"  "0"  "1"  
+## t10 "1"  "1"  "1"  "1"  "0"  "0"  "1"  "0"  "1"  "1"  
+## t9  "1"  "1"  "1"  "1"  "0"  "0"  "1"  "0"  "0"  "1"  
+## t5  "0"  "0"  "0"  "0"  "1"  "1"  "1"  "0"  "0"  "0"  
+## t6  "0"  "0"  "0"  "0"  "1"  "1"  "1"  "0"  "0"  "0"  
+## t4  "0"  "0"  "0"  "0"  "1"  "0"  "0"  "0"  "1"  "0"  
+## t7  "0"  "0"  "0"  "0"  "1"  "0"  "0"  "0"  "1"  "0"
    +

    But of course, as mentioned above, in practice, such matrices have more nuance and can including missing characters, ambiguous characters, multi-state characters, inapplicable characters, etc… +All these coded and defined by different authors using different tokens (or symbols). +Let’s give it a go and transform this simple data to something more messy:

    +
    ## Modify the matrix to contain missing and special data
+matrix_complex <- matrix_simple
+## Adding 50 random "-" tokens
+matrix_complex[sample(1:length(matrix_complex), 50)] <- "-"
+## Adding 50 random "?" tokens
+matrix_complex[sample(1:length(matrix_complex), 50)] <- "?"
+## Adding 50 random "0%2" tokens
+matrix_complex[sample(1:length(matrix_complex), 50)] <- "0%2"
+matrix_complex[1:10,1:10]
    +
    ##     [,1] [,2]  [,3] [,4]  [,5]  [,6]  [,7]  [,8]  [,9] [,10]
+## t8  "1"  "1"   "1"  "1"   "?"   "0"   "0"   "0"   "0"  "0%2"
+## t3  "1"  "-"   "1"  "1"   "?"   "0"   "0"   "0"   "0"  "1"  
+## t2  "1"  "1"   "1"  "0%2" "0"   "0%2" "1"   "1"   "0"  "1"  
+## t1  "1"  "1"   "1"  "1"   "0"   "0"   "1"   "?"   "0"  "1"  
+## t10 "1"  "0%2" "1"  "1"   "-"   "?"   "0%2" "0%2" "1"  "1"  
+## t9  "1"  "1"   "?"  "1"   "0%2" "0"   "1"   "0"   "0"  "1"  
+## t5  "0"  "-"   "?"  "0"   "1"   "1"   "1"   "0"   "0"  "-"  
+## t6  "0"  "-"   "0"  "0"   "1"   "1"   "-"   "-"   "?"  "0"  
+## t4  "?"  "0"   "0"  "0"   "1"   "0"   "0"   "0"   "1"  "0"  
+## t7  "0"  "0"   "0"  "0%2" "1"   "0"   "0"   "-"   "1"  "-"
    +

    In multi.ace you can specify what all these tokens actually mean and how the code should interpret them. +For example, - often means inapplicable data (i.e. the specimen does not have the coded feature, for example, the colour of the tail of a tailless bird); or ? that often means missing data (i.e. it is unknown if the specimen has a tail or not since only the head was available). +And more than the differences in meaning between these characters, different people treat these characters differently even if they have the same meaning for the token. +For example, one might want to treat - as meaning “we don’t know” (which will be treated by the algorithm as “any possible trait value”) or “we know, and it’s no possible” (which will be treated by the algorithm as NA). +Because of this situation, multi.ace allows combining any special case marked with a special token to a special behaviour. +For example we might want to create a special case called "missing" (i.e. the data is missing) that we want to denote using the token "?" and we can specify the algorithm to treat this "missing" cases ("?") as treating the character token value as “any possible values”. +This behaviour can be hard coded by providing a function with the name of the behaviour. +For example:

    +
    ## The specific token for the missing cases (note the "\\" for protecting the value)
+special.tokens <- c("missing" = "\\?")
+
+## The behaviour for the missing cases (?)
+special.behaviour <- list(missing <- function(x, y) return(y))
+## Where x is the input value (here "?") and y is all the possible normal values for the character
    +

    This example shows a very common case (and is actually used by default, more on that below) but this architecture allows for very modular combination of tokens and behaviours. +For example, in our code above we introduced the token "%" which is very odd (to my knowledge) and might mean something very specific in our case. +Say we want to call this case "weirdtoken" and mean that whenever this token is encountered in a character, it should be interpreted by the algorithm as the values 1 and 2, no matter what:

    +
    ## Set a list of extra special tokens
+my_spec_tokens <- c("weirdtoken" = "\\%")
+
+## Weird tokens are considered as state 0 and 3
+my_spec_behaviours <- list()
+my_spec_behaviours$weirdtoken <- function(x,y) return(c(1,2))
    +

    If you don’t need/don’t have any of this specific tokens, don’t worry, most special but common tokens are handled by default as such:

    +
    ## The token for missing values:
+default_tokens <- c("missing"      = "\\?",
+## The token for inapplicable values:                    
+                    "inapplicable" = "\\-",
+## The token for polymorphisms:
+                    "polymorphism" = "\\&",
+## The token for uncertainties:
+                    "uncertanity"  = "\\/")
    +

    With the following associated default behaviours

    +
    ## Treating missing data as all data values
+default_behaviour <- list(missing      <- function(x,y) y,
+## Treating inapplicable data as all data values (like missing)    
+                          inapplicable <- function(x, y) y,
+## Treating polymorphisms as all values present:
+                          polymorphism <- function(x,y) strsplit(x, split = "\\&")[[1]],
+## Treating uncertainties as all values present (like polymorphisms):
+                          uncertanity  <- function(x,y) strsplit(x, split = "\\&")[[1]])
    +

    We can then use these token description along with our complex matrix and our list of trees to run the ancestral states estimations as follows:

    +
    ## Running ancestral states
+ancestral_states <- multi.ace(matrix_complex, multiple_trees,
+                              special.tokens = my_spec_tokens,
+                              special.behaviours = my_spec_behaviours,
+                              verbose = TRUE)
    +
    ## Preparing the data:...
    +
    ## Warning: The characters 39 are invariant (using the current special behaviours
+## for special characters) and are simply duplicated for each node.
    +
    ## ..Done.
+## Running ancestral states estimations:
+## .................................................
    +
    ## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs =
+## list(special.tokens = special.tokens), : longer argument not a multiple of
+## length of shorter
    +
    ##  Done.
+## Running ancestral states estimations:
+## .................................................
    +
    ## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs =
+## list(special.tokens = special.tokens), : longer argument not a multiple of
+## length of shorter
    +
    ##  Done.
+## Running ancestral states estimations:
+## .................................................
    +
    ## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs =
+## list(special.tokens = special.tokens), : longer argument not a multiple of
+## length of shorter
    +
    ##  Done.
+## Running ancestral states estimations:
+## .................................................
    +
    ## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs =
+## list(special.tokens = special.tokens), : longer argument not a multiple of
+## length of shorter
    +
    ##  Done.
+## Running ancestral states estimations:
+## .................................................
    +
    ## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs =
+## list(special.tokens = special.tokens), : longer argument not a multiple of
+## length of shorter
    +
    ##  Done.
    +
    ## This outputs a list of ancestral parts of the matrices for each tree
+## For example, here's the first one:
+ancestral_states[[1]][1:9, 1:10]
    +
    ##       [,1] [,2]  [,3]  [,4] [,5]  [,6]    [,7]  [,8] [,9] [,10]
+##  [1,] "1"  "1"   "1"   "1"  "1"   "0/1/2" "1"   "0"  "0"  "1"  
+##  [2,] "1"  "1"   "1"   "1"  "0/1" "0/1/2" "0/1" "0"  "0"  "1"  
+##  [3,] "1"  "1"   "1"   "1"  "0/1" "0/1/2" "0"   "0"  "0"  "1"  
+##  [4,] "1"  "1"   "1"   "1"  "0"   "0/1/2" "1"   "1"  "0"  "1"  
+##  [5,] "1"  "1"   "1"   "1"  "1"   "0/1/2" "1"   "0"  "0"  "1"  
+##  [6,] "1"  "1"   "1"   "1"  "1"   "0/1/2" "1"   "0"  "0"  "1"  
+##  [7,] "0"  "0/1" "0/1" "0"  "1"   "1"     "1"   "0"  "0"  "0/1"
+##  [8,] "0"  "0"   "0"   "0"  "1"   "0/1/2" "0"   "0"  "1"  "0"  
+##  [9,] "0"  "0"   "0"   "0"  "1"   "1"     "0"   "0"  "1"  "0"
    +

    Note that there are many different options that are not covered here. +For example, you can use different models for each character via the models argument, you can specify how to handle uncertainties via the threshold argument, use a branch length modifier (brlen.multiplier), specify the type of output, etc…

    +
    +
    +

    6.13.2 Feeding the results to char.diff to get distance matrices

    +

    Finally, after running your ancestral states estimations, it is not uncommon to then use these resulting data to calculate the distances between taxa and then ordinate the results to measure disparity. +You can do that using the char.diff function described above but instead of measuring the distances between characters (columns) you can measure the distances between species (rows). +You might notice that this function uses the same modular token and behaviour descriptions. +That makes sense because they’re using the same core C functions implemented in dispRity that greatly speed up distance calculations.

    +
    ## Running ancestral states
+## and outputing a list of combined matrices (tips and nodes)
+ancestral_states <- multi.ace(matrix_complex, multiple_trees,
+                              special.tokens = my_spec_tokens,
+                              special.behaviours = my_spec_behaviours,
+                              output = "combined.matrix",
+                              verbose = TRUE)
    +
    ## Preparing the data:...
    +
    ## Warning: The characters 39 are invariant (using the current special behaviours
+## for special characters) and are simply duplicated for each node.
    +
    ## ..Done.
+## Running ancestral states estimations:
+## .................................................
    +
    ## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs =
+## list(special.tokens = special.tokens), : longer argument not a multiple of
+## length of shorter
    +
    ##  Done.
+## Running ancestral states estimations:
+## .................................................
    +
    ## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs =
+## list(special.tokens = special.tokens), : longer argument not a multiple of
+## length of shorter
    +
    ##  Done.
+## Running ancestral states estimations:
+## .................................................
    +
    ## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs =
+## list(special.tokens = special.tokens), : longer argument not a multiple of
+## length of shorter
    +
    ##  Done.
+## Running ancestral states estimations:
+## .................................................
    +
    ## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs =
+## list(special.tokens = special.tokens), : longer argument not a multiple of
+## length of shorter
    +
    ##  Done.
+## Running ancestral states estimations:
+## .................................................
    +
    ## Warning in mapply(replace.NA, ancestral_states, characters_states, MoreArgs =
+## list(special.tokens = special.tokens), : longer argument not a multiple of
+## length of shorter
    +
    ##  Done.
    +

    We can then feed these matrices directly to char.diff, say for calculating the “MORD” distance:

    +
    ## Measuring the distances between rows using the MORD distance
+distances <- lapply(ancestral_states, char.diff, method = "mord", by.col = FALSE)
    +

    And we now have a list of distances matrices with ancestral states estimated!

    +
    @@ -982,7 +1229,7 @@

    6.12 tree.age - 9 Palaeobiology demo: disparity-through-time and within groups | Morphometric geometric demo: a between group analysis - - + 9 Palaeobiology demo: disparity-through-time and within groups | dispRity R package manual + + - + - + - + - + - + - + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -337,16 +370,16 @@

    9.1.1 The morphospaceexample data description for more details. Briefly, this dataset contains an ordinated matrix of the Gower distance between 50 mammals based (BeckLee_mat50), another matrix of the same 50 mammals and the estimated discrete data characters of their descendants (thus 50 + 49 rows, BeckLee_mat99), a dataframe containing the ages of each taxon in the dataset (BeckLee_ages) and finally a phylogenetic tree with the relationships among the 50 mammals (BeckLee_tree). The ordinated matrix will represent our full morphospace, i.e. all the mammalian morphologies that ever existed through time (for this dataset).

    -
    ## Loading demo and the package data
-library(dispRity)
-
-## Setting the random seed for repeatability
-set.seed(123)
-
-## Loading the ordinated matrix/morphospace:
-data(BeckLee_mat50)
-data(BeckLee_mat99)
-head(BeckLee_mat50[,1:5])
    +
    ## Loading demo and the package data
+library(dispRity)
+
+## Setting the random seed for repeatability
+set.seed(123)
+
+## Loading the ordinated matrix/morphospace:
+data(BeckLee_mat50)
+data(BeckLee_mat99)
+head(BeckLee_mat50[,1:5])
    ##                    [,1]        [,2]        [,3]       [,4]        [,5]
 ## Cimolestes   -0.5613001  0.06006259  0.08414761 -0.2313084 -0.18825039
 ## Maelestes    -0.4186019 -0.12186005  0.25556379  0.2737995 -0.28510479
@@ -354,13 +387,13 @@ 
    9.1.1 The morphospace
    dim(BeckLee_mat50)

    +
    dim(BeckLee_mat50)
    ## [1] 50 48
    -
    ## The morphospace contains 50 taxa and has 48 dimensions (or axes)
-
-## Showing a list of first and last occurrences data for some fossils
-data(BeckLee_ages)
-head(BeckLee_ages)
    +
    ## The morphospace contains 50 taxa and has 48 dimensions (or axes)
+
+## Showing a list of first and last occurrences data for some fossils
+data(BeckLee_ages)
+head(BeckLee_ages)
    ##             FAD  LAD
 ## Adapis     37.2 36.8
 ## Asioryctes 83.6 72.1
@@ -368,17 +401,17 @@ 
    9.1.1 The morphospace
    ## Plotting a phylogeny
-data(BeckLee_tree)
-plot(BeckLee_tree, cex = 0.7)
-axisPhylo(root = 140)
    
-
    
+
    ## Plotting a phylogeny
+data(BeckLee_tree)
+plot(BeckLee_tree, cex = 0.7)
+axisPhylo(root = 140)
    
+
    
 
    
 
    You can have an even nicer looking tree if you use the strap package!
    
 
    
-
    if(!require(strap)) install.packages("strap")
-strap::geoscalePhylo(BeckLee_tree, cex.tip = 0.7, cex.ts = 0.6)
    
-
    
+
    if(!require(strap)) install.packages("strap")
+strap::geoscalePhylo(BeckLee_tree, cex.tip = 0.7, cex.ts = 0.6)
    
+

    -
    ## Functions to get simulate a PCO looking like matrix from a tree
-i.need.a.matrix <- function(tree) {
-    matrix <- space.maker(elements = Ntip(tree), dimensions = Ntip(tree), distribution = rnorm,
-                          scree = rev(cumsum(rep(1/Ntip(tree), Ntip(tree)))))
-    rownames(matrix) <- tree$tip.label
-    return(matrix)
-}
-
-## Function to simulate a tree
-i.need.a.tree <- function(matrix) {
-    tree <- rtree(nrow(matrix))
-    tree$root.time <- max(tree.age(tree)$age)
-    tree$tip.label <- rownames(matrix)
-    tree$node.label <- paste0("n", 1:(nrow(matrix)-1))
-    return(tree)
-}
-
-## Function to simulate some "node" data
-i.need.node.data <- function(matrix, tree) {
-    matrix_node <- space.maker(elements = Nnode(tree), dimensions = ncol(matrix),
-                               distribution = rnorm, scree = apply(matrix, 2, var))
-    if(!is.null(tree$node.label)) {
-        rownames(matrix_node) <- tree$node.label
-    } else {
-        rownames(matrix_node) <- paste0("n", 1:(nrow(matrix)-1))
-    }
-    return(rbind(matrix, matrix_node))
-}
-
-## Function to simulate some "FADLAD" data
-i.need.FADLAD <- function(tree) {
-    tree_ages <- tree.age(tree)[1:Ntip(tree),]
-    return(data.frame(FAD = tree_ages[,1], LAD = tree_ages[,1], row.names = tree_ages[,2]))
-}
    +
    ## Functions to get simulate a PCO looking like matrix from a tree
+i.need.a.matrix <- function(tree) {
+    matrix <- space.maker(elements = Ntip(tree), dimensions = Ntip(tree), distribution = rnorm,
+                          scree = rev(cumsum(rep(1/Ntip(tree), Ntip(tree)))))
+    rownames(matrix) <- tree$tip.label
+    return(matrix)
+}
+
+## Function to simulate a tree
+i.need.a.tree <- function(matrix) {
+    tree <- rtree(nrow(matrix))
+    tree$root.time <- max(tree.age(tree)$age)
+    tree$tip.label <- rownames(matrix)
+    tree$node.label <- paste0("n", 1:(nrow(matrix)-1))
+    return(tree)
+}
+
+## Function to simulate some "node" data
+i.need.node.data <- function(matrix, tree) {
+    matrix_node <- space.maker(elements = Nnode(tree), dimensions = ncol(matrix),
+                               distribution = rnorm, scree = apply(matrix, 2, var))
+    if(!is.null(tree$node.label)) {
+        rownames(matrix_node) <- tree$node.label
+    } else {
+        rownames(matrix_node) <- paste0("n", 1:(nrow(matrix)-1))
+    }
+    return(rbind(matrix, matrix_node))
+}
+
+## Function to simulate some "FADLAD" data
+i.need.FADLAD <- function(tree) {
+    tree_ages <- tree.age(tree)[1:Ntip(tree),]
+    return(data.frame(FAD = tree_ages[,1], LAD = tree_ages[,1], row.names = tree_ages[,2]))
+}

    You can use these functions for the generating the data you need. For example

    -
    ## Aaaaah I don't have FADLAD data!
-my_FADLAD <- i.need.FADLAD(tree)
-## Sorted.
    +
    ## Aaaaah I don't have FADLAD data!
+my_FADLAD <- i.need.FADLAD(tree)
+## Sorted.

    In the end this is what your data should be named to facilitate the rest of this tutorial (fill in yours here):

    -
    ## A matrix with tip data
-my_matrix <- BeckLee_mat50
-
-## A phylogenetic tree 
-my_tree <- BeckLee_tree
-
-## A matrix with tip and node data
-my_tip_node_matrix <- BeckLee_mat99
-
-## A table of first and last occurrences data (FADLAD)
-my_fadlad <- BeckLee_ages
    +
    ## A matrix with tip data
+my_matrix <- BeckLee_mat50
+
+## A phylogenetic tree 
+my_tree <- BeckLee_tree
+
+## A matrix with tip and node data
+my_tip_node_matrix <- BeckLee_mat99
+
+## A table of first and last occurrences data (FADLAD)
+my_fadlad <- BeckLee_ages
    @@ -464,21 +497,21 @@

    9.2.1 Splitting the morphospace t

    The dispRity package provides a chrono.subsets function that allows users to split the morphospace into time slices (using method = continuous) or into time bins (using method = discrete). In this example, we are going to split the morphospace into five equal time bins of 20 million years long from 100 million years ago to the present. We will also provide to the function a table containing the first and last occurrences dates for some fossils to take into account that some fossils might occur in several of our different time bins.

    -
    ## Creating the vector of time bins ages
-time_bins <- rev(seq(from = 0, to = 100, by = 20))
-
-## Splitting the morphospace using the chrono.subsets function
-binned_morphospace <- chrono.subsets(data = my_matrix, tree = my_tree,
-    method = "discrete", time = time_bins, inc.nodes = FALSE,
-    FADLAD = my_fadlad)
    +
    ## Creating the vector of time bins ages
+time_bins <- rev(seq(from = 0, to = 100, by = 20))
+
+## Splitting the morphospace using the chrono.subsets function
+binned_morphospace <- chrono.subsets(data = my_matrix, tree = my_tree,
+    method = "discrete", time = time_bins, inc.nodes = FALSE,
+    FADLAD = my_fadlad)

    The output object is a dispRity object (see more about that here. In brief, dispRity objects are lists of different elements (i.e. disparity results, morphospace time subsets, morphospace attributes, etc.) that display only a summary of the object when calling the object to avoiding filling the R console with superfluous output. It also allows easy plotting/summarising/analysing for repeatability down the line but we will not go into this right now.

    -
    ## Printing the class of the object
-class(binned_morphospace)
    +
    ## Printing the class of the object
+class(binned_morphospace)
    ## [1] "dispRity"
    -
    ## Printing the content of the object
-str(binned_morphospace)
    +
    ## Printing the content of the object
+str(binned_morphospace)
    ## List of 4
 ##  $ matrix :List of 1
 ##   ..$ : num [1:50, 1:48] -0.561 -0.419 -0.834 -0.771 -0.832 ...
@@ -511,10 +544,10 @@ 
    9.2.1 Splitting the morphospace t
 ##   ..$ 20 - 0  :List of 1
 ##   .. ..$ elements: int [1:10, 1] 36 37 38 32 33 34 50 48 29 30
 ##  - attr(*, "class")= chr "dispRity"
    -
    names(binned_morphospace)
    +
    names(binned_morphospace)
    ## [1] "matrix"  "tree"    "call"    "subsets"
    -
    ## Printing the object as a dispRity class
-binned_morphospace
    +
    ## Printing the object as a dispRity class
+binned_morphospace
    ##  ---- dispRity object ---- 
 ## 5 discrete time subsets for 50 elements in one matrix with 1 phylogenetic tree
 ##     100 - 80, 80 - 60, 60 - 40, 40 - 20, 20 - 0.
    @@ -527,12 +560,12 @@

    9.2.2 Bootstrapping the dataOnce we obtain our different time subsets, we can bootstrap and rarefy them (i.e. pseudo-replicating the data). The bootstrapping allows us to make each subset more robust to outliers and the rarefaction allows us to compare subsets with the same number of taxa to remove sampling biases (i.e. more taxa in one subset than the others). The boot.matrix function bootstraps the dispRity object and the rarefaction option within performs rarefaction.

    -
    ## Getting the minimum number of rows (i.e. taxa) in the time subsets
-minimum_size <- min(size.subsets(binned_morphospace))
-
-## Bootstrapping each time subset 100 times and rarefying them 
-rare_bin_morphospace <- boot.matrix(binned_morphospace, bootstraps = 100,
-    rarefaction = minimum_size)
    +
    ## Getting the minimum number of rows (i.e. taxa) in the time subsets
+minimum_size <- min(size.subsets(binned_morphospace))
+
+## Bootstrapping each time subset 100 times and rarefying them 
+rare_bin_morphospace <- boot.matrix(binned_morphospace, bootstraps = 100,
+    rarefaction = minimum_size)

    Note how information is adding up to the dispRity object.

    @@ -550,29 +583,29 @@

    9.2.3 Calculating disparity

    This is not a straightforward question but you can use the test.metric function to check your assumptions (more details here): basically what test.metric does is modifying your morphospace using a null process of interest (e.g. changes in size) and checks whether your metric does indeed pick up that change. For example here, let see if the sum of variances picks up changes in size but not random changes:

    -
    my_test <- test.metric(my_matrix, metric = c(sum, dispRity::variances), shifts = c("random", "size"))
-summary(my_test)
    -
    ##             10%  20%  30%  40%  50%  60%  70%  80%  90% 100%        slope
-## random     2.41 2.49 2.56 2.50 2.54 2.51 2.52 2.53 2.53 2.52 0.0007332213
-## size.inner 2.23 2.19 2.25 2.33 2.31 2.35 2.39 2.44 2.48 2.52 0.0035472233
-## size.outer 2.43 2.56 2.59 2.60 2.52 2.64 2.60 2.58 2.55 2.52 0.0004775114
-##                 p_value   R^2(adj)
-## random     1.346446e-02 0.17044976
-## size.inner 5.563985e-17 0.93281279
-## size.outer 1.837621e-01 0.02872334
    -
    plot(my_test)
    -

    +
    my_test <- test.metric(my_matrix, metric = c(sum, dispRity::variances), shifts = c("random", "size"))
+summary(my_test)
    +
    ##                  10%  20%  30%  40%  50%  60%  70%  80%  90% 100%        slope
+## random          2.41 2.51 2.56 2.50 2.54 2.51 2.52 2.53 2.53 2.52 0.0006434981
+## size.increase   2.23 2.19 2.25 2.33 2.31 2.35 2.43 2.44 2.48 2.52 0.0036071419
+## size.hollowness 2.40 2.56 2.56 2.60 2.63 2.64 2.60 2.58 2.55 2.52 0.0006032204
+##                      p_value   R^2(adj)
+## random          3.046683e-02 0.12638784
+## size.increase   4.009847e-16 0.90601561
+## size.hollowness 1.324664e-01 0.04783366
    +
    plot(my_test)
    +

    We see that changes in the inner size (see Thomas Guillerme, Puttick, et al. (2020) for more details) is actually picked up by the sum of variances but not random changes or outer changes. Which is a good thing!

    As you’ve noted, the sum of variances is defined in test.metric as c(sum, variances). This is a core bit of the dispRity package were you can define your own metric as a function or a set of functions. You can find more info about this in the dispRity metric section but in brief, the dispRity package considers metrics by their “dimensions” level which corresponds to what they output. For example, the function sum is a dimension level 1 function because no matter the input it outputs a single value (the sum), variances on the other hand is a dimension level 2 function because it will output the variance of each column in a matrix (an example of a dimensions level 3 would be the function var that outputs a matrix). The dispRity package always automatically sorts the dimensions levels: it will always run dimensions level 3 > dimensions level 2 > and dimensions level 1. In this case both c(sum, variances) and c(variances, sum) will result in actually running sum(variances(matrix)).

    Anyways, let’s calculate the sum of variances on our bootstrapped and rarefied morphospaces:

    -
    ## Calculating disparity for the bootstrapped and rarefied data
-disparity <- dispRity(rare_bin_morphospace , metric = c(sum, dispRity::variances))
    +
    ## Calculating disparity for the bootstrapped and rarefied data
+disparity <- dispRity(rare_bin_morphospace , metric = c(sum, dispRity::variances))

    To display the actual calculated scores, we need to summarise the disparity object using the S3 method summary that is applied to a dispRity object (see ?summary.dispRity for more details). By the way, as for any R package, you can refer to the help files for each individual function for more details.

    -
    ## Summarising the disparity results
-summary(disparity)
    +
    ## Summarising the disparity results
+summary(disparity)
    ##    subsets  n   obs bs.median  2.5%   25%   75% 97.5%
 ## 1 100 - 80  8 2.207     1.962 1.615 1.876 2.017 2.172
 ## 2 100 - 80  6    NA     1.923 1.477 1.768 2.065 2.222
@@ -591,15 +624,15 @@ 
    9.2.3 Calculating disparity9.2.4 Plotting the results
    
 
    It is sometimes easier to visualise the results in a plot than in a table.
 For that we can use the plot S3 function to plot the dispRity objects (see ?plot.dispRity for more details).
    
-
    ## Graphical options
-quartz(width = 10, height = 5) ; par(mfrow = (c(1,2)), bty = "n")
    
+
    ## Graphical options
+quartz(width = 10, height = 5) ; par(mfrow = (c(1,2)), bty = "n")
    
 
    ## Warning in quartz(width = 10, height = 5): Quartz device is not available on
 ## this platform
    
-
    ## Plotting the bootstrapped and rarefied results
-plot(disparity, type = "continuous", main = "bootstrapped results")
-plot(disparity, type = "continuous", main = "rarefied results",
-     rarefaction = minimum_size)
    
-
    
+
    ## Plotting the bootstrapped and rarefied results
+plot(disparity, type = "continuous", main = "bootstrapped results")
+plot(disparity, type = "continuous", main = "rarefied results",
+     rarefaction = minimum_size)
    
+
    
 
    Nice. The curves look pretty similar.
    
 
    
 
    Same as for the summary.dispRity function, check out the plot.dispRity manual for the many, many options available.
    
@@ -612,9 +645,9 @@ 
    9.2.5 Testing differencesn is equal to the disparity in bin n+1, and whether this is in turn equal to the disparity in bin n+2, etc.
 Because our data is temporally autocorrelated (i.e. what happens in bin n+1 depends on what happened in bin n) and pseudoreplicated (i.e. each bootstrap draw creates non-independent time subsets because they are all based on the same time subsets), we apply a non-parametric mean comparison: the wilcox.test.
 Also, we need to apply a p-value correction (e.g. Bonferroni correction) to correct for multiple testing (see ?p.adjust for more details).
    
-
    ## Testing the differences between bins in the bootstrapped dataset.
-test.dispRity(disparity, test = wilcox.test, comparison = "sequential",
-    correction = "bonferroni")
    
+
    ## Testing the differences between bins in the bootstrapped dataset.
+test.dispRity(disparity, test = wilcox.test, comparison = "sequential",
+    correction = "bonferroni")
    
 
    ## [[1]]
 ##                    statistic: W
 ## 100 - 80 : 80 - 60          730
@@ -628,9 +661,9 @@ 
    9.2.5 Testing differences
    
-
    ## Testing the differences between bins in the rarefied dataset.
-test.dispRity(disparity, test = wilcox.test, comparison = "sequential",
-    correction = "bonferroni", rarefaction  = minimum_size)
    
+
    ## Testing the differences between bins in the rarefied dataset.
+test.dispRity(disparity, test = wilcox.test, comparison = "sequential",
+    correction = "bonferroni", rarefaction  = minimum_size)
    
 
    ## [[1]]
 ##                    statistic: W
 ## 100 - 80 : 80 - 60         1518
@@ -661,7 +694,7 @@ 
    9.3 Some more advanced stuff

    -

    References

    +

    References

    Beck, Robin M, and Michael S Lee. 2014. “Ancient Dates or Accelerated Rates? Morphological Clocks and the Antiquity of Placental Mammals.” Proceedings of the Royal Society B: Biological Sciences 281 (20141278): 1–10. https://doi.org/10.1098/rspb.2014.1278.

    @@ -681,7 +714,7 @@

    References

    - + @@ -742,7 +775,7 @@

    References + + + + + + 12 References | dispRity R package manual + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
    +
    + + +
    +
    + +
    +
    +

    12 References

    + +
    +
    +

    Aguilera, Antonio, and Ricardo Pérez-Aguila. 2004. “General N-Dimensional Rotations.” http://wscg.zcu.cz/wscg2004/Papers_2004_Short/N29.pdf.

    +
    +
    +

    Beck, Robin M, and Michael S Lee. 2014. “Ancient Dates or Accelerated Rates? Morphological Clocks and the Antiquity of Placental Mammals.” Proceedings of the Royal Society B: Biological Sciences 281 (20141278): 1–10. https://doi.org/10.1098/rspb.2014.1278.

    +
    +
    +

    Brazeau, Martin D, Thomas Guillerme, and Martin R Smith. 2018. “An algorithm for Morphological Phylogenetic Analysis with Inapplicable Data.” Systematic Biology 68 (4): 619–31. https://doi.org/10.1093/sysbio/syy083.

    +
    +
    +

    Cooper, Natalie, Gavin H. Thomas, Chris Venditti, Andrew Meade, and Rob P. Freckleton. 2016. “A Cautionary Note on the Use of Ornstein Uhlenbeck Models in Macroevolutionary Studies.” Biological Journal of the Linnean Society 118 (1): 64–77. https://doi.org/10.1111/bij.12701.

    +
    +
    +

    Dı́az, Sandra, Jens Kattge, Johannes HC Cornelissen, Ian J Wright, Sandra Lavorel, Stéphane Dray, Björn Reu, et al. 2016. “The Global Spectrum of Plant Form and Function.” Nature 529 (7585): 167. http://dx.doi.org/10.1038/nature16489.

    +
    +
    +

    E., O’Reilly Joseph, Puttick Mark N., Pisani Davide, and Donoghue Philip C. J. n.d. “Probabilistic Methods Surpass Parsimony When Assessing Clade Support in Phylogenetic Analyses of Discrete Morphological Data.” Palaeontology 61 (1): 105–18. https://doi.org/10.1111/pala.12330.

    +
    +
    +

    Endler, John A, David A Westcott, Joah R Madden, and Tim Robson. 2005. “Animal Visual Systems and the Evolution of Color Patterns: Sensory Processing Illuminates Signal Evolution.” Evolution 59 (8): 1795–1818.

    +
    +
    +

    FitzJohn, Richard G. 2012. “Diversitree: Comparative Phylogenetic Analyses of Diversification in R.” Methods in Ecology and Evolution 3 (6): 1084–92. https://doi.org/10.1111/j.2041-210X.2012.00234.x.

    +
    +
    +

    Guillerme, T., and N. Cooper. 2018. “Time for a Rethink: Time Sub-Sampling Methods in Disparity-Through-Time Analyses.” Palaeontology 61 (4): 481–93. https://doi.org/10.1111/pala.12364.

    +
    +
    +

    Guillerme, Thomas, and Natalie Cooper. 2016. “Effects of Missing Data on Topological Inference Using a Total Evidence Approach.” Molecular Phylogenetics and Evolution 94, Part A: 146–58. https://doi.org/http://dx.doi.org/10.1016/j.ympev.2015.08.023.

    +
    +
    +

    Guillerme, Thomas, Natalie Cooper, Stephen L. Brusatte, Katie E. Davis, Andrew L. Jackson, Sylvain Gerber, Anjali Goswami, et al. 2020. “Disparities in the Analysis of Morphological Disparity.” Biology Letters 16 (7): 20200199. https://doi.org/10.1098/rsbl.2020.0199.

    +
    +
    +

    Guillerme, Thomas, and Kevin Healy. 2014. mulTree: a package for running MCMCglmm analysis on multiple trees. Zenodo. https://doi.org/10.5281/zenodo.12902.

    +
    +
    +

    Guillerme, Thomas, Mark N Puttick, Ariel E Marcy, and Vera Weisbecker. 2020. “Shifting Spaces: Which Disparity or Dissimilarity Measurement Best Summarize Occupancy in Multidimensional Spaces?” Ecology and Evolution.

    +
    +
    +

    Hadfield, Jarrod D. 2010a. “MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package.” Journal of Statistical Software 33 (2): 1–22. https://www.jstatsoft.org/v33/i02/.

    +
    +
    +

    ———. 2010b. “MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package.” Journal of Statistical Software 33 (2): 1–22. https://www.jstatsoft.org/v33/i02/.

    +
    +
    +

    Hasegawa, M., H. Kishino, and T. A. Yano. 1985. “Dating of the Human Ape Splitting by a Molecular Clock of Mitochondrial-DNA.” Journal of Molecular Evolution 22 (2): 160–74.

    +
    +
    +

    Hunt, Gene. 2006. “Fitting and Comparing Models of Phyletic Evolution: Random Walks and Beyond.” Paleobiology 32 (4): 578–601. https://doi.org/10.1666/05070.1.

    +
    +
    +

    ———. 2012. “Measuring Rates of Phenotypic Evolution and the Inseparability of Tempo and Mode.” Paleobiology 38 (3): 351–73. https://doi.org/10.1666/11047.1.

    +
    +
    +

    Hunt, Gene, Melanie J Hopkins, and Scott Lidgard. 2015. “Simple Versus Complex Models of Trait Evolution and Stasis as a Response to Environmental Change.” Proceedings of the National Academy of Sciences, 201403662. https://doi.org/10.1073/pnas.1403662111.

    +
    +
    +

    Lewis, P. 2001. “A Likelihood Approach to Estimating Phylogeny from Discrete Morphological Character Data.” Systematic Biology 50 (6): 913–25. https://doi.org/10.1080/106351501753462876.

    +
    +
    +

    Murrell, David J. 2018. “A Global Envelope Test to Detect Non-Random Bursts of Trait Evolution.” Methods in Ecology and Evolution 9 (7): 1739–48. https://doi.org/10.1111/2041-210X.13006.

    +
    +
    +

    O’Reilly, Joseph E., Mark N. Puttick, Luke Parry, Alastair R. Tanner, James E. Tarver, James Fleming, Davide Pisani, and Philip C. J. Donoghue. 2016. “Bayesian Methods Outperform Parsimony but at the Expense of Precision in the Estimation of Phylogeny from Discrete Morphological Data.” Biology Letters 12 (4). https://doi.org/10.1098/rsbl.2016.0081.

    +
    +
    +

    Puttick, Mark N, Joseph E O’Reilly, Alastair R Tanner, James F Fleming, James Clark, Lucy Holloway, Jesus Lozano-Fernandez, et al. 2017. “Uncertain-Tree: Discriminating Among Competing Approaches to the Phylogenetic Analysis of Phenotype Data.” Proceedings of the Royal Society B 284 (1846): 20162290. http://dx.doi.org/10.1098/rspb.2016.2290.

    +
    +
    +
    +
    + +
    +
    +
    + + +
    +
    + + + + + + + + + + + + + + + diff --git a/inst/gitbook/_book/references.html b/inst/gitbook/_book/references.html index b6a673f4..059c785e 100644 --- a/inst/gitbook/_book/references.html +++ b/inst/gitbook/_book/references.html @@ -4,34 +4,34 @@ - References | Morphometric geometric demo: a between group analysis - - + References | dispRity R package manual + + - + - + - + - + - + - - + + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -327,86 +360,14 @@

    References

    - -
    -
    -

    Aguilera, Antonio, and Ricardo Pérez-Aguila. 2004. “General N-Dimensional Rotations.” http://wscg.zcu.cz/wscg2004/Papers_2004_Short/N29.pdf.

    -
    -
    -

    Beck, Robin M, and Michael S Lee. 2014. “Ancient Dates or Accelerated Rates? Morphological Clocks and the Antiquity of Placental Mammals.” Proceedings of the Royal Society B: Biological Sciences 281 (20141278): 1–10. https://doi.org/10.1098/rspb.2014.1278.

    -
    -
    -

    Brazeau, Martin D, Thomas Guillerme, and Martin R Smith. 2018. “An algorithm for Morphological Phylogenetic Analysis with Inapplicable Data.” Systematic Biology 68 (4): 619–31. https://doi.org/10.1093/sysbio/syy083.

    -
    -
    -

    Cooper, Natalie, Gavin H. Thomas, Chris Venditti, Andrew Meade, and Rob P. Freckleton. 2016. “A Cautionary Note on the Use of Ornstein Uhlenbeck Models in Macroevolutionary Studies.” Biological Journal of the Linnean Society 118 (1): 64–77. https://doi.org/10.1111/bij.12701.

    -
    -
    -

    Dı́az, Sandra, Jens Kattge, Johannes HC Cornelissen, Ian J Wright, Sandra Lavorel, Stéphane Dray, Björn Reu, et al. 2016. “The Global Spectrum of Plant Form and Function.” Nature 529 (7585): 167. http://dx.doi.org/10.1038/nature16489.

    -
    -
    -

    E., O’Reilly Joseph, Puttick Mark N., Pisani Davide, and Donoghue Philip C. J. n.d. “Probabilistic Methods Surpass Parsimony When Assessing Clade Support in Phylogenetic Analyses of Discrete Morphological Data.” Palaeontology 61 (1): 105–18. https://doi.org/10.1111/pala.12330.

    -
    -
    -

    Endler, John A, David A Westcott, Joah R Madden, and Tim Robson. 2005. “Animal Visual Systems and the Evolution of Color Patterns: Sensory Processing Illuminates Signal Evolution.” Evolution 59 (8): 1795–1818.

    -
    -
    -

    FitzJohn, Richard G. 2012. “Diversitree: Comparative Phylogenetic Analyses of Diversification in R.” Methods in Ecology and Evolution 3 (6): 1084–92. https://doi.org/10.1111/j.2041-210X.2012.00234.x.

    -
    -
    -

    Guillerme, T., and N. Cooper. 2018. “Time for a Rethink: Time Sub-Sampling Methods in Disparity-Through-Time Analyses.” Palaeontology 61 (4): 481–93. https://doi.org/10.1111/pala.12364.

    -
    -
    -

    Guillerme, Thomas, and Natalie Cooper. 2016. “Effects of Missing Data on Topological Inference Using a Total Evidence Approach.” Molecular Phylogenetics and Evolution 94, Part A: 146–58. https://doi.org/http://dx.doi.org/10.1016/j.ympev.2015.08.023.

    -
    -
    -

    Guillerme, Thomas, Natalie Cooper, Stephen L. Brusatte, Katie E. Davis, Andrew L. Jackson, Sylvain Gerber, Anjali Goswami, et al. 2020. “Disparities in the Analysis of Morphological Disparity.” Biology Letters 16 (7): 20200199. https://doi.org/10.1098/rsbl.2020.0199.

    -
    -
    -

    Guillerme, Thomas, and Kevin Healy. 2014. mulTree: a package for running MCMCglmm analysis on multiple trees. Zenodo. https://doi.org/10.5281/zenodo.12902.

    -
    -
    -

    Guillerme, Thomas, Mark N Puttick, Ariel E Marcy, and Vera Weisbecker. 2020. “Shifting Spaces: Which Disparity or Dissimilarity Measurement Best Summarize Occupancy in Multidimensional Spaces?” Ecology and Evolution.

    -
    -
    -

    Hadfield, Jarrod D. 2010a. “MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package.” Journal of Statistical Software 33 (2): 1–22. https://www.jstatsoft.org/v33/i02/.

    -
    -
    -

    ———. 2010b. “MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package.” Journal of Statistical Software 33 (2): 1–22. https://www.jstatsoft.org/v33/i02/.

    -
    -
    -

    Hasegawa, M., H. Kishino, and T. A. Yano. 1985. “Dating of the Human Ape Splitting by a Molecular Clock of Mitochondrial-DNA.” Journal of Molecular Evolution 22 (2): 160–74.

    -
    -
    -

    Hunt, Gene. 2006. “Fitting and Comparing Models of Phyletic Evolution: Random Walks and Beyond.” Paleobiology 32 (4): 578–601. https://doi.org/10.1666/05070.1.

    -
    -
    -

    ———. 2012. “Measuring Rates of Phenotypic Evolution and the Inseparability of Tempo and Mode.” Paleobiology 38 (3): 351–73. https://doi.org/10.1666/11047.1.

    -
    -
    -

    Hunt, Gene, Melanie J Hopkins, and Scott Lidgard. 2015. “Simple Versus Complex Models of Trait Evolution and Stasis as a Response to Environmental Change.” Proceedings of the National Academy of Sciences, 201403662. https://doi.org/10.1073/pnas.1403662111.

    -
    -
    -

    Lewis, P. 2001. “A Likelihood Approach to Estimating Phylogeny from Discrete Morphological Character Data.” Systematic Biology 50 (6): 913–25. https://doi.org/10.1080/106351501753462876.

    -
    -
    -

    Murrell, David J. 2018. “A Global Envelope Test to Detect Non-Random Bursts of Trait Evolution.” Methods in Ecology and Evolution 9 (7): 1739–48. https://doi.org/10.1111/2041-210X.13006.

    -
    -
    -

    O’Reilly, Joseph E., Mark N. Puttick, Luke Parry, Alastair R. Tanner, James E. Tarver, James Fleming, Davide Pisani, and Philip C. J. Donoghue. 2016. “Bayesian Methods Outperform Parsimony but at the Expense of Precision in the Estimation of Phylogeny from Discrete Morphological Data.” Biology Letters 12 (4). https://doi.org/10.1098/rsbl.2016.0081.

    -
    -
    -

    Puttick, Mark N, Joseph E O’Reilly, Alastair R Tanner, James F Fleming, James Clark, Lucy Holloway, Jesus Lozano-Fernandez, et al. 2017. “Uncertain-Tree: Discriminating Among Competing Approaches to the Phylogenetic Analysis of Phenotype Data.” Proceedings of the Royal Society B 284 (1846): 20162290. http://dx.doi.org/10.1098/rspb.2016.2290.

    -
    -

    - - + + @@ -466,7 +427,7 @@

    References - 7 The guts of the dispRity package | Morphometric geometric demo: a between group analysis - - + 7 The guts of the dispRity package | dispRity R package manual + + - + - + - + - + - + @@ -31,7 +31,7 @@ - + @@ -53,6 +53,28 @@ + + + + + + + + + + + + + + + + + + + + + + + @@ -250,6 +276,11 @@
  • 6.10 slice.tree
  • 6.11 slide.nodes and remove.zero.brlen
  • 6.12 tree.age
    • +
  • 6.13 multi.ace +
  • 7 The guts of the dispRity package
    • -
  • 8 Ecology demo +
  • 8 dispRity ecology demo
  • 9 Palaeobiology demo: disparity-through-time and within groups @@ -304,7 +335,9 @@
  • 10.2 Calculating disparity
  • 10.3 Analyse the results
    • +
  • 11 dispRity R package manual
  • References
    • +
  • 12 References
  • Project GitHub page
    • @@ -317,7 +350,7 @@
    @@ -336,26 +369,26 @@

    7.1 Manipulating dispRitydispRity also contains various utility functions that manipulate the dispRity object (e.g. sort.dispRity, extract.dispRity see the full list in the next section). These functions modify the dispRity object without having to delve into its complex structure! The full structure of a dispRity object is detailed here.

    -
    ## Loading the example data
-data(disparity)
-
-## What is the class of the median_centroids object?
-class(disparity)
    +
    ## Loading the example data
+data(disparity)
+
+## What is the class of the median_centroids object?
+class(disparity)
    ## [1] "dispRity"
    -
    ## What does the object contain?
-names(disparity)
    +
    ## What does the object contain?
+names(disparity)
    ## [1] "matrix"    "tree"      "call"      "subsets"   "disparity"
    -
    ## Summarising it using the S3 method print.dispRity
-disparity
    +
    ## Summarising it using the S3 method print.dispRity
+disparity
    ##  ---- dispRity object ---- 
 ## 7 continuous (acctran) time subsets for 99 elements in one matrix with 97 dimensions with 1 phylogenetic tree
 ##      90, 80, 70, 60, 50 ...
 ## Data was bootstrapped 100 times (method:"full") and rarefied to 20, 15, 10, 5 elements.
 ## Disparity was calculated as: c(median, centroids).

    Note that it is always possible to recall the full object using the argument all = TRUE in print.dispRity:

    -
    ## Display the full object
-print(disparity, all = TRUE)
-## This is more nearly ~ 5000 lines on my 13 inch laptop screen!
    +
    ## Display the full object
+print(disparity, all = TRUE)
+## This is more nearly ~ 5000 lines on my 13 inch laptop screen!

    7.2 dispRity utilities

    @@ -366,28 +399,28 @@

    7.2.1 dispRity objec

    7.2.1.1 make.dispRity

    This function creates empty dispRity objects.

    -
    ## Creating an empty dispRity object
-make.dispRity()
    +
    ## Creating an empty dispRity object
+make.dispRity()
    ## Empty dispRity object.
    -
    ## Creating an "empty" dispRity object with a matrix
-(disparity_obj <- make.dispRity(matrix(rnorm(20), 5, 4)))
    +
    ## Creating an "empty" dispRity object with a matrix
+(disparity_obj <- make.dispRity(matrix(rnorm(20), 5, 4)))
    ##  ---- dispRity object ---- 
 ## Contains a matrix 5x4.

    7.2.1.2 fill.dispRity

    This function initialises a dispRity object and generates its call properties.

    -
    ## The dispRity object's call is indeed empty
-disparity_obj$call
    +
    ## The dispRity object's call is indeed empty
+disparity_obj$call
    ## list()
    -
    ## Filling an empty disparity object (that needs to contain at least a matrix)
-(disparity_obj <- fill.dispRity(disparity_obj))
    -
    ## Warning in check.dispRity.data(data$matrix): Row names have been automatically
+
    ## Filling an empty disparity object (that needs to contain at least a matrix)
+(disparity_obj <- fill.dispRity(disparity_obj))
    
+
    ## Warning in check.data(data, match_call): Row names have been automatically
 ## added to data$matrix.
    
 
    ##  ---- dispRity object ---- 
 ## 5 elements in one matrix with 4 dimensions.
    
-
    ## The dipRity object has now the correct minimal attributes
-disparity_obj$call
    
+
    ## The dipRity object has now the correct minimal attributes
+disparity_obj$call
    
 
    ## $dimensions
 ## [1] 1 2 3 4
    @@ -395,15 +428,15 @@

    7.2.1.2 fill.dispRity7.2.1.3 get.matrix

    This function extracts a specific matrix from a disparity object. The matrix can be one of the bootstrapped matrices or/and a rarefied matrix.

    -
    ## Extracting the matrix containing the coordinates of the elements at time 50
-str(get.matrix(disparity, "50"))
    +
    ## Extracting the matrix containing the coordinates of the elements at time 50
+str(get.matrix(disparity, "50"))
    ##  num [1:18, 1:97] -0.1036 0.4318 0.3371 0.0501 0.685 ...
 ##  - attr(*, "dimnames")=List of 2
 ##   ..$ : chr [1:18] "Leptictis" "Dasypodidae" "n24" "Potamogalinae" ...
 ##   ..$ : NULL
    -
    ## Extracting the 3rd bootstrapped matrix with the 2nd rarefaction level
-## (15 elements) from the second group (80 Mya)
-str(get.matrix(disparity, subsets = 1, bootstrap = 3, rarefaction = 2))
    +
    ## Extracting the 3rd bootstrapped matrix with the 2nd rarefaction level
+## (15 elements) from the second group (80 Mya)
+str(get.matrix(disparity, subsets = 1, bootstrap = 3, rarefaction = 2))
    ##  num [1:15, 1:97] -0.12948 -0.57973 0.00361 0.27123 0.27123 ...
 ##  - attr(*, "dimnames")=List of 2
 ##   ..$ : chr [1:15] "n15" "Maelestes" "n20" "n34" ...
@@ -412,79 +445,139 @@ 
    7.2.1.3 get.matrix
 
    7.2.1.4 n.subsets
    
 
    This function simply counts the number of subsets in a dispRity object.
    
-
    ## How many subsets are in this object?
-n.subsets(disparity)
    
+
    ## How many subsets are in this object?
+n.subsets(disparity)
    
 
    ## [1] 7

    -
    -

    7.2.1.5 size.subsets

    +
    +

    7.2.1.5 name.subsets

    +

    This function gets you the names of the subsets in a dispRity object as a vector.

    +
    ## What are they called?
+name.subsets(disparity)
    +
    ## [1] "90" "80" "70" "60" "50" "40" "30"
    +
    +
    +

    7.2.1.6 size.subsets

    This function tells the number of elements in each subsets of a dispRity object.

    -
    ## How many elements are there in each subset?
-size.subsets(disparity)
    +
    ## How many elements are there in each subset?
+size.subsets(disparity)
    ## 90 80 70 60 50 40 30 
 ## 18 22 23 21 18 15 10
    -
    -

    7.2.1.6 get.subsets

    +
    +

    7.2.1.7 get.subsets

    This function creates a dispRity object that contains only elements from one specific subsets.

    -
    ## Extracting all the data for the crown mammals
-(crown_mammals <- get.subsets(disp_crown_stemBS, "Group.crown"))
-
-## The object keeps the properties of the parent object but is composed of only one subsets
-length(crown_mammals$subsets)
    +
    ## Extracting all the data for the crown mammals
+(crown_mammals <- get.subsets(disp_crown_stemBS, "Group.crown"))
+
+## The object keeps the properties of the parent object but is composed of only one subsets
+length(crown_mammals$subsets)
    -
    -

    7.2.1.7 combine.subsets

    +
    +

    7.2.1.8 combine.subsets

    This function allows to merge different subsets.

    -
    ## Combine the two first subsets in the dispRity data example
-combine.subsets(disparity, c(1,2))
    +
    ## Combine the two first subsets in the dispRity data example
+combine.subsets(disparity, c(1,2))

    Note that the computed values (bootstrapped data + disparity metric) are not merge.

    -
    -

    7.2.1.8 get.disparity

    +
    +

    7.2.1.9 get.disparity

    This function extracts the calculated disparity values of a specific matrix.

    -
    ## Extracting the observed disparity (default)
-get.disparity(disparity)
-
-## Extracting the disparity from the bootstrapped values from the
-## 10th rarefaction level from the second subsets (80 Mya)
-get.disparity(disparity, observed = FALSE, subsets = 2, rarefaction = 10)
    +
    ## Extracting the observed disparity (default)
+get.disparity(disparity)
+
+## Extracting the disparity from the bootstrapped values from the
+## 10th rarefaction level from the second subsets (80 Mya)
+get.disparity(disparity, observed = FALSE, subsets = 2, rarefaction = 10)
    -
    -

    7.2.1.9 rescale.dispRity

    +
    +

    7.2.1.10 scale.dispRity

    This is the modified S3 method for scale (scaling and/or centring) that can be applied to the disparity data of a dispRity object and can take optional arguments (for example the rescaling by dividing by a maximum value).

    -
    ## Getting the disparity values of the time subsets
-head(summary(disparity))
-
-## Scaling the same disparity values
-head(summary(rescale.dispRity(disparity, scale = TRUE)))
-
-## Scaling and centering:
-head(summary(rescale.dispRity(disparity, scale = TRUE, center = TRUE)))
-
-## Rescaling the value by dividing by a maximum value
-head(summary(rescale.dispRity(disparity, max = 10)))
    +
    ## Getting the disparity values of the time subsets
+head(summary(disparity))
+
+## Scaling the same disparity values
+head(summary(scale.dispRity(disparity, scale = TRUE)))
+
+## Scaling and centering:
+head(summary(scale.dispRity(disparity, scale = TRUE, center = TRUE)))
+
+## Rescaling the value by dividing by a maximum value
+head(summary(scale.dispRity(disparity, max = 10)))
    -
    -

    7.2.1.10 sort.dispRity

    +
    +

    7.2.1.11 sort.dispRity

    This is the S3 method of sort for sorting the subsets alphabetically (default) or following a specific pattern.

    -
    ## Sorting the disparity subsets in inverse alphabetic order
-head(summary(sort(disparity, decreasing = TRUE)))
-
-## Customised sorting
-head(summary(sort(disparity, sort = c(7, 1, 3, 4, 5, 2, 6))))
    +
    ## Sorting the disparity subsets in inverse alphabetic order
+head(summary(sort(disparity, decreasing = TRUE)))
+
+## Customised sorting
+head(summary(sort(disparity, sort = c(7, 1, 3, 4, 5, 2, 6))))
    -
    -

    7.2.1.11 get.tree add.tree and remove.tree

    +
    +

    7.2.1.12 get.tree add.tree and remove.tree

    These functions allow to manipulate the potential tree components of dispRity objects.

    -
    ## Getting the tree component of a dispRity object
-get.tree(disparity)
-
-## Removing the tree
-remove.tree(disparity)
-
-## Adding a tree
-add.tree(disparity, tree = BeckLee_tree)
    +
    ## Getting the tree component of a dispRity object
+get.tree(disparity)
+
+## Removing the tree
+remove.tree(disparity)
+
+## Adding a tree
+add.tree(disparity, tree = BeckLee_tree)
    +

    Note that get.tree can also be used to extract trees from different subsets (custom or continuous/discrete subsets).

    +

    For example, if we have three time bins like in the example below we have three time bins and we can extract the subtrees for these three time bins in different ways using the option subsets and to.root:

    +
    ## Load the Beck & Lee 2014 data
+data(BeckLee_tree) ; data(BeckLee_mat99) ; data(BeckLee_ages)
+
+## Time binning (discrete method)
+## Generate two discrete time bins from 120 to 40 Ma every 20 Ma
+time_bins <- chrono.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
+                            method = "discrete", time = c(120, 100, 80, 60),
+                            inc.nodes = TRUE, FADLAD = BeckLee_ages)
+
+## Getting the subtrees all the way to the root
+root_subsets <- get.tree(time_bins, subsets = TRUE)
+
+## Plotting the bin contents
+old_par <- par(mfrow = c(2,2))
+plot(BeckLee_tree, main = "original tree", show.tip.label = FALSE)
+axisPhylo()
+abline(v = BeckLee_tree$root.time - c(120, 100, 80, 60))
+for(i in 1:3) {
+     plot(root_subsets[[i]], main = names(root_subsets)[i],
+          show.tip.label = FALSE)
+     axisPhylo()
+}
    +

    +
    par(old_par)
    +

    But we can also extract the subtrees containing only branch lengths for the actual bins using to.root = FALSE:

    +
    ## Getting the subtrees all the way to the root
+bin_subsets <- get.tree(time_bins, subsets = TRUE, to.root = FALSE)
+
+## Plotting the bin contents
+old_par <- par(mfrow = c(2,2))
+plot(BeckLee_tree, main = "original tree", show.tip.label = FALSE)
+axisPhylo()
+abline(v = BeckLee_tree$root.time - c(120, 100, 80, 60))
+for(i in 1:3) {
+     plot(bin_subsets[[i]], main = names(bin_subsets)[i],
+          show.tip.label = FALSE)
+     axisPhylo()
+}
    +

    +
    par(old_par)
    +

    This can be useful for example for calculating the branch lengths in each bin:

    +
    ## How many cumulated phylogenetic diversity in each bin?
+lapply(bin_subsets, function(tree) sum(tree$edge.length))
    +
    ## $`120 - 100`
+## [1] 189.2799
+## 
+## $`100 - 80`
+## [1] 341.7199
+## 
+## $`80 - 60`
+## [1] 426.7493
    @@ -590,7 +683,7 @@

    7.3.4 $disparity

    - +
    @@ -650,7 +743,7 @@

    7.3.4 $disparity patch level 5 -L3 programming layer <2022-07-15> (/usr/local/texlive/2022/texmf-dist/tex/latex -/base/book.cls +LaTeX2e <2021-11-15> patch level 1 +L3 programming layer <2022-01-21> (/usr/share/texlive/texmf-dist/tex/latex/base +/book.cls Document Class: book 2021/10/04 v1.4n Standard LaTeX document class -(/usr/local/texlive/2022/texmf-dist/tex/latex/base/bk10.clo +(/usr/share/texlive/texmf-dist/tex/latex/base/bk10.clo File: bk10.clo 2021/10/04 v1.4n Standard LaTeX file (size option) ) \c@part=\count181 @@ -23,8 +23,8 @@ File: bk10.clo 2021/10/04 v1.4n Standard LaTeX file (size option) \abovecaptionskip=\skip47 \belowcaptionskip=\skip48 \bibindent=\dimen138 -) (/usr/local/texlive/2022/texmf-dist/tex/latex/lm/lmodern.sty -Package: lmodern 2015/05/01 v1.6.1 Latin Modern Fonts +) (/usr/share/texmf/tex/latex/lm/lmodern.sty +Package: lmodern 2009/10/30 v1.6 Latin Modern Fonts LaTeX Font Info: Overwriting symbol font `operators' in version `normal' (Font) OT1/cmr/m/n --> OT1/lmr/m/n on input line 22. 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Redefining \cite on input line 694. -\c@NAT@ctr=\count315 +\c@NAT@ctr=\count317 ) (./dispRity_manual.aux) \openout1 = `dispRity_manual.aux'. @@ -742,12 +724,23 @@ Package microtype Info: Using protrusion set `basicmath'. 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dispRity_manual_files/figure-latex/unnamed-chunk-13-1.pdf Graphic file (t ype pdf) -Underfull \vbox (badness 1502) has occurred while \output is active [] +Underfull \vbox (badness 10000) has occurred while \output is active [] [18] Overfull \hbox (180.0pt too wide) in paragraph at lines 719--719 @@ -1021,13 +1014,15 @@ Overfull \hbox (180.0pt too wide) in paragraph at lines 719--719 File: dispRity_manual_files/figure-latex/unnamed-chunk-16-1.pdf Graphic file (t ype pdf) -[20] + +Underfull \vbox (badness 1194) has occurred while \output is active [] + +[20] [21] Overfull \hbox (38.25pt too wide) in paragraph at lines 829--829 []\TU/lmtt/m/n/10 ## 2 customised subsets for 50 elements in one matrix with 48 dimensions:[] [] -[21] File: dispRity_manual_files/figure-latex/unnamed-chunk-18-1.pdf Graphic file (t ype pdf) @@ -1066,672 +1061,764 @@ ne matrix with 1 phylogenetic tree[] [] [28] [29] -Overfull \hbox (17.25pt too wide) in paragraph at lines 1189--1189 +Overfull \hbox (17.25pt too wide) in paragraph at lines 1191--1191 []\TU/lmtt/m/n/10 ## Data was bootstrapped 20 times (method:"full") and fully r arefied.[] [] -Overfull \hbox (106.5pt too wide) in paragraph at lines 1203--1203 +Overfull \hbox (106.5pt too wide) in paragraph at lines 1205--1205 []\TU/lmtt/m/n/10 ## Data was bootstrapped 20 times (method:"full") and rarefie d to 6, 7, 8, 3 elements.[] [] [30] -Overfull \hbox (38.25pt too wide) in paragraph at lines 1275--1275 +Overfull \hbox (38.25pt too wide) in paragraph at lines 1277--1277 []\TU/lmtt/m/n/10 ## 2 customised subsets for 50 elements in one matrix with 48 dimensions:[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 1275--1275 +Overfull \hbox (22.5pt too wide) in paragraph at lines 1277--1277 []\TU/lmtt/m/n/10 ## Data was bootstrapped 200 times (method:"full") and fully rarefied.[] [] [31] -Overfull \hbox (253.5pt too wide) in paragraph at lines 1297--1297 +Overfull \hbox (253.5pt too wide) in paragraph at lines 1299--1299 []\TU/lmtt/m/n/10 ## 4 continuous (proximity) time subsets for 99 elements in o ne matrix with 97 dimensions with 1 phylogenetic tree[] [] File: dispRity_fun.png Graphic file (type bmp) + +Underfull \vbox (badness 3240) has occurred while \output is active [] + [32] -Underfull \hbox (badness 2600) in paragraph at lines 1388--1389 +Underfull \hbox (badness 2600) in paragraph at lines 1390--1391 []\TU/lmr/m/n/10 Several dimension-level 2 functions are implemented in \TU/lmt t/m/n/10 dispRity \TU/lmr/m/n/10 (see [] - -Underfull \vbox (badness 1412) has occurred while \output is active [] - [33] [34] -Overfull \hbox (27.75pt too wide) in paragraph at lines 1487--1487 +Overfull \hbox (27.75pt too wide) in paragraph at lines 1489--1489 []\TU/lmtt/m/n/10 ## 2 customised subsets for 8 elements in one matrix with 3 d imensions:[] [] [35] [36] -Overfull \hbox (12.0pt too wide) in paragraph at lines 1615--1615 +Overfull \hbox (12.0pt too wide) in paragraph at lines 1617--1617 []\TU/lmtt/m/n/10 ## Additional dimension-level 2 and/or 1 function(s) will be needed.[] [] [37] -Overfull \hbox (1.11748pt too wide) in paragraph at lines 1724--1725 +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[38] +Overfull \hbox (1.11748pt too wide) in paragraph at lines 1726--1727 []\TU/lmr/m/n/10 Name| [] -Overfull \hbox (49.34747pt too wide) in paragraph at lines 1735--1736 +Overfull \hbox (49.34747pt too wide) in paragraph at lines 1737--1738 []\TU/lmtt/m/n/10 ancestral.dist| [] -Overfull \hbox (7.4979pt too wide) in paragraph at lines 1739--1740 +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1741--1742 []\TU/lmtt/m/n/10 dispRity| [] -Overfull \hbox (7.34747pt too wide) in paragraph at lines 1744--1745 +Overfull \hbox (7.34747pt too wide) in paragraph at lines 1746--1747 []\TU/lmtt/m/n/10 angles| [] -Overfull \hbox (7.4979pt too wide) in paragraph at lines 1748--1749 +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1750--1751 []\TU/lmtt/m/n/10 dispRity| [] -Overfull \hbox (28.09747pt too wide) in paragraph at lines 1753--1754 +Overfull \hbox (28.09747pt too wide) in paragraph at lines 1755--1756 []\TU/lmtt/m/n/10 centroids\TU/lmr/m/n/10 1| [] -Overfull \hbox (7.4979pt too wide) in paragraph at lines 1757--1758 +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1759--1760 []\TU/lmtt/m/n/10 dispRity| [] -Overfull \hbox (59.84747pt too wide) in paragraph at lines 1762--1763 +Overfull \hbox (59.84747pt too wide) in paragraph at lines 1764--1765 []\TU/lmtt/m/n/10 convhull.surface| [] -Overfull \hbox (91.4979pt too wide) in paragraph at lines 1766--1767 +Overfull \hbox (91.4979pt too wide) in paragraph at lines 1768--1769 [][]\TU/lmtt/m/n/10 geometry[]::convhulln$area| [] -Overfull \hbox (54.59747pt too wide) in paragraph at lines 1771--1772 +Overfull \hbox (54.59747pt too wide) in paragraph at lines 1773--1774 []\TU/lmtt/m/n/10 convhull.volume| [] -Overfull \hbox (86.2479pt too wide) in paragraph at lines 1775--1776 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paragraph at lines 1847--1848 +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1849--1850 []\TU/lmtt/m/n/10 dispRity [] -Overfull \hbox (33.7479pt too wide) in paragraph at lines 1847--1848 +Overfull \hbox (33.7479pt too wide) in paragraph at lines 1849--1850 []\TU/lmtt/m/n/10 FD[]::dbFD$FEve [] -Overfull \hbox (16.0679pt too wide) in paragraph at lines 1847--1848 +Overfull \hbox (16.0679pt too wide) in paragraph at lines 1849--1850 \TU/lmr/m/n/10 abundance)| [] -Overfull \hbox (28.34747pt too wide) in paragraph at lines 1852--1853 +Overfull \hbox (28.34747pt too wide) in paragraph at lines 1854--1855 []\TU/lmtt/m/n/10 group.dist| [] -Overfull \hbox (7.4979pt too wide) in paragraph at lines 1856--1857 +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1858--1859 []\TU/lmtt/m/n/10 dispRity| [] -Overfull \hbox (17.84747pt too wide) in paragraph at lines 1861--1862 +Overfull \hbox (17.84747pt too wide) in paragraph at lines 1863--1864 []\TU/lmtt/m/n/10 mode.val| [] 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\hbox (75.59747pt too wide) in paragraph at lines 1917--1918 []\TU/lmtt/m/n/10 projections.between| [] -Overfull \hbox (7.4979pt too wide) in paragraph at lines 1919--1920 +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1921--1922 []\TU/lmtt/m/n/10 dispRity| [] -Overfull \hbox (59.84747pt too wide) in paragraph at lines 1924--1925 +Overfull \hbox (59.84747pt too wide) in paragraph at lines 1926--1927 []\TU/lmtt/m/n/10 projections.tree| [] -Overfull \hbox (7.4979pt too wide) in paragraph at lines 1928--1929 +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1930--1931 []\TU/lmtt/m/n/10 dispRity| [] -Overfull \hbox (23.09747pt too wide) in paragraph at lines 1933--1934 +Overfull \hbox (23.09747pt too wide) in paragraph at lines 1935--1936 []\TU/lmtt/m/n/10 quantiles| [] -Overfull \hbox (7.4979pt too wide) in paragraph at lines 1937--1938 +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1939--1940 []\TU/lmtt/m/n/10 dispRity| [] -Overfull \hbox (7.34747pt too wide) in paragraph at lines 1942--1943 +Overfull \hbox (7.34747pt too wide) in paragraph at lines 1944--1945 []\TU/lmtt/m/n/10 radius| [] -Overfull \hbox (7.4979pt too wide) in paragraph at lines 1946--1947 +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1948--1949 []\TU/lmtt/m/n/10 dispRity| [] -Overfull \hbox (7.34747pt too wide) in paragraph at lines 1951--1952 +Overfull \hbox (7.34747pt too wide) in paragraph at lines 1953--1954 []\TU/lmtt/m/n/10 ranges| [] -Overfull \hbox (7.4979pt too wide) in paragraph at lines 1955--1956 +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1957--1958 +[]\TU/lmtt/m/n/10 dispRity| + [] + + +Overfull \hbox (23.09747pt too wide) in paragraph at lines 1962--1963 +[]\TU/lmtt/m/n/10 roundness| + [] + + +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1966--1967 []\TU/lmtt/m/n/10 dispRity| [] -Overfull \hbox (59.84747pt too wide) in paragraph at lines 1960--1961 +Overfull \hbox (59.84747pt too wide) in paragraph at lines 1971--1972 []\TU/lmtt/m/n/10 span.tree.length| [] -Overfull \hbox (44.2479pt too wide) in paragraph at lines 1964--1965 +Overfull \hbox (44.2479pt too wide) in paragraph at lines 1975--1976 [][]\TU/lmtt/m/n/10 vegan[]::spantree| [] -Overfull \hbox (23.09747pt too wide) in paragraph at lines 1969--1970 +Overfull \hbox (23.09747pt too wide) in paragraph at lines 1980--1981 []\TU/lmtt/m/n/10 variances| [] -Overfull \hbox (7.4979pt too wide) in paragraph at lines 1973--1974 +Overfull \hbox (7.4979pt too wide) in paragraph at lines 1984--1985 []\TU/lmtt/m/n/10 dispRity| [] -[38] [39] -Underfull \hbox (badness 4013) in paragraph at lines 1978--1980 +[39] +Underfull \hbox (badness 4013) in paragraph at lines 1989--1991 \TU/lmtt/m/n/10 centroids(space, centroid = rep(0, ncol(space)))\TU/lmr/m/n/10 , for example the [] [40] [41] [42] -Underfull \hbox (badness 1371) in paragraph at lines 2138--2138 +Underfull \hbox (badness 1371) in paragraph at lines 2153--2153 []\TU/lmr/bx/n/10 Ranges, variances, quantiles, radius, pairwise distance, [] -[43] -Underfull \hbox (badness 1688) in paragraph at lines 2323--2324 +[43] [44] +Underfull \hbox (badness 1688) in paragraph at lines 2338--2339 []\TU/lmr/m/n/10 The pairwise distances and the neighbours distances uses the f unction [] -[44] -Underfull \vbox (badness 2334) has occurred while \output is active [] - [45] -Underfull \hbox (badness 2229) in paragraph at lines 2467--2469 +Underfull \hbox (badness 2229) in paragraph at lines 2482--2484 []\TU/lmr/m/n/10 If you have subsets in your \TU/lmtt/m/n/10 dispRity \TU/lmr/m /n/10 object, you can also use the [] [46] [47] -Underfull \hbox (badness 4660) in paragraph at lines 2576--2579 +Underfull \hbox (badness 4660) in paragraph at lines 2591--2594 \TU/lmr/m/n/10 The \TU/lmtt/m/n/10 func.div \TU/lmr/m/n/10 and \TU/lmtt/m/n/10 func.eve \TU/lmr/m/n/10 functions are based on the \TU/lmtt/m/n/10 FD::dpFD [] -Underfull \hbox (badness 4713) in paragraph at lines 2576--2579 +Underfull \hbox (badness 4713) in paragraph at lines 2591--2594 \TU/lmr/m/n/10 package. They are the equivalent to \TU/lmtt/m/n/10 FD::dpFD(mat rix)$FDiv \TU/lmr/m/n/10 and [] -Underfull \hbox (badness 2538) in paragraph at lines 2576--2579 +Underfull \hbox (badness 2538) in paragraph at lines 2591--2594 \TU/lmtt/m/n/10 FD::dpFD(matrix)$FEve \TU/lmr/m/n/10 but a bit faster (since th ey don’t deal with [] -[48] [49] [50] -Overfull \hbox (85.5pt too wide) in paragraph at lines 2793--2793 +[48] [49] [50] [51] +Overfull \hbox (85.5pt too wide) in paragraph at lines 2808--2808 []\TU/lmtt/m/n/10 ## Warning in max(nchar(round(column)), na.rm = TRUE): no non -missing arguments to[] [] -[51] -Overfull \hbox (85.5pt too wide) in paragraph at lines 2793--2793 + +Overfull \hbox (85.5pt too wide) in paragraph at lines 2808--2808 []\TU/lmtt/m/n/10 ## Warning in max(nchar(round(column)), na.rm = TRUE): no non -missing arguments to[] [] -[52] [53] -Overfull \hbox (12.0pt too wide) in paragraph at lines 2987--2987 + +Overfull \hbox (64.5pt too wide) in paragraph at lines 2851--2851 +[]\TU/lmtt/m/n/10 ## Warning in snapshot3d(scene = x, width = width, height = h +eight): webshot =[] + [] + + +Overfull \hbox (64.5pt too wide) in paragraph at lines 2851--2851 +[]\TU/lmtt/m/n/10 ## TRUE requires the webshot2 package and Chrome browser; usi +ng rgl.snapshot()[] + [] + +[52] +Overfull \hbox (85.5pt too wide) in paragraph at lines 2856--2856 +[]\TU/lmtt/m/n/10 ## Warning in rgl.snapshot(filename, fmt, top): this build of + rgl does not support[] + [] + +File: ../../../../../../tmp/RtmpuRA2JU/file80cb6a29f05b.png Graphic file (type +bmp) +<../../../../../../tmp/RtmpuRA2JU/file80cb6a29f05b.png> + +Overfull \hbox (64.5pt too wide) in paragraph at lines 2864--2864 +[]\TU/lmtt/m/n/10 ## Warning in snapshot3d(scene = x, width = width, height = h +eight): webshot =[] + [] + + +Overfull \hbox (64.5pt too wide) in paragraph at lines 2864--2864 +[]\TU/lmtt/m/n/10 ## TRUE requires the webshot2 package and Chrome browser; usi +ng rgl.snapshot()[] + [] + + +Overfull \hbox (85.5pt too wide) in paragraph at lines 2869--2869 +[]\TU/lmtt/m/n/10 ## Warning in rgl.snapshot(filename, fmt, top): this build of + rgl does not support[] + [] + +File: ../../../../../../tmp/RtmpuRA2JU/file80cb29a4e334.png Graphic file (type +bmp) +<../../../../../../tmp/RtmpuRA2JU/file80cb29a4e334.png> + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[53] +Overfull \hbox (64.5pt too wide) in paragraph at lines 2877--2877 +[]\TU/lmtt/m/n/10 ## Warning in snapshot3d(scene = x, width = width, height = h +eight): webshot =[] + [] + + +Overfull \hbox (64.5pt too wide) in paragraph at lines 2877--2877 +[]\TU/lmtt/m/n/10 ## TRUE requires the webshot2 package and Chrome browser; usi +ng rgl.snapshot()[] + [] + + +Overfull \hbox (85.5pt too wide) in paragraph at lines 2882--2882 +[]\TU/lmtt/m/n/10 ## Warning in rgl.snapshot(filename, fmt, top): this build of + rgl does not support[] + [] + +File: ../../../../../../tmp/RtmpuRA2JU/file80cb4a93cfcb.png Graphic file (type +bmp) +<../../../../../../tmp/RtmpuRA2JU/file80cb4a93cfcb.png> +File: dispRity_manual_files/figure-latex/unnamed-chunk-69-1.pdf Graphic file (t +ype pdf) + + +Overfull \hbox (291.31003pt too wide) in paragraph at lines 2884--2886 +[][] [] + [] + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[54] [55] [56] +Overfull \hbox (12.0pt too wide) in paragraph at lines 3049--3049 []\TU/lmtt/m/n/10 ## subsets n_1 n_2 obs.median 2.5% 25% 75 % 97.5%[] [] -Overfull \hbox (12.0pt too wide) in paragraph at lines 2987--2987 +Overfull \hbox (12.0pt too wide) in paragraph at lines 3049--3049 []\TU/lmtt/m/n/10 ## 1 gulls:phylogeny 159 359 8.25 2.101 6.25 14.9 8 41.8[] [] -[54] -Overfull \hbox (12.0pt too wide) in paragraph at lines 2987--2987 -[]\TU/lmtt/m/n/10 ## 2 plovers:phylogeny 98 359 33.75 5.700 16.33 83.0 -4 130.4[] +[57] +Overfull \hbox (12.0pt too wide) in paragraph at lines 3049--3049 +[]\TU/lmtt/m/n/10 ## 2 plovers:phylogeny 98 359 33.75 5.700 16.33 75.5 +0 131.5[] [] -Overfull \hbox (12.0pt too wide) in paragraph at lines 2987--2987 +Overfull \hbox (12.0pt too wide) in paragraph at lines 3049--3049 []\TU/lmtt/m/n/10 ## 3 sandpipers:phylogeny 102 359 10.79 3.876 8.10 16.5 9 95.9[] [] -Overfull \hbox (6.75pt too wide) in paragraph at lines 3010--3010 -[]\TU/lmtt/m/n/10 ## subsets n_1 n_2 obs.median 2.5% 25% 75% - 97.5%[] +Overfull \hbox (12.0pt too wide) in paragraph at lines 3072--3072 +[]\TU/lmtt/m/n/10 ## subsets n_1 n_2 obs.median 2.5% 25% 75 +% 97.5%[] [] -Overfull \hbox (6.75pt too wide) in paragraph at lines 3010--3010 -[]\TU/lmtt/m/n/10 ## 1 gulls:phylogeny 159 359 0.002 0 0.001 0.002 - 0.007[] +Overfull \hbox (12.0pt too wide) in paragraph at lines 3072--3072 +[]\TU/lmtt/m/n/10 ## 1 gulls:phylogeny 159 359 0.003 0.001 0.002 0.00 +5 0.015[] [] -Overfull \hbox (6.75pt too wide) in paragraph at lines 3010--3010 -[]\TU/lmtt/m/n/10 ## 2 plovers:phylogeny 98 359 0.000 0 0.000 0.001 - 0.003[] +Overfull \hbox (12.0pt too wide) in paragraph at lines 3072--3072 +[]\TU/lmtt/m/n/10 ## 2 plovers:phylogeny 98 359 0.001 0.000 0.001 0.00 +1 0.006[] [] -Overfull \hbox (6.75pt too wide) in paragraph at lines 3010--3010 -[]\TU/lmtt/m/n/10 ## 3 sandpipers:phylogeny 102 359 0.001 0 0.000 0.001 - 0.005[] +Overfull \hbox (12.0pt too wide) in paragraph at lines 3072--3072 +[]\TU/lmtt/m/n/10 ## 3 sandpipers:phylogeny 102 359 0.002 0.000 0.001 0.00 +3 0.009[] [] Underfull \vbox (badness 3861) has occurred while \output is active [] -[55] -Overfull \hbox (127.5pt too wide) in paragraph at lines 3069--3069 +[58] +Overfull \hbox (127.5pt too wide) in paragraph at lines 3131--3131 []\TU/lmtt/m/n/10 ## The test was run on the random, size shifts for 3 replicat es using the following model:[] [] -Overfull \hbox (54.0pt too wide) in paragraph at lines 3087--3087 -[]\TU/lmtt/m/n/10 ## 10% 20% 30% 40% 50% 60% 70% 80% 90% 10 -0% slope[] +Overfull \hbox (80.25pt too wide) in paragraph at lines 3149--3149 +[]\TU/lmtt/m/n/10 ## 10% 20% 30% 40% 50% 60% 70% 80% 9 +0% 100% slope[] [] -Overfull \hbox (54.0pt too wide) in paragraph at lines 3087--3087 -[]\TU/lmtt/m/n/10 ## random 0.84 0.88 0.94 0.95 0.96 0.98 0.97 0.98 0.96 0. -98 1.450100e-03[] +Overfull \hbox (80.25pt too wide) in paragraph at lines 3149--3149 +[]\TU/lmtt/m/n/10 ## random 0.84 0.88 0.94 0.95 0.96 0.98 0.97 0.98 0. +96 0.98 1.450100e-03[] [] -Overfull \hbox (54.0pt too wide) in paragraph at lines 3087--3087 -[]\TU/lmtt/m/n/10 ## size.inner 0.10 0.21 0.31 0.45 0.54 0.70 0.78 0.94 0.96 0. -98 1.054925e-02[] +Overfull \hbox (80.25pt too wide) in paragraph at lines 3149--3149 +[]\TU/lmtt/m/n/10 ## size.increase 0.10 0.21 0.31 0.45 0.54 0.70 0.78 0.94 0. +96 0.98 1.054925e-02[] [] -Overfull \hbox (54.0pt too wide) in paragraph at lines 3087--3087 -[]\TU/lmtt/m/n/10 ## size.outer 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0. -98 1.453782e-05[] +Overfull \hbox (80.25pt too wide) in paragraph at lines 3149--3149 +[]\TU/lmtt/m/n/10 ## size.hollowness 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0. +98 0.98 1.453782e-05[] [] -[56] -File: dispRity_manual_files/figure-latex/unnamed-chunk-72-1.pdf Graphic file (t +[59] +File: dispRity_manual_files/figure-latex/unnamed-chunk-77-1.pdf Graphic file (t ype pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[57] [58] [59] [60] -Overfull \hbox (253.5pt too wide) in paragraph at lines 3230--3230 +[60] [61] [62] [63] +Overfull \hbox (253.5pt too wide) in paragraph at lines 3292--3292 []\TU/lmtt/m/n/10 ## 4 continuous (proximity) time subsets for 99 elements in o ne matrix with 97 dimensions with 1 phylogenetic tree[] [] -[61] -File: dispRity_manual_files/figure-latex/unnamed-chunk-78-1.pdf Graphic file (t +[64] +File: dispRity_manual_files/figure-latex/unnamed-chunk-83-1.pdf Graphic file (t ype pdf) - -[62] -File: dispRity_manual_files/figure-latex/unnamed-chunk-79-1.pdf Graphic file (t + + +Underfull \vbox (badness 1565) has occurred while \output is active [] + +[65] +File: dispRity_manual_files/figure-latex/unnamed-chunk-84-1.pdf Graphic file (t ype pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[63] -Underfull \vbox (badness 1655) has occurred while \output is active [] +[66] +Underfull \vbox (badness 1057) detected at line 3413 + [] -[64] -File: dispRity_manual_files/figure-latex/unnamed-chunk-80-1.pdf Graphic file (t +[67] +File: dispRity_manual_files/figure-latex/unnamed-chunk-85-1.pdf Graphic file (t ype pdf) - -[65] -File: dispRity_manual_files/figure-latex/unnamed-chunk-81-1.pdf Graphic file (t + +[68] +File: dispRity_manual_files/figure-latex/unnamed-chunk-86-1.pdf Graphic file (t ype pdf) - -[66] -File: dispRity_manual_files/figure-latex/unnamed-chunk-82-1.pdf Graphic file (t + +File: dispRity_manual_files/figure-latex/unnamed-chunk-87-1.pdf Graphic file (t ype pdf) - -[67] -File: dispRity_manual_files/figure-latex/unnamed-chunk-83-1.pdf Graphic file (t + +[69] [70] +File: dispRity_manual_files/figure-latex/unnamed-chunk-88-1.pdf Graphic file (t ype pdf) - -[68] -File: dispRity_manual_files/figure-latex/unnamed-chunk-84-1.pdf Graphic file (t + +[71] +File: dispRity_manual_files/figure-latex/unnamed-chunk-89-1.pdf Graphic file (t ype pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[69] [70] -File: dispRity_manual_files/figure-latex/unnamed-chunk-85-1.pdf Graphic file (t +[72] +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[73] +File: dispRity_manual_files/figure-latex/unnamed-chunk-90-1.pdf Graphic file (t ype pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[71] [72] -Overfull \hbox (17.25pt too wide) in paragraph at lines 3613--3613 +[74] [75] +Overfull \hbox (17.25pt too wide) in paragraph at lines 3675--3675 []\TU/lmtt/m/n/10 ## alternative hypothesis: true difference in means is not eq ual to 0[] [] -Overfull \vbox (1.28995pt too high) detected at line 3622 +Overfull \vbox (1.34995pt too high) detected at line 3684 [] -[73] [74] -Overfull \hbox (54.0pt too wide) in paragraph at lines 3694--3694 +[76] [77] +Overfull \hbox (54.0pt too wide) in paragraph at lines 3756--3756 []\TU/lmtt/m/n/10 ## Warning: custom.subsets is applied on what seems to be a d istance matrix.[] [] -[75] -Overfull \hbox (54.0pt too wide) in paragraph at lines 3737--3737 +[78] +Overfull \hbox (54.0pt too wide) in paragraph at lines 3799--3799 []\TU/lmtt/m/n/10 ## Warning: custom.subsets is applied on what seems to be a d istance matrix.[] [] -Overfull \hbox (6.75pt too wide) in paragraph at lines 3758--3758 +Overfull \hbox (6.75pt too wide) in paragraph at lines 3820--3820 []\TU/lmtt/m/n/10 ## vegan::adonis2(formula = matrix ~ g1 + g2, method = "eucli dean")[] [] -Overfull \hbox (211.5pt too wide) in paragraph at lines 3781--3781 +Overfull \hbox (211.5pt too wide) in paragraph at lines 3843--3843 []\TU/lmtt/m/n/10 ## Warning in adonis.dispRity(time_subsets): The input data f or adonis.dispRity was not a distance matrix.[] [] -Overfull \hbox (190.5pt too wide) in paragraph at lines 3781--3781 +Overfull \hbox (190.5pt too wide) in paragraph at lines 3843--3843 []\TU/lmtt/m/n/10 ## The results are thus based on the distance matrix for the input data (i.e. dist(data$matrix[[1]])).[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 3796--3796 +Overfull \hbox (22.5pt too wide) in paragraph at lines 3858--3858 []\TU/lmtt/m/n/10 ## vegan::adonis2(formula = dist(matrix) ~ time, method = "eu clidean")[] [] -[76] -Overfull \hbox (342.75pt too wide) in paragraph at lines 3814--3814 +[79] +Overfull \hbox (342.75pt too wide) in paragraph at lines 3876--3876 []\TU/lmtt/m/n/10 ## Warning in adonis.dispRity(time_subsets, matrix ~ chrono.s ubsets): The input data for adonis.dispRity was not a distance matrix.[] [] -Overfull \hbox (190.5pt too wide) in paragraph at lines 3814--3814 +Overfull \hbox (190.5pt too wide) in paragraph at lines 3876--3876 []\TU/lmtt/m/n/10 ## The results are thus based on the distance matrix for the input data (i.e. dist(data$matrix[[1]])).[] [] -Overfull \hbox (75.0pt too wide) in paragraph at lines 3830--3830 +Overfull \hbox (75.0pt too wide) in paragraph at lines 3892--3892 []\TU/lmtt/m/n/10 ## vegan::adonis2(formula = dist(matrix) ~ chrono.subsets, me thod = "euclidean")[] [] -[77] -Overfull \hbox (75.0pt too wide) in paragraph at lines 3864--3864 +[80] +Overfull \hbox (75.0pt too wide) in paragraph at lines 3926--3926 []\TU/lmtt/m/n/10 ## Warning in dtt.dispRity(data = geiger_data$dat, metric = c (sum, variances), :[] [] -File: dispRity_manual_files/figure-latex/unnamed-chunk-91-1.pdf Graphic file (t +File: dispRity_manual_files/figure-latex/unnamed-chunk-96-1.pdf Graphic file (t ype pdf) - - -Overfull \hbox (90.75pt too wide) in paragraph at lines 3899--3899 -[]\TU/lmtt/m/n/10 ## Warning in check.dispRity.data(data): Row names have been -automatically added to[] + +[81] +Overfull \hbox (59.25pt too wide) in paragraph at lines 3961--3961 +[]\TU/lmtt/m/n/10 ## Warning in check.data(data, match_call): Row names have be +en automatically[] [] -[78] -File: dispRity_manual_files/figure-latex/unnamed-chunk-93-1.pdf Graphic file (t +File: dispRity_manual_files/figure-latex/unnamed-chunk-98-1.pdf Graphic file (t ype pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[79] [80] -Overfull \hbox (38.25pt too wide) in paragraph at lines 3973--3973 +[82] [83] +Overfull \hbox (38.25pt too wide) in paragraph at lines 4035--4035 []\TU/lmtt/m/n/10 ## Evidence of equal variance (Bartlett[]s test of equal vari ances p = 0).[] [] -Overfull \hbox (38.25pt too wide) in paragraph at lines 4010--4010 +Overfull \hbox (38.25pt too wide) in paragraph at lines 4072--4072 []\TU/lmtt/m/n/10 ## Evidence of equal variance (Bartlett[]s test of equal vari ances p = 0).[] [] -Overfull \hbox (101.25pt too wide) in paragraph at lines 4027--4027 +Overfull \hbox (101.25pt too wide) in paragraph at lines 4089--4089 []\TU/lmtt/m/n/10 ## Call: model.test(data = BeckLee_disparity, model = "Stasis ", pool.variance = NULL)[] [] -[81] -Overfull \hbox (38.25pt too wide) in paragraph at lines 4066--4066 +[84] +Overfull \hbox (38.25pt too wide) in paragraph at lines 4128--4128 []\TU/lmtt/m/n/10 ## Evidence of equal variance (Bartlett[]s test of equal vari ances p = 0).[] [] -Overfull \hbox (33.0pt too wide) in paragraph at lines 4084--4084 +Overfull \hbox (33.0pt too wide) in paragraph at lines 4146--4146 []\TU/lmtt/m/n/10 ## Call: model.test(data = BeckLee_disparity, model = c("Stas is", "BM"))[] [] -[82] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4104--4104 +[85] +Overfull \hbox (80.25pt too wide) in paragraph at lines 4166--4166 []\TU/lmtt/m/n/10 ## aicc delta_aicc weight_aicc log.lik param theta.1 o mega ancestral state[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4104--4104 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4166--4166 []\TU/lmtt/m/n/10 ## Stasis 41 336 0 -18.7 2 3.629 0 .074 NA[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4104--4104 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4166--4166 []\TU/lmtt/m/n/10 ## BM -294 0 1 149.3 2 NA NA 3.267[] [] @@ -1739,75 +1826,75 @@ Overfull \hbox (80.25pt too wide) in paragraph at lines 4104--4104 File: dispRity_manual_files/figure-latex/plot1-1.pdf Graphic file (type pdf) -Overfull \hbox (38.25pt too wide) in paragraph at lines 4144--4144 +Overfull \hbox (38.25pt too wide) in paragraph at lines 4206--4206 []\TU/lmtt/m/n/10 ## Evidence of equal variance (Bartlett[]s test of equal vari ances p = 0).[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4165--4165 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4227--4227 []\TU/lmtt/m/n/10 ## aicc delta_aicc weight_aicc log.lik param theta.1 o mega ancestral state[] [] -[83] [84] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4165--4165 +[86] [87] +Overfull \hbox (80.25pt too wide) in paragraph at lines 4227--4227 []\TU/lmtt/m/n/10 ## Stasis 41 339.5 0.000 -18.7 2 3.629 0 .074 NA[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4165--4165 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4227--4227 []\TU/lmtt/m/n/10 ## BM -294 3.6 0.112 149.3 2 NA NA 3.267[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4165--4165 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4227--4227 []\TU/lmtt/m/n/10 ## OU -296 2.1 0.227 152.1 4 NA - NA 3.255[] + NA 3.254[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4165--4165 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4227--4227 []\TU/lmtt/m/n/10 ## Trend -298 0.0 0.661 152.1 3 NA NA 3.255[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4165--4165 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4227--4227 []\TU/lmtt/m/n/10 ## EB -246 51.7 0.000 126.3 3 NA NA 4.092[] [] File: dispRity_manual_files/figure-latex/plot2-1.pdf Graphic file (type pdf) -[85] -Overfull \hbox (85.5pt too wide) in paragraph at lines 4204--4204 + +Overfull \hbox (85.5pt too wide) in paragraph at lines 4266--4266 []\TU/lmtt/m/n/10 ## aicc delta_aicc weight_aicc lo g.lik param[] [] -Overfull \hbox (85.5pt too wide) in paragraph at lines 4204--4204 +Overfull \hbox (85.5pt too wide) in paragraph at lines 4266--4266 []\TU/lmtt/m/n/10 ## -298.000 0.000 0.661 15 2.100 3.000[] [] -Overfull \hbox (85.5pt too wide) in paragraph at lines 4204--4204 +Overfull \hbox (85.5pt too wide) in paragraph at lines 4266--4266 []\TU/lmtt/m/n/10 ## theta.1 omega ancestral state sigma sq uared alpha[] [] -Overfull \hbox (85.5pt too wide) in paragraph at lines 4204--4204 +Overfull \hbox (85.5pt too wide) in paragraph at lines 4266--4266 []\TU/lmtt/m/n/10 ## NA NA 3.255 0.001 NA[] [] - -Overfull \hbox (38.25pt too wide) in paragraph at lines 4236--4236 +[88] [89] +Overfull \hbox (38.25pt too wide) in paragraph at lines 4298--4298 []\TU/lmtt/m/n/10 ## Evidence of equal variance (Bartlett[]s test of equal vari ances p = 0).[] [] @@ -1815,82 +1902,82 @@ ances p = 0).[] File: dispRity_manual_files/figure-latex/plot3-1.pdf Graphic file (type pdf) -LaTeX Warning: Float too large for page by 19.72452pt on input line 4245. +LaTeX Warning: Float too large for page by 15.4363pt on input line 4307. -Overfull \hbox (80.25pt too wide) in paragraph at lines 4272--4272 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 []\TU/lmtt/m/n/10 ## aicc delta_aicc weight_aicc log.lik param theta.1 o mega ancestral state[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4272--4272 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 []\TU/lmtt/m/n/10 ## Trend -298 0.0 0.661 152.1 3 NA NA 3.255[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4272--4272 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 []\TU/lmtt/m/n/10 ## OU -296 2.1 0.227 152.1 4 NA - NA 3.255[] + NA 3.254[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4272--4272 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 []\TU/lmtt/m/n/10 ## BM -294 3.6 0.112 149.3 2 NA NA 3.267[] [] -[86] [87] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4272--4272 + +Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 []\TU/lmtt/m/n/10 ## EB -246 51.7 0.000 126.3 3 NA NA 4.092[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4272--4272 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 []\TU/lmtt/m/n/10 ## Stasis 41 339.5 0.000 -18.7 2 3.629 0 .074 NA[] [] -Overfull \hbox (75.0pt too wide) in paragraph at lines 4272--4272 +Overfull \hbox (75.0pt too wide) in paragraph at lines 4334--4334 []\TU/lmtt/m/n/10 ## sigma squared alpha optima.1 trend eb median p value lower p value[] [] -Overfull \hbox (75.0pt too wide) in paragraph at lines 4272--4272 -[]\TU/lmtt/m/n/10 ## Trend 0.001 NA NA 0.007 NA 0.977 -52248 0.977022977[] +Overfull \hbox (75.0pt too wide) in paragraph at lines 4334--4334 +[]\TU/lmtt/m/n/10 ## Trend 0.001 NA NA 0.007 NA 0.9780 +21978 0.9760240[] [] -Overfull \hbox (75.0pt too wide) in paragraph at lines 4272--4272 -[]\TU/lmtt/m/n/10 ## OU 0.001 0.001 10.89 NA NA 0.973 -02697 0.972027972[] +Overfull \hbox (75.0pt too wide) in paragraph at lines 4334--4334 +[]\TU/lmtt/m/n/10 ## OU 0.001 0.001 12.35 NA NA 0.9780 +21978 0.9770230[] [] -Overfull \hbox (75.0pt too wide) in paragraph at lines 4272--4272 -[]\TU/lmtt/m/n/10 ## BM 0.001 NA NA NA NA 0.162 -83716 0.137862138[] +Overfull \hbox (75.0pt too wide) in paragraph at lines 4334--4334 +[]\TU/lmtt/m/n/10 ## BM 0.001 NA NA NA NA 0.1438 +56144 0.1368631[] [] -Overfull \hbox (75.0pt too wide) in paragraph at lines 4272--4272 -[]\TU/lmtt/m/n/10 ## EB 0.000 NA NA NA -0.032 0.068 -93107 0.000999001[] +Overfull \hbox (75.0pt too wide) in paragraph at lines 4334--4334 +[]\TU/lmtt/m/n/10 ## EB 0.000 NA NA NA -0.032 0.0009 +99001 0.0000000[] [] -Overfull \hbox (75.0pt too wide) in paragraph at lines 4272--4272 -[]\TU/lmtt/m/n/10 ## Stasis NA NA NA NA NA 1.000 -00000 1.000000000[] +Overfull \hbox (75.0pt too wide) in paragraph at lines 4334--4334 +[]\TU/lmtt/m/n/10 ## Stasis NA NA NA NA NA 1.0000 +00000 0.9990010[] [] - -Overfull \hbox (38.25pt too wide) in paragraph at lines 4298--4298 +[90] [91] +Overfull \hbox (38.25pt too wide) in paragraph at lines 4360--4360 []\TU/lmtt/m/n/10 ## Evidence of equal variance (Bartlett[]s test of equal vari ances p = 0).[] [] @@ -1898,281 +1985,276 @@ ances p = 0).[] File: dispRity_manual_files/figure-latex/plot4-1.pdf Graphic file (type pdf) -LaTeX Warning: Float too large for page by 31.31526pt on input line 4307. +LaTeX Warning: Float too large for page by 27.4363pt on input line 4369. -Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4396--4396 []\TU/lmtt/m/n/10 ## aicc delta_aicc weight_aicc log.lik param theta.1 o mega ancestral state[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4396--4396 []\TU/lmtt/m/n/10 ## Trend -298 0.0 0.814 152.1 3 NA NA 3.255[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4396--4396 []\TU/lmtt/m/n/10 ## BM -294 3.6 0.138 149.3 2 NA NA 3.267[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4396--4396 []\TU/lmtt/m/n/10 ## OU -292 5.7 0.048 149.3 3 NA NA 3.267[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4396--4396 []\TU/lmtt/m/n/10 ## EB -246 51.7 0.000 126.3 3 NA NA 4.092[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4334--4334 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4396--4396 []\TU/lmtt/m/n/10 ## Stasis 41 339.5 0.000 -18.7 2 3.629 0 .074 NA[] [] -[88] [89] -Overfull \hbox (27.75pt too wide) in paragraph at lines 4334--4334 + +Overfull \hbox (27.75pt too wide) in paragraph at lines 4396--4396 []\TU/lmtt/m/n/10 ## sigma squared alpha trend eb median p value low er p value[] [] -Overfull \hbox (27.75pt too wide) in paragraph at lines 4334--4334 -[]\TU/lmtt/m/n/10 ## Trend 0.001 NA 0.007 NA 0.98351648 0 -.983016983[] +Overfull \hbox (27.75pt too wide) in paragraph at lines 4396--4396 +[]\TU/lmtt/m/n/10 ## Trend 0.001 NA 0.007 NA 0.984015984 + 0.9820180[] [] -Overfull \hbox (27.75pt too wide) in paragraph at lines 4334--4334 -[]\TU/lmtt/m/n/10 ## BM 0.001 NA NA NA 0.26473526 0 -.249750250[] +Overfull \hbox (27.75pt too wide) in paragraph at lines 4396--4396 +[]\TU/lmtt/m/n/10 ## BM 0.001 NA NA NA 0.256743257 + 0.2487512[] [] -Overfull \hbox (27.75pt too wide) in paragraph at lines 4334--4334 -[]\TU/lmtt/m/n/10 ## OU 0.001 0 NA NA 0.30469530 0 -.292707293[] +Overfull \hbox (27.75pt too wide) in paragraph at lines 4396--4396 +[]\TU/lmtt/m/n/10 ## OU 0.001 0 NA NA 0.293706294 + 0.2917083[] [] -Overfull \hbox (27.75pt too wide) in paragraph at lines 4334--4334 -[]\TU/lmtt/m/n/10 ## EB 0.000 NA NA -0.032 0.06943057 0 -.000999001[] +Overfull \hbox (27.75pt too wide) in paragraph at lines 4396--4396 +[]\TU/lmtt/m/n/10 ## EB 0.000 NA NA -0.032 0.000999001 + 0.0000000[] [] -Overfull \hbox (27.75pt too wide) in paragraph at lines 4334--4334 -[]\TU/lmtt/m/n/10 ## Stasis NA NA NA NA 0.99900100 0 -.999000999[] +Overfull \hbox (27.75pt too wide) in paragraph at lines 4396--4396 +[]\TU/lmtt/m/n/10 ## Stasis NA NA NA NA 0.999000999 + 0.9980020[] [] - -Overfull \hbox (38.25pt too wide) in paragraph at lines 4370--4370 +[92] [93] +Overfull \hbox (38.25pt too wide) in paragraph at lines 4432--4432 []\TU/lmtt/m/n/10 ## Evidence of equal variance (Bartlett[]s test of equal vari ances p = 0).[] [] -[90] File: dispRity_manual_files/figure-latex/plot5-1.pdf Graphic file (type pdf) -Overfull \hbox (17.25pt too wide) in paragraph at lines 4403--4403 +Overfull \hbox (17.25pt too wide) in paragraph at lines 4465--4465 []\TU/lmtt/m/n/10 ## aicc delta_aicc weight_aicc log.lik param ancestr al state[] [] -Overfull \hbox (17.25pt too wide) in paragraph at lines 4403--4403 -[]\TU/lmtt/m/n/10 ## Trend -298 0.000 0.635 152.1 3 +Overfull \hbox (17.25pt too wide) in paragraph at lines 4465--4465 +[]\TU/lmtt/m/n/10 ## Trend -298 0.000 0.657 152.1 3 3.255[] [] -Overfull \hbox (17.25pt too wide) in paragraph at lines 4403--4403 -[]\TU/lmtt/m/n/10 ## multi.OU -296 2.124 0.220 152.1 4 - 3.254[] +Overfull \hbox (17.25pt too wide) in paragraph at lines 4465--4465 +[]\TU/lmtt/m/n/10 ## multi.OU -296 2.456 0.193 152.0 4 + 3.253[] [] -Overfull \hbox (17.25pt too wide) in paragraph at lines 4403--4403 -[]\TU/lmtt/m/n/10 ## BM -294 3.550 0.108 149.3 2 +Overfull \hbox (17.25pt too wide) in paragraph at lines 4465--4465 +[]\TU/lmtt/m/n/10 ## BM -294 3.550 0.111 149.3 2 3.267[] [] -[91] -Overfull \hbox (17.25pt too wide) in paragraph at lines 4403--4403 -[]\TU/lmtt/m/n/10 ## OU -292 5.654 0.038 149.3 3 + +Overfull \hbox (17.25pt too wide) in paragraph at lines 4465--4465 +[]\TU/lmtt/m/n/10 ## OU -292 5.654 0.039 149.3 3 3.267[] [] -Overfull \hbox (48.75pt too wide) in paragraph at lines 4403--4403 +Overfull \hbox (48.75pt too wide) in paragraph at lines 4465--4465 []\TU/lmtt/m/n/10 ## sigma squared trend alpha optima.2 median p value lower p value[] [] -Overfull \hbox (48.75pt too wide) in paragraph at lines 4403--4403 +Overfull \hbox (48.75pt too wide) in paragraph at lines 4465--4465 []\TU/lmtt/m/n/10 ## Trend 0.001 0.007 NA NA 0.9870130 - 0.9870130[] + 0.9860140[] [] -Overfull \hbox (48.75pt too wide) in paragraph at lines 4403--4403 -[]\TU/lmtt/m/n/10 ## multi.OU 0.001 NA 0.001 10.63 0.9690310 - 0.9680320[] +Overfull \hbox (48.75pt too wide) in paragraph at lines 4465--4465 +[]\TU/lmtt/m/n/10 ## multi.OU 0.001 NA 0.006 4.686 0.9570430 + 0.9560440[] [] -Overfull \hbox (48.75pt too wide) in paragraph at lines 4403--4403 -[]\TU/lmtt/m/n/10 ## BM 0.001 NA NA NA 0.2012987 - 0.1818182[] +Overfull \hbox (48.75pt too wide) in paragraph at lines 4465--4465 +[]\TU/lmtt/m/n/10 ## BM 0.001 NA NA NA 0.1868132 + 0.1808192[] [] -Overfull \hbox (48.75pt too wide) in paragraph at lines 4403--4403 -[]\TU/lmtt/m/n/10 ## OU 0.001 NA 0.000 NA 0.2867133 - 0.2717283[] +Overfull \hbox (48.75pt too wide) in paragraph at lines 4465--4465 +[]\TU/lmtt/m/n/10 ## OU 0.001 NA 0.000 NA 0.2727273 + 0.2707293[] [] - -Overfull \hbox (38.25pt too wide) in paragraph at lines 4452--4452 +[94] [95] +Overfull \hbox (38.25pt too wide) in paragraph at lines 4514--4514 []\TU/lmtt/m/n/10 ## Evidence of equal variance (Bartlett[]s test of equal vari ances p = 0).[] [] - -Underfull \vbox (badness 1052) has occurred while \output is active [] - -[92] File: dispRity_manual_files/figure-latex/plot6-1.pdf Graphic file (type pdf) -LaTeX Warning: Float too large for page by 29.89452pt on input line 4461. +LaTeX Warning: Float too large for page by 25.6063pt on input line 4523. -Overfull \hbox (22.5pt too wide) in paragraph at lines 4488--4488 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## aicc delta_aicc weight_aicc log.lik param ancest ral state[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4488--4488 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## OU:Trend -287 0.0 0.977 147.8 4 3.352[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4488--4488 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## BM:OU -280 7.5 0.023 144.1 4 3.350[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4488--4488 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## Stasis:BM -244 43.4 0.000 125.1 3 NA[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4488--4488 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## Stasis:OU -240 47.7 0.000 125.1 5 NA[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4488--4488 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## BM:Stasis -130 157.1 0.000 69.3 4 3.268[] [] -Overfull \hbox (54.0pt too wide) in paragraph at lines 4488--4488 +Overfull \hbox (54.0pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## sigma squared alpha optima.1 theta.1 omega trend median p value[] [] -Overfull \hbox (54.0pt too wide) in paragraph at lines 4488--4488 +Overfull \hbox (54.0pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## OU:Trend 0.001 0.041 NA NA NA 0.011 - 0.3246753[] + 0.2987013[] [] - -Overfull \hbox (54.0pt too wide) in paragraph at lines 4488--4488 +[96] [97] +Overfull \hbox (54.0pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## BM:OU 0.001 0.000 4.092 NA NA NA - 0.5009990[] + 0.4925075[] [] -Overfull \hbox (54.0pt too wide) in paragraph at lines 4488--4488 +Overfull \hbox (54.0pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## Stasis:BM 0.002 NA NA 3.390 0.004 NA 0.9970030[] [] -Overfull \hbox (54.0pt too wide) in paragraph at lines 4488--4488 +Overfull \hbox (54.0pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## Stasis:OU 0.002 0.000 4.092 3.390 0.004 NA 1.0000000[] [] -Overfull \hbox (54.0pt too wide) in paragraph at lines 4488--4488 +Overfull \hbox (54.0pt too wide) in paragraph at lines 4550--4550 []\TU/lmtt/m/n/10 ## BM:Stasis 0.000 NA NA 3.806 0.058 NA 1.0000000[] [] -[93] [94] -Overfull \hbox (390.0pt too wide) in paragraph at lines 4524--4524 + +Overfull \hbox (390.0pt too wide) in paragraph at lines 4586--4586 []\TU/lmtt/m/n/10 ## Call: model.test.sim(sim = 1000, model = "BM", time.span = 50, variance = 0.1, sample.size = 100, parameters = list(ancestral.state = 0)) [] [] -Overfull \hbox (33.0pt too wide) in paragraph at lines 4545--4545 +Overfull \hbox (33.0pt too wide) in paragraph at lines 4607--4607 []\TU/lmtt/m/n/10 ## subsets n var median 2.5% 25% 7 5% 97.5%[] [] -Overfull \hbox (33.0pt too wide) in paragraph at lines 4545--4545 +Overfull \hbox (33.0pt too wide) in paragraph at lines 4607--4607 []\TU/lmtt/m/n/10 ## 1 50 100 0.1 -0.06195918 -1.963569 -0.7361336 0.55567 15 1.806730[] [] -Overfull \hbox (33.0pt too wide) in paragraph at lines 4545--4545 +Overfull \hbox (33.0pt too wide) in paragraph at lines 4607--4607 []\TU/lmtt/m/n/10 ## 2 49 100 0.1 -0.09905061 -2.799025 -1.0670018 0.88366 05 2.693583[] [] - -Overfull \hbox (33.0pt too wide) in paragraph at lines 4545--4545 +[98] +Overfull \hbox (33.0pt too wide) in paragraph at lines 4607--4607 []\TU/lmtt/m/n/10 ## 3 48 100 0.1 -0.06215828 -3.594213 -1.3070097 1.13497 12 3.272569[] [] -Overfull \hbox (33.0pt too wide) in paragraph at lines 4545--4545 +Overfull \hbox (33.0pt too wide) in paragraph at lines 4607--4607 []\TU/lmtt/m/n/10 ## 4 47 100 0.1 -0.10602238 -3.949521 -1.4363010 1.22346 25 3.931000[] [] -Overfull \hbox (33.0pt too wide) in paragraph at lines 4545--4545 +Overfull \hbox (33.0pt too wide) in paragraph at lines 4607--4607 []\TU/lmtt/m/n/10 ## 5 46 100 0.1 -0.09016928 -4.277897 -1.5791755 1.38895 84 4.507491[] [] -Overfull \hbox (33.0pt too wide) in paragraph at lines 4545--4545 +Overfull \hbox (33.0pt too wide) in paragraph at lines 4607--4607 []\TU/lmtt/m/n/10 ## 6 45 100 0.1 -0.13183180 -5.115647 -1.7791878 1.62705 27 5.144023[] [] @@ -2180,213 +2262,227 @@ Overfull \hbox (33.0pt too wide) in paragraph at lines 4545--4545 File: dispRity_manual_files/figure-latex/plot7-1.pdf Graphic file (type pdf) -Overfull \hbox (38.25pt too wide) in paragraph at lines 4587--4587 +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[99] +Overfull \hbox (38.25pt too wide) in paragraph at lines 4649--4649 []\TU/lmtt/m/n/10 ## Evidence of equal variance (Bartlett[]s test of equal vari ances p = 0).[] [] -[95] [96] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4608--4608 + +Overfull \hbox (80.25pt too wide) in paragraph at lines 4670--4670 []\TU/lmtt/m/n/10 ## aicc delta_aicc weight_aicc log.lik param theta.1 o mega ancestral state[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4608--4608 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4670--4670 []\TU/lmtt/m/n/10 ## Stasis 41 339.5 0.000 -18.7 2 3.629 0 .074 NA[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4608--4608 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4670--4670 []\TU/lmtt/m/n/10 ## BM -294 3.6 0.112 149.3 2 NA NA 3.267[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4608--4608 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4670--4670 []\TU/lmtt/m/n/10 ## OU -296 2.1 0.227 152.1 4 NA - NA 3.255[] + NA 3.254[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4608--4608 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4670--4670 []\TU/lmtt/m/n/10 ## Trend -298 0.0 0.661 152.1 3 NA NA 3.255[] [] -Overfull \hbox (80.25pt too wide) in paragraph at lines 4608--4608 +Overfull \hbox (80.25pt too wide) in paragraph at lines 4670--4670 []\TU/lmtt/m/n/10 ## EB -246 51.7 0.000 126.3 3 NA NA 4.092[] [] - -Overfull \hbox (22.5pt too wide) in paragraph at lines 4656--4656 +[100] +Overfull \hbox (22.5pt too wide) in paragraph at lines 4718--4718 []\TU/lmtt/m/n/10 ## Call: model.test.sim(sim = 1000, model = disp_time, model. rank = 2)[] [] -[97] -Overfull \hbox (17.25pt too wide) in paragraph at lines 4656--4656 + +Overfull \hbox (17.25pt too wide) in paragraph at lines 4718--4718 []\TU/lmtt/m/n/10 ## aicc log.lik param ancestral state sigma squared alpha optima.1[] [] -Overfull \hbox (17.25pt too wide) in paragraph at lines 4656--4656 -[]\TU/lmtt/m/n/10 ## OU -296 152.1 4 3.255 0.001 0.001 - 10.89[] +Overfull \hbox (17.25pt too wide) in paragraph at lines 4718--4718 +[]\TU/lmtt/m/n/10 ## OU -296 152.1 4 3.254 0.001 0.001 + 12.35[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4674--4674 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4736--4736 []\TU/lmtt/m/n/10 ## subsets n var median 2.5% 25% 75% 97.5%[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4674--4674 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4736--4736 []\TU/lmtt/m/n/10 ## 1 120 5 0.01723152 3.255121 3.135057 3.219150 3.293407 3.375118[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4674--4674 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4736--4736 []\TU/lmtt/m/n/10 ## 2 119 5 0.03555816 3.265538 3.093355 3.200493 3.323520 3.440795[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4674--4674 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4736--4736 []\TU/lmtt/m/n/10 ## 3 118 6 0.03833089 3.269497 3.090438 3.212015 3.329629 3.443074[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4674--4674 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4736--4736 []\TU/lmtt/m/n/10 ## 4 117 7 0.03264826 3.279180 3.112205 3.224810 3.336801 3.447997[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4674--4674 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4736--4736 []\TU/lmtt/m/n/10 ## 5 116 7 0.03264826 3.284500 3.114788 3.223247 3.347970 3.463631[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4674--4674 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4736--4736 []\TU/lmtt/m/n/10 ## 6 115 7 0.03264826 3.293918 3.101298 3.231659 3.354321 3.474645[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4690--4690 +Overfull \hbox (22.5pt too wide) in paragraph at lines 4752--4752 []\TU/lmtt/m/n/10 ## subsets n var median 2.5% 25% 75% 97.5%[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4690--4690 -[]\TU/lmtt/m/n/10 ## 1 120 5 0.01723152 3.254315 3.142419 3.213128 3.294708 - 3.372570[] +Overfull \hbox (22.5pt too wide) in paragraph at lines 4752--4752 +[]\TU/lmtt/m/n/10 ## 1 120 5 0.01723152 3.253367 3.141471 3.212180 3.293760 + 3.371622[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4690--4690 -[]\TU/lmtt/m/n/10 ## 2 119 5 0.03555816 3.264164 3.084458 3.198432 3.325439 - 3.441458[] +Overfull \hbox (22.5pt too wide) in paragraph at lines 4752--4752 +[]\TU/lmtt/m/n/10 ## 2 119 5 0.03555816 3.263167 3.083477 3.197442 3.324438 + 3.440447[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4690--4690 -[]\TU/lmtt/m/n/10 ## 3 118 6 0.03833089 3.263995 3.102370 3.204886 3.333641 - 3.441232[] +Overfull \hbox (22.5pt too wide) in paragraph at lines 4752--4752 +[]\TU/lmtt/m/n/10 ## 3 118 6 0.03833089 3.262952 3.101351 3.203860 3.332595 + 3.440163[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4690--4690 -[]\TU/lmtt/m/n/10 ## 4 117 7 0.03264826 3.273661 3.105529 3.215598 3.331685 - 3.443935[] +Overfull \hbox (22.5pt too wide) in paragraph at lines 4752--4752 +[]\TU/lmtt/m/n/10 ## 4 117 7 0.03264826 3.272569 3.104476 3.214511 3.330587 + 3.442792[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4690--4690 -[]\TU/lmtt/m/n/10 ## 5 116 7 0.03264826 3.281556 3.101302 3.220882 3.343858 - 3.477011[] +Overfull \hbox (22.5pt too wide) in paragraph at lines 4752--4752 +[]\TU/lmtt/m/n/10 ## 5 116 7 0.03264826 3.280423 3.100220 3.219765 3.342726 + 3.475877[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 4690--4690 -[]\TU/lmtt/m/n/10 ## 6 115 7 0.03264826 3.288532 3.095856 3.223696 3.356467 - 3.478720[] +Overfull \hbox (22.5pt too wide) in paragraph at lines 4752--4752 +[]\TU/lmtt/m/n/10 ## 6 115 7 0.03264826 3.287359 3.094699 3.222523 3.355278 + 3.477518[] [] + +Underfull \vbox (badness 2119) has occurred while \output is active [] + +[101] File: dispRity_manual_files/figure-latex/plot8-1.pdf Graphic file (type pdf) -[98] [99] [100] -File: dispRity_manual_files/figure-latex/unnamed-chunk-111-1.pdf Graphic file ( +[102] [103] +File: dispRity_manual_files/figure-latex/unnamed-chunk-116-1.pdf Graphic file ( type pdf) - + -Underfull \hbox (badness 3088) in paragraph at lines 4801--4802 +Underfull \hbox (badness 3088) in paragraph at lines 4863--4864 []\TU/lmr/m/n/10 We can then test for differences in the resulting distribution s using [] -[101] [102] -Overfull \hbox (54.0pt too wide) in paragraph at lines 4917--4917 + +Underfull \vbox (badness 2626) has occurred while \output is active [] + +[104] [105] +Overfull \hbox (54.0pt too wide) in paragraph at lines 4979--4979 []\TU/lmtt/m/n/10 ## Warning: custom.subsets is applied on what seems to be a d istance matrix.[] [] +[106] +File: dispRity_manual_files/figure-latex/unnamed-chunk-122-1.pdf Graphic file ( +type pdf) + + +Underfull \vbox (badness 10000) has occurred while \output is active [] -Overfull \vbox (0.84837pt too high) detected at line 4931 +[107] +Underfull \vbox (badness 1577) detected at line 5058 [] -[103] -File: dispRity_manual_files/figure-latex/unnamed-chunk-117-1.pdf Graphic file ( -type pdf) - 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DISPARITY FROM MULTIPLE MATRICES (AND MULTIPLE TREES!) \ -TU/lmr/m/n/10 105 +TU/lmr/m/n/10 109 + [] + +[109] [110] +File: dispRity_manual_files/figure-latex/unnamed-chunk-127-1.pdf Graphic file ( +type pdf) + + +Underfull \vbox (badness 1052) detected at line 5222 [] -[105] [106] + Overfull \hbox (22.86343pt too wide) has occurred while \output is active \TU/lmr/m/sl/10 4.10. DISPARITY FROM MULTIPLE MATRICES (AND MULTIPLE TREES!) \ -TU/lmr/m/n/10 107 +TU/lmr/m/n/10 111 [] -[107] -File: dispRity_manual_files/figure-latex/unnamed-chunk-122-1.pdf Graphic file ( -type pdf) - -[108] [109] -File: dispRity_manual_files/figure-latex/unnamed-chunk-124-1.pdf Graphic file ( +[111] [112] +File: dispRity_manual_files/figure-latex/unnamed-chunk-129-1.pdf Graphic file ( type pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[110] -File: dispRity_manual_files/figure-latex/unnamed-chunk-125-1.pdf Graphic file ( +[113] [114] +File: dispRity_manual_files/figure-latex/unnamed-chunk-130-1.pdf Graphic file ( type pdf) - + -Underfull \vbox (badness 6893) has occurred while \output is active [] - -[111] -Underfull \vbox (badness 10000) has occurred while \output is active [] +Underfull \vbox (badness 2253) has occurred while \output is active [] -[112] -Overfull \hbox (12.0pt too wide) in paragraph at lines 5320--5320 +[115] +Overfull \hbox (12.0pt too wide) in paragraph at lines 5385--5385 []\TU/lmtt/m/n/10 ## 4 covar subsets for 359 elements in one matrix with 3 dime nsions:[] [] @@ -2394,405 +2490,933 @@ nsions:[] Underfull \vbox (badness 10000) has occurred while \output is active [] -[113] -File: dispRity_manual_files/figure-latex/unnamed-chunk-129-1.pdf Graphic file ( +[116] +File: dispRity_manual_files/figure-latex/unnamed-chunk-134-1.pdf Graphic file ( type pdf) - -[114] [115] [116 - -] -Chapter 5. + [117] [118] -Underfull \hbox (badness 10000) in paragraph at lines 5549--5552 +Chapter 5. +[119 + +] [120] +Underfull \hbox (badness 10000) in paragraph at lines 5614--5617 \TU/lmr/m/n/10 format \TU/lmtt/m/n/10 c(random_distribution_function, distribut ion_parameters) [] -[119] [120] [121] -File: dispRity_manual_files/figure-latex/unnamed-chunk-136-1.pdf Graphic file ( +[121] [122] [123] +File: dispRity_manual_files/figure-latex/unnamed-chunk-141-1.pdf Graphic file ( type pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[122] -Underfull \vbox (badness 1776) has occurred while \output is active [] +[124] +Underfull \vbox (badness 1436) has occurred while \output is active [] -[123] -File: dispRity_manual_files/figure-latex/unnamed-chunk-138-1.pdf Graphic file ( +[125] +File: dispRity_manual_files/figure-latex/unnamed-chunk-143-1.pdf Graphic file ( type pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[124] -Underfull \vbox (badness 1910) has occurred while \output is active [] +[126] [127] +File: dispRity_manual_files/figure-latex/unnamed-chunk-144-1.pdf Graphic file ( +type pdf) + -[125] -File: dispRity_manual_files/figure-latex/unnamed-chunk-139-1.pdf Graphic file ( +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[128] +Underfull \vbox (badness 1014) detected at line 5910 + [] + +[129] +File: dispRity_manual_files/figure-latex/unnamed-chunk-145-1.pdf Graphic file ( type pdf) - + -Underfull \vbox (badness 1038) has occurred while \output is active [] +Underfull \vbox (badness 10000) has occurred while \output is active [] -[126] -File: dispRity_manual_files/figure-latex/unnamed-chunk-140-1.pdf Graphic file ( +[130] [131] +File: dispRity_manual_files/figure-latex/unnamed-chunk-146-1.pdf Graphic file ( type pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[127] -Underfull \vbox (badness 1629) detected at line 5888 - [] +[132] [133] [134 -[128] -File: dispRity_manual_files/figure-latex/unnamed-chunk-141-1.pdf Graphic file ( -type pdf) - -[129] [130] +] Chapter 6. -Overfull \hbox (43.802pt too wide) in paragraph at lines 5904--5907 +Overfull \hbox (43.802pt too wide) in paragraph at lines 5969--5972 \TU/lmtt/m/n/10 vegan::vegdist\TU/lmr/m/n/10 , \TU/lmtt/m/n/10 cluster::daisy \ TU/lmr/m/n/10 or \TU/lmtt/m/n/10 Claddis::calculate_morphological_distances\TU/ lmr/m/n/10 ) [] -Overfull \hbox (45.91pt too wide) in paragraph at lines 5904--5907 +Overfull \hbox (45.91pt too wide) in paragraph at lines 5969--5972 \TU/lmr/m/n/10 above (though not that dissimilar from \TU/lmtt/m/n/10 Claddis:: calculate_morphological_distances\TU/lmr/m/n/10 ) [] -[131 - -] -File: dispRity_manual_files/figure-latex/unnamed-chunk-144-1.pdf Graphic file ( +[135] +File: dispRity_manual_files/figure-latex/unnamed-chunk-149-1.pdf Graphic file ( type pdf) - -[132] [133] -Underfull \hbox (badness 4072) in paragraph at lines 6057--6061 + +[136] [137] +Underfull \hbox (badness 4072) in paragraph at lines 6122--6126 \TU/lmr/m/n/10 and \TU/lmtt/m/n/10 special.behaviours\TU/lmr/m/n/10 . The \TU/l mtt/m/n/10 special.tokens \TU/lmr/m/n/10 are \TU/lmtt/m/n/10 missing = "?"\TU/l mr/m/n/10 , [] -[134] [135] -Overfull \hbox (48.75pt too wide) in paragraph at lines 6187--6187 +[138] [139] +Overfull \hbox (48.75pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [1] "Dasypodidae" "Bradypus" "Myrmecophagidae" "Todralestes"[] [] -Overfull \hbox (48.75pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (48.75pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [5] "Potamogalinae" "Dilambdogale" "Widanelfarasia" "Rhynchocyon"[] [] -Overfull \hbox (43.5pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (43.5pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [9] "Procavia" "Moeritherium" "Pezosiren" "Trichechus"[] [] -Overfull \hbox (27.75pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (27.75pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [13] "Tribosphenomys" "Paramys" "Rhombomylus" "Gomphos"[] [] -Overfull \hbox (48.75pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (48.75pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [17] "Mimotona" "Cynocephalus" "Purgatorius" "Plesiadapis"[] [] -Overfull \hbox (38.25pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (38.25pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [21] "Notharctus" "Adapis" "Patriomanis" "Protictis"[] [] -Overfull \hbox (38.25pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (38.25pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [25] "Vulpavus" "Miacis" "Icaronycteris" "Soricidae"[] [] -Overfull \hbox (33.0pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (33.0pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [1] "Daulestes" "Bulaklestes" "Uc hkudukodon"[] [] -Overfull \hbox (33.0pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (33.0pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [4] "Kennalestes" "Asioryctes" "Uk haatherium"[] [] -Overfull \hbox (17.25pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (17.25pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [7] "Cimolestes" "unnamed_cimolestid" "Ma elestes"[] [] -Overfull \hbox (33.0pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (33.0pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [10] "Batodon" "Kulbeckia" "Zh angolestes"[] [] -Overfull \hbox (27.75pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (27.75pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [13] "unnamed_zalambdalestid" "Zalambdalestes" "Ba runlestes"[] [] -Overfull \hbox (22.5pt too wide) in paragraph at lines 6187--6187 +Overfull \hbox (22.5pt too wide) in paragraph at lines 6252--6252 []\TU/lmtt/m/n/10 ## [16] "Gypsonictops" "Leptictis" "Ox yclaenus"[] [] -[136] -Overfull \hbox (69.75pt too wide) in paragraph at lines 6210--6210 +[140] +Overfull \hbox (69.75pt too wide) in paragraph at lines 6275--6275 []\TU/lmtt/m/n/10 ## [1] 132.9000 129.4000 125.0000 113.0000 100.5000 93.9000 89.8000 86.3000[] [] -Overfull \hbox (69.75pt too wide) in paragraph at lines 6210--6210 +Overfull \hbox (69.75pt too wide) in paragraph at lines 6275--6275 []\TU/lmtt/m/n/10 ## 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"us(at.level(clade, 3):trait):animal" " us(trait):animal"[] [] -File: dispRity_manual_files/figure-latex/unnamed-chunk-157-1.pdf Graphic file ( +File: dispRity_manual_files/figure-latex/unnamed-chunk-161-1.pdf Graphic file ( type pdf) - -[140] -File: dispRity_manual_files/figure-latex/unnamed-chunk-158-1.pdf Graphic file ( + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[144] +File: dispRity_manual_files/figure-latex/unnamed-chunk-162-1.pdf Graphic file ( type pdf) - -File: dispRity_manual_files/figure-latex/unnamed-chunk-159-1.pdf Graphic file ( + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[145] +File: dispRity_manual_files/figure-latex/unnamed-chunk-163-1.pdf Graphic file ( type pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[141] -File: dispRity_manual_files/figure-latex/unnamed-chunk-160-1.pdf Graphic file ( +[146] +File: dispRity_manual_files/figure-latex/unnamed-chunk-164-1.pdf Graphic file ( type pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[142] -File: dispRity_manual_files/figure-latex/unnamed-chunk-162-1.pdf Graphic file ( +[147] +File: dispRity_manual_files/figure-latex/unnamed-chunk-165-1.pdf Graphic file ( type pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[143] -Underfull \vbox (badness 1661) has occurred while \output is active [] +[148] +File: dispRity_manual_files/figure-latex/unnamed-chunk-167-1.pdf Graphic file ( +type pdf) + -[144] -Overfull \hbox (190.5pt too wide) in paragraph at lines 6519--6519 +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[149] [150] +Overfull \hbox (190.5pt too wide) in paragraph at lines 6591--6591 []\TU/lmtt/m/n/10 ## The first 3 dimensions are needed to express at least 95% of the variance in the whole trait space.[] [] -Overfull \hbox (132.75pt too wide) in paragraph at lines 6519--6519 +Overfull \hbox (132.75pt too wide) in paragraph at lines 6591--6591 []\TU/lmtt/m/n/10 ## You can use x$dimensions to select them or use plot(x) and summary(x) to summarise them.[] [] -Overfull \hbox (75.0pt too wide) in paragraph at lines 6535--6535 +Overfull \hbox (75.0pt too wide) in paragraph at lines 6607--6607 []\TU/lmtt/m/n/10 ## Comp.1.var Comp.1.sum Comp.2.var Comp.2.sum Co mp.3.var Comp.3.sum[] [] -Overfull \hbox (75.0pt too wide) in paragraph at lines 6535--6535 +Overfull \hbox (75.0pt too wide) in paragraph at lines 6607--6607 []\TU/lmtt/m/n/10 ## whole_space 0.62 0.62 0.247 0.868 0.089 0.957[] [] -File: dispRity_manual_files/figure-latex/unnamed-chunk-165-1.pdf Graphic file ( +File: dispRity_manual_files/figure-latex/unnamed-chunk-170-1.pdf Graphic file ( type pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[145] -File: dispRity_manual_files/figure-latex/unnamed-chunk-166-1.pdf Graphic file ( +[151] [152] +File: dispRity_manual_files/figure-latex/unnamed-chunk-171-1.pdf Graphic file ( type pdf) - -[146] -File: dispRity_manual_files/figure-latex/unnamed-chunk-167-1.pdf Graphic file ( + +[153] +File: dispRity_manual_files/figure-latex/unnamed-chunk-172-1.pdf Graphic file ( type pdf) - - -Underfull \vbox (badness 10000) has occurred while \output is active [] + -[147] -Overfull \hbox (38.25pt too wide) in paragraph at lines 6633--6633 +Overfull \hbox (38.25pt too wide) in paragraph at lines 6705--6705 []\TU/lmtt/m/n/10 ## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22[] [] -Overfull \hbox (54.0pt too wide) in paragraph at lines 6633--6633 +Overfull \hbox (54.0pt too wide) in paragraph at lines 6705--6705 []\TU/lmtt/m/n/10 ## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23[] [] -Underfull \hbox (badness 1052) in paragraph at lines 6645--6648 +Underfull \hbox (badness 1052) in paragraph at lines 6717--6720 \TU/lmr/m/n/10 This function is a modification of the \TU/lmtt/m/n/10 paleotree ::timeSliceTree \TU/lmr/m/n/10 func- [] -[148] -File: dispRity_manual_files/figure-latex/unnamed-chunk-168-1.pdf Graphic file ( +[154] +File: dispRity_manual_files/figure-latex/unnamed-chunk-173-1.pdf Graphic file ( type pdf) - -[149] -File: dispRity_manual_files/figure-latex/unnamed-chunk-169-1.pdf Graphic file ( + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[155] +File: dispRity_manual_files/figure-latex/unnamed-chunk-174-1.pdf Graphic file ( type pdf) - + -Underfull \hbox (badness 1406) in paragraph at lines 6706--6709 +Underfull \hbox (badness 1406) in paragraph at lines 6778--6781 []\TU/lmr/m/n/10 The \TU/lmtt/m/n/10 remove.zero.brlen \TU/lmr/m/n/10 is a “cle ver” wrapping function that uses the [] -[150] -File: dispRity_manual_files/figure-latex/unnamed-chunk-170-1.pdf Graphic file ( +[156] +File: dispRity_manual_files/figure-latex/unnamed-chunk-175-1.pdf Graphic file ( type pdf) - + Underfull \vbox (badness 10000) has occurred while \output is active [] -[151] [152] [153] [154] +[157] [158] [159] [160] [161] [162] +Overfull \hbox (80.25pt too wide) in paragraph at lines 7102--7102 +[]\TU/lmtt/m/n/10 ## Warning: The characters 39 are invariant (using the curren +t special behaviours[] + [] + + +Overfull \hbox (6.75pt too wide) in paragraph at lines 7102--7102 +[]\TU/lmtt/m/n/10 ## for special characters) and are simply duplicated for each + node.[] + [] + + +Overfull \hbox (75.0pt too wide) in paragraph at lines 7114--7114 +[]\TU/lmtt/m/n/10 ## Warning in mapply(replace.NA, ancestral_states, characters +_states, MoreArgs =[] + [] + + +Overfull \hbox (59.25pt too wide) in paragraph at lines 7114--7114 +[]\TU/lmtt/m/n/10 ## list(special.tokens = special.tokens), : longer argument n +ot a multiple of[] + [] + + +Overfull \hbox (75.0pt too wide) in paragraph at lines 7126--7126 +[]\TU/lmtt/m/n/10 ## Warning in mapply(replace.NA, ancestral_states, characters +_states, MoreArgs =[] + [] + + +Overfull \hbox (59.25pt too wide) in paragraph at lines 7126--7126 +[]\TU/lmtt/m/n/10 ## list(special.tokens = special.tokens), : longer argument n +ot a multiple of[] + [] + + +Overfull \hbox (75.0pt too wide) in paragraph at lines 7138--7138 +[]\TU/lmtt/m/n/10 ## Warning in mapply(replace.NA, ancestral_states, characters +_states, MoreArgs =[] + [] + + +Overfull \hbox (59.25pt too wide) in paragraph at lines 7138--7138 +[]\TU/lmtt/m/n/10 ## list(special.tokens = special.tokens), : longer argument n +ot a multiple of[] + [] + + +Overfull \hbox (75.0pt too wide) in paragraph at lines 7150--7150 +[]\TU/lmtt/m/n/10 ## Warning in mapply(replace.NA, ancestral_states, characters +_states, MoreArgs =[] + [] + + +Overfull \hbox (59.25pt too wide) in paragraph at lines 7150--7150 +[]\TU/lmtt/m/n/10 ## list(special.tokens = special.tokens), : longer argument n +ot a multiple of[] + [] + +[163] +Overfull \hbox (75.0pt too wide) in paragraph at lines 7162--7162 +[]\TU/lmtt/m/n/10 ## Warning in mapply(replace.NA, ancestral_states, characters +_states, MoreArgs =[] + [] + + +Overfull \hbox (59.25pt too wide) in paragraph at lines 7162--7162 +[]\TU/lmtt/m/n/10 ## list(special.tokens = special.tokens), : longer argument n +ot a multiple of[] + [] + + +Overfull \hbox (1.5pt too wide) in paragraph at lines 7187--7187 +[]\TU/lmtt/m/n/10 ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] +[,10][] + [] + + +Overfull \hbox (1.5pt too wide) in paragraph at lines 7187--7187 +[]\TU/lmtt/m/n/10 ## [7,] "0" "0/1" "0/1" "0" "1" "1" "1" "0" "0" +"0/1"[] + [] + +[164] +Overfull \hbox (80.25pt too wide) in paragraph at lines 7219--7219 +[]\TU/lmtt/m/n/10 ## Warning: The characters 39 are invariant (using the curren +t special behaviours[] + [] + + +Overfull \hbox (6.75pt too wide) in paragraph at lines 7219--7219 +[]\TU/lmtt/m/n/10 ## for special characters) and are simply duplicated for each + node.[] + [] + + +Overfull \hbox (75.0pt too wide) in paragraph at lines 7231--7231 +[]\TU/lmtt/m/n/10 ## Warning in mapply(replace.NA, ancestral_states, characters +_states, MoreArgs =[] + [] + + +Overfull \hbox (59.25pt too wide) in paragraph at lines 7231--7231 +[]\TU/lmtt/m/n/10 ## list(special.tokens = special.tokens), : longer argument n +ot a multiple of[] + [] + + +Overfull \hbox (75.0pt too wide) in paragraph at lines 7243--7243 +[]\TU/lmtt/m/n/10 ## Warning in mapply(replace.NA, ancestral_states, characters +_states, MoreArgs =[] + [] + + +Overfull \hbox (59.25pt too wide) in paragraph at lines 7243--7243 +[]\TU/lmtt/m/n/10 ## list(special.tokens = special.tokens), : longer argument n +ot a multiple of[] + [] + + +Overfull \hbox (75.0pt too wide) in paragraph at lines 7255--7255 +[]\TU/lmtt/m/n/10 ## Warning in mapply(replace.NA, ancestral_states, characters +_states, MoreArgs =[] + [] + + +Overfull \hbox (59.25pt too wide) in paragraph at lines 7255--7255 +[]\TU/lmtt/m/n/10 ## list(special.tokens = special.tokens), : longer argument n +ot a multiple of[] + [] + + +Overfull \hbox (75.0pt too wide) in paragraph at lines 7267--7267 +[]\TU/lmtt/m/n/10 ## Warning in mapply(replace.NA, ancestral_states, characters +_states, MoreArgs =[] + [] + + +Overfull \hbox (59.25pt too wide) in paragraph at lines 7267--7267 +[]\TU/lmtt/m/n/10 ## list(special.tokens = special.tokens), : longer argument n +ot a multiple of[] + [] + + +Overfull \hbox (75.0pt too wide) in paragraph at lines 7279--7279 +[]\TU/lmtt/m/n/10 ## Warning in mapply(replace.NA, ancestral_states, characters +_states, MoreArgs =[] + [] + + +Overfull \hbox (59.25pt too wide) in paragraph at lines 7279--7279 +[]\TU/lmtt/m/n/10 ## list(special.tokens = special.tokens), : longer argument n +ot a multiple of[] + [] + +[165] [166] Chapter 7. -Overfull \hbox (1.5pt too wide) in paragraph at lines 6905--6905 +Overfull \hbox (1.5pt too wide) in paragraph at lines 7334--7334 []\TU/lmtt/m/n/10 ## [1] "matrix" "tree" "call" "subsets" "dispa rity"[] [] -[155 +[167 ] -Overfull \hbox (243.0pt too wide) in paragraph at lines 6920--6920 +Overfull \hbox (243.0pt too wide) in paragraph at lines 7349--7349 []\TU/lmtt/m/n/10 ## 7 continuous (acctran) time subsets for 99 elements in one matrix with 97 dimensions with 1 phylogenetic tree[] [] -Overfull \hbox (127.5pt too wide) in paragraph at lines 6920--6920 +Overfull \hbox (127.5pt too wide) in paragraph at lines 7349--7349 []\TU/lmtt/m/n/10 ## Data was bootstrapped 100 times (method:"full") and rarefi ed to 20, 15, 10, 5 elements.[] [] -[156] -Overfull \hbox (80.25pt too wide) in paragraph at lines 6996--6996 -[]\TU/lmtt/m/n/10 ## Warning in check.dispRity.data(data$matrix): Row names hav -e been automatically[] +[168] +Overfull \hbox (59.25pt too wide) in paragraph at lines 7425--7425 +[]\TU/lmtt/m/n/10 ## Warning in check.data(data, match_call): Row names have be +en automatically[] [] -Overfull \hbox (38.25pt too wide) in paragraph at lines 7033--7033 +Overfull \hbox (38.25pt too wide) in paragraph at lines 7462--7462 []\TU/lmtt/m/n/10 ## ..$ : chr [1:18] "Leptictis" "Dasypodidae" "n24" "Potamo galinae" ...[] [] -Overfull \hbox (1.5pt too wide) in paragraph at lines 7048--7048 +Overfull \hbox (1.5pt too wide) in paragraph at lines 7477--7477 []\TU/lmtt/m/n/10 ## num [1:15, 1:97] -0.12948 -0.57973 0.00361 0.27123 0.2712 3 ...[] [] -[157] [158] [159] -Underfull \vbox (badness 3872) has occurred while \output is active [] +[169] [170] [171] +File: dispRity_manual_files/figure-latex/unnamed-chunk-202-1.pdf Graphic file ( +type pdf) + + +Underfull \vbox (badness 5217) has occurred while \output is active [] + +[172] +File: dispRity_manual_files/figure-latex/unnamed-chunk-203-1.pdf Graphic file ( +type pdf) + +[173] [174] [175] [176] [177] [178 + +] +Chapter 8. +[179] +File: dispRity_manual_files/figure-latex/unnamed-chunk-208-1.pdf Graphic file ( +type pdf) + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[180] +File: dispRity_manual_files/figure-latex/unnamed-chunk-209-1.pdf Graphic file ( +type pdf) + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[181] +Overfull \vbox (1.76578pt too high) detected at line 7980 + [] + +[182] +Overfull \hbox (38.25pt too wide) in paragraph at lines 8008--8008 +[]\TU/lmtt/m/n/10 ## 3 customised subsets for 150 elements in one matrix with 4 + dimensions:[] + [] + + +Overfull \hbox (38.25pt too wide) in paragraph at lines 8035--8035 +[]\TU/lmtt/m/n/10 ## 3 customised subsets for 150 elements in one matrix with 4 + dimensions:[] + [] + +[183] +File: dispRity_manual_files/figure-latex/unnamed-chunk-214-1.pdf Graphic file ( +type pdf) + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[184] +Overfull \hbox (489.75pt too wide) in paragraph at lines 8090--8090 +[]\TU/lmtt/m/n/10 ## Warning in test.dispRity(petal_disparity, test = adonis.di +spRity): adonis.dispRity test will be applied to the data matrix, not to the ca +lculated disparity.[] + [] + + +Overfull \hbox (195.75pt too wide) in paragraph at lines 8096--8096 +[]\TU/lmtt/m/n/10 ## Warning in adonis.dispRity(data, ...): The input data for +adonis.dispRity was not a distance matrix.[] + [] + + +Overfull \hbox (190.5pt too wide) in paragraph at lines 8096--8096 +[]\TU/lmtt/m/n/10 ## The results are thus based on the distance matrix for the +input data (i.e. dist(data$matrix[[1]])).[] + [] + +[185] +Overfull \hbox (27.75pt too wide) in paragraph at lines 8111--8111 +[]\TU/lmtt/m/n/10 ## vegan::adonis2(formula = dist(matrix) ~ group, method = "e +uclidean")[] + [] + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[186] +File: dispRity_manual_files/figure-latex/unnamed-chunk-216-1.pdf Graphic file ( +type pdf) + +File: dispRity_manual_files/figure-latex/unnamed-chunk-217-1.pdf Graphic file ( +type pdf) + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[187] [188] +Chapter 9. +[189 + +] +Overfull \hbox (38.25pt too wide) in paragraph at lines 8220--8220 +[]\TU/lmtt/m/n/10 ## [,1] [,2] [,3] [,4] + [,5][] + [] + + +Overfull \hbox (38.25pt too wide) in paragraph at lines 8220--8220 +[]\TU/lmtt/m/n/10 ## Cimolestes -0.5613001 0.06006259 0.08414761 -0.2313084 + -0.18825039[] + [] + + +Overfull \hbox (38.25pt too wide) in paragraph at lines 8220--8220 +[]\TU/lmtt/m/n/10 ## Maelestes -0.4186019 -0.12186005 0.25556379 0.2737995 + -0.28510479[] + [] + + +Overfull \hbox (38.25pt too wide) in paragraph at lines 8220--8220 +[]\TU/lmtt/m/n/10 ## Batodon -0.8337640 0.28718501 -0.10594610 -0.2381511 + -0.07132646[] + [] + + +Overfull \hbox (38.25pt too wide) in paragraph at lines 8220--8220 +[]\TU/lmtt/m/n/10 ## Bulaklestes -0.7708261 -0.07629583 0.04549285 -0.4951160 + -0.39962626[] + [] + + +Overfull \hbox (38.25pt too wide) in paragraph at lines 8220--8220 +[]\TU/lmtt/m/n/10 ## Daulestes -0.8320466 -0.09559563 0.04336661 -0.5792351 + -0.37385914[] + [] + + +Overfull \hbox (38.25pt too wide) in paragraph at lines 8220--8220 +[]\TU/lmtt/m/n/10 ## Uchkudukodon -0.5074468 -0.34273248 0.40410310 -0.1223782 + -0.34857351[] + [] + +File: dispRity_manual_files/figure-latex/unnamed-chunk-218-1.pdf Graphic file ( +type pdf) + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + + +Overfull \hbox (118.47563pt too wide) has occurred while \output is active +\TU/lmr/m/n/10 190 \TU/lmr/m/sl/10 CHAPTER 9. PALAEOBIOLOGY DEMO: DISPARITY-TH +ROUGH-TIME AND WITHIN GROUPS + [] + +[190] +File: dispRity_manual_files/figure-latex/unnamed-chunk-219-1.pdf Graphic file ( +type pdf) + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[191] +Underfull \hbox (badness 1024) in paragraph at lines 8285--8287 +[]\TU/lmr/m/n/10 You can use any type of morphospace in any dataset form (\TU/l +mtt/m/n/10 "matrix"\TU/lmr/m/n/10 , + [] + + +Overfull \hbox (3.03001pt too wide) in paragraph at lines 8309--8311 +[]\TU/lmr/bx/n/10 WARNING: \TU/lmr/m/n/10 the data generated by the functions \ +TU/lmtt/m/n/10 i.need.a.matrix\TU/lmr/m/n/10 , + [] + + +Overfull \hbox (118.47563pt too wide) has occurred while \output is active +\TU/lmr/m/n/10 192 \TU/lmr/m/sl/10 CHAPTER 9. PALAEOBIOLOGY DEMO: DISPARITY-TH +ROUGH-TIME AND WITHIN GROUPS + [] + +[192] +Underfull \vbox (badness 1102) has occurred while \output is active [] + +[193] +Overfull \hbox (118.47563pt too wide) has occurred while \output is active +\TU/lmr/m/n/10 194 \TU/lmr/m/sl/10 CHAPTER 9. PALAEOBIOLOGY DEMO: DISPARITY-TH +ROUGH-TIME AND WITHIN GROUPS + [] + +[194] +Overfull \hbox (1.5pt too wide) in paragraph at lines 8460--8460 +[]\TU/lmtt/m/n/10 ## ..$ : num [1:50, 1:48] -0.561 -0.419 -0.834 -0.771 -0.83 +2 ...[] + [] + + +Overfull \hbox (75.0pt too wide) in paragraph at lines 8460--8460 +[]\TU/lmtt/m/n/10 ## .. .. ..$ : chr [1:50] "Cimolestes" "Maelestes" "Batodon +" "Bulaklestes" ...[] + [] + + +Overfull \hbox (43.5pt too wide) in paragraph at lines 8460--8460 +[]\TU/lmtt/m/n/10 ## .. ..$ edge : int [1:98, 1:2] 51 52 52 53 53 51 54 + 55 56 56 ...[] + [] + + +Overfull \hbox (148.5pt too wide) in paragraph at lines 8460--8460 +[]\TU/lmtt/m/n/10 ## .. ..$ tip.label : chr [1:50] "Daulestes" "Bulaklestes" + "Uchkudukodon" "Kennalestes" ...[] + [] + + +Overfull \hbox (12.0pt too wide) in paragraph at lines 8460--8460 +[]\TU/lmtt/m/n/10 ## .. ..- attr(*, "names")= chr [1:4] "" "trees" "matrices" + "bind"[] + [] + + +Overfull \hbox (17.25pt too wide) in paragraph at lines 8460--8460 +[]\TU/lmtt/m/n/10 ## .. ..$ elements: int [1:13, 1] 41 49 24 25 26 27 28 21 2 +2 19 ...[] + [] + +[195] +Overfull \hbox (80.25pt too wide) in paragraph at lines 8483--8483 +[]\TU/lmtt/m/n/10 ## 5 discrete time subsets for 50 elements in one matrix with + 1 phylogenetic tree[] + [] + + +Underfull \hbox (badness 1048) in paragraph at lines 8514--8517 +\TU/lmr/m/n/10 some confidence intervals generated by the pseudoreplication ste +p above + [] + + +Overfull \hbox (118.47563pt too wide) has occurred while \output is active +\TU/lmr/m/n/10 196 \TU/lmr/m/sl/10 CHAPTER 9. PALAEOBIOLOGY DEMO: DISPARITY-TH +ROUGH-TIME AND WITHIN GROUPS + [] + +[196] +Overfull \hbox (80.25pt too wide) in paragraph at lines 8545--8545 +[]\TU/lmtt/m/n/10 ## 10% 20% 30% 40% 50% 60% 70% 80% 9 +0% 100% slope[] + [] + + +Overfull \hbox (80.25pt too wide) in paragraph at lines 8545--8545 +[]\TU/lmtt/m/n/10 ## random 2.41 2.51 2.56 2.50 2.54 2.51 2.52 2.53 2. +53 2.52 0.0006434981[] + [] + + +Overfull \hbox (80.25pt too wide) in paragraph at lines 8545--8545 +[]\TU/lmtt/m/n/10 ## size.increase 2.23 2.19 2.25 2.33 2.31 2.35 2.43 2.44 2. +48 2.52 0.0036071419[] + [] -[160] [161] (./dispRity_manual.bbl [162] [163 -] [164]) [165] (./dispRity_manual.aux) ) +Overfull \hbox (80.25pt too wide) in paragraph at lines 8545--8545 +[]\TU/lmtt/m/n/10 ## size.hollowness 2.40 2.56 2.56 2.60 2.63 2.64 2.60 2.58 2. +55 2.52 0.0006032204[] + [] + +File: dispRity_manual_files/figure-latex/unnamed-chunk-226-1.pdf Graphic file ( +type pdf) + + +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[197] +Underfull \vbox (badness 10000) has occurred while \output is active [] + + +Overfull \hbox (118.47563pt too wide) has occurred while \output is active +\TU/lmr/m/n/10 198 \TU/lmr/m/sl/10 CHAPTER 9. PALAEOBIOLOGY DEMO: DISPARITY-TH +ROUGH-TIME AND WITHIN GROUPS + [] + +[198] +Underfull \vbox (badness 1540) has occurred while \output is active [] + +[199] +Overfull \hbox (69.75pt too wide) in paragraph at lines 8613--8613 +[]\TU/lmtt/m/n/10 ## Warning in quartz(width = 10, height = 5): Quartz device i +s not available on[] + [] + +File: dispRity_manual_files/figure-latex/unnamed-chunk-229-1.pdf Graphic file ( +type pdf) + + +Underfull \hbox (badness 1742) in paragraph at lines 8629--8630 +[]\TU/lmr/m/n/10 Same as for the \TU/lmtt/m/n/10 summary.dispRity \TU/lmr/m/n/1 +0 function, check out the + [] + + +Overfull \hbox (118.47563pt too wide) has occurred while \output is active +\TU/lmr/m/n/10 200 \TU/lmr/m/sl/10 CHAPTER 9. PALAEOBIOLOGY DEMO: DISPARITY-TH +ROUGH-TIME AND WITHIN GROUPS + [] + +[200] +Underfull \vbox (badness 10000) has occurred while \output is active [] + +[201] +Overfull \hbox (118.47563pt too wide) has occurred while \output is active +\TU/lmr/m/n/10 202 \TU/lmr/m/sl/10 CHAPTER 9. PALAEOBIOLOGY DEMO: DISPARITY-TH +ROUGH-TIME AND WITHIN GROUPS + [] + +[202] +Chapter 10. +[203 + +] +Overfull \hbox (2988.75pt too wide) in paragraph at lines 8743--8743 +[]\TU/lmtt/m/n/10 ## | + | + | 0% | + |================== + | 25% | + |=================================== + | 50% | + |======================================================= +===============| 100%[] + [] + + +Overfull \hbox (88.00455pt too wide) has occurred while \output is active +\TU/lmr/m/n/10 204 \TU/lmr/m/sl/10 CHAPTER 10. MORPHOMETRIC GEOMETRIC DEMO: A +BETWEEN GROUP ANALYSIS + [] + +[204] +File: dispRity_manual_files/figure-latex/unnamed-chunk-234-1.pdf Graphic file ( +type pdf) + + +Underfull \vbox (badness 1845) has occurred while \output is active [] + +[205] +Overfull \hbox (88.00455pt too wide) has occurred while \output is active +\TU/lmr/m/n/10 206 \TU/lmr/m/sl/10 CHAPTER 10. MORPHOMETRIC GEOMETRIC DEMO: A +BETWEEN GROUP ANALYSIS + [] + +[206] +File: dispRity_manual_files/figure-latex/unnamed-chunk-238-1.pdf Graphic file ( +type pdf) + +[207] +Overfull \hbox (88.00455pt too wide) has occurred while \output is active +\TU/lmr/m/n/10 208 \TU/lmr/m/sl/10 CHAPTER 10. MORPHOMETRIC GEOMETRIC DEMO: A +BETWEEN GROUP ANALYSIS + [] + +[208] [209] +Overfull \hbox (88.00455pt too wide) has occurred while \output is active +\TU/lmr/m/n/10 210 \TU/lmr/m/sl/10 CHAPTER 10. MORPHOMETRIC GEOMETRIC DEMO: A +BETWEEN GROUP ANALYSIS + [] + +[210 + +] +Chapter 11. +[211] [212 + +] +Chapter 12. +(./dispRity_manual.bbl [213] [214 + +] [215] [216]) [217] (./dispRity_manual.aux) ) Here is how much of TeX's memory you used: - 19845 strings out of 475865 - 382193 string characters out of 5802057 - 866860 words of memory out of 5000000 - 40560 multiletter control sequences out of 15000+600000 + 20023 strings out of 478190 + 390854 string characters out of 5854826 + 846096 words of memory out of 5000000 + 40255 multiletter control sequences out of 15000+600000 475806 words of font info for 93 fonts, out of 8000000 for 9000 - 1348 hyphenation exceptions out of 8191 - 86i,7n,121p,1012b,587s stack positions out of 10000i,1000n,20000p,200000b,200000s + 14 hyphenation exceptions out of 8191 + 86i,7n,121p,1012b,576s stack positions out of 5000i,500n,10000p,200000b,80000s -Output written on dispRity_manual.pdf (165 pages). +Output written on dispRity_manual.pdf (217 pages). 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b/inst/gitbook/figure/unnamed-chunk-98-1.png deleted file mode 100755 index 5ff523a7..00000000 Binary files a/inst/gitbook/figure/unnamed-chunk-98-1.png and /dev/null differ diff --git a/inst/gitbook/figure/unnamed-chunk-99-1.png b/inst/gitbook/figure/unnamed-chunk-99-1.png deleted file mode 100755 index cc4af10b..00000000 Binary files a/inst/gitbook/figure/unnamed-chunk-99-1.png and /dev/null differ diff --git a/inst/gitbook/index.Rmd b/inst/gitbook/index.Rmd index a67cd0f0..1e517b8f 100755 --- a/inst/gitbook/index.Rmd +++ b/inst/gitbook/index.Rmd @@ -8,7 +8,7 @@ documentclass: book bibliography: [../References.bib, ../packages.bib] link-citations: yes github-repo: TGuillerme/dispRity -description: "dispRity R package vignette" +description: "dispRity R package manual" --- diff --git a/inst/gitbook/min_example_for_eco.tmp b/inst/gitbook/min_example_for_eco.tmp deleted file mode 100755 index 0f9ec664..00000000 --- a/inst/gitbook/min_example_for_eco.tmp +++ /dev/null @@ -1,61 +0,0 @@ - - - \ No newline at end of file diff --git a/inst/gitbook/packages.bib b/inst/gitbook/packages.bib index a59d1584..80086e25 100755 --- a/inst/gitbook/packages.bib +++ b/inst/gitbook/packages.bib @@ -1,8 +1,8 @@ @Manual{R-ape, title = {ape: Analyses of Phylogenetics and Evolution}, author = {Emmanuel Paradis and Simon Blomberg and Ben Bolker and Joseph Brown and Santiago Claramunt and Julien Claude and Hoa Sien Cuong and Richard Desper and Gilles Didier and Benoit Durand and Julien Dutheil and RJ Ewing and Olivier Gascuel and Thomas Guillerme and Christoph Heibl and Anthony Ives and Bradley Jones and Franz Krah and Daniel Lawson and Vincent Lefort and Pierre Legendre and Jim Lemon and Guillaume Louvel and Eric Marcon and Rosemary McCloskey and Johan Nylander and Rainer Opgen-Rhein and Andrei-Alin Popescu and Manuela Royer-Carenzi and Klaus Schliep and Korbinian Strimmer and Damien {de Vienne}}, - year = {2022}, - note = {R package version 5.6-2}, + year = {2023}, + note = {R package version 5.7-1.6}, url = {http://ape-package.ird.fr/}, } @@ -11,170 +11,142 @@ @Manual{R-base author = {{R Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, - year = {2022}, + year = {2023}, url = {https://www.R-project.org/}, } @Manual{R-bookdown, title = {bookdown: Authoring Books and Technical Documents with R Markdown}, author = {Yihui Xie}, - year = {2022}, - note = {R package version 0.27}, - url = {https://CRAN.R-project.org/package=bookdown}, + year = {2023}, + note = {R package version 0.36, +https://pkgs.rstudio.com/bookdown/}, + url = {https://github.com/rstudio/bookdown}, } -@Manual{R-Claddis, - title = {Claddis: Measuring Morphological Diversity and Evolutionary Tempo}, - author = {Graeme T. Lloyd and Thomas Guillerme and Emma Sherratt and Steve C. Wang}, - year = {2020}, - note = {R package version 0.6.3}, - url = {https://CRAN.R-project.org/package=Claddis}, -} @Manual{R-devtools, title = {devtools: Tools to Make Developing R Packages Easier}, author = {Hadley Wickham and Jim Hester and Winston Chang and Jennifer Bryan}, - year = {2021}, - note = {R package version 2.4.3}, - url = {https://CRAN.R-project.org/package=devtools}, + year = {2022}, + note = {R package version 2.4.5}, + url = {https://devtools.r-lib.org/}, } @Manual{R-dispRity, title = {dispRity: Measuring Disparity}, - author = {Thomas Guillerme and Mark N Puttick}, - year = {2022}, - note = {R package version 1.7.0}, + author = {Thomas Guillerme and Mark Puttick and Jack Hadfield}, + year = {2023}, + note = {R package version 1.8}, url = {https://github.com/TGuillerme/dispRity}, } -@Manual{R-geiger, - title = {geiger: Analysis of Evolutionary Diversification}, - author = {Luke Harmon and Matthew Pennell and Chad Brock and Joseph Brown and Wendell Challenger and Jon Eastman and Rich FitzJohn and Rich Glor and Gene Hunt and Liam Revell and Graham Slater and Josef Uyeda and Jason Weir and CRAN team}, - year = {2022}, - note = {R package version 2.0.9}, - url = {https://CRAN.R-project.org/package=geiger}, +@Manual{R-ellipse, + title = {ellipse: Functions for Drawing Ellipses and Ellipse-Like Confidence +Regions}, + author = {Duncan Murdoch and E. D. Chow}, + year = {2023}, + note = {R package version 0.5.0, +https://dmurdoch.github.io/ellipse/}, + url = {https://github.com/dmurdoch/ellipse}, } + @Manual{R-geomorph, - title = {geomorph: Geometric Morphometric Analyses of 2D/3D Landmark Data}, + title = {geomorph: Geometric Morphometric Analyses of 2D and 3D Landmark Data}, author = {Dean Adams and Michael Collyer and Antigoni Kaliontzopoulou and Erica Baken}, - year = {2022}, - note = {R package version 4.0.4}, + year = {2023}, + note = {R package version 4.0.6}, url = {https://github.com/geomorphR/geomorph}, } -@Manual{R-geoscale, - title = {geoscale: Geological Time Scale Plotting}, - author = {Mark A. Bell.}, - year = {2022}, - note = {R package version 2.0.1}, - url = {https://CRAN.R-project.org/package=geoscale}, -} @Manual{R-knitr, title = {knitr: A General-Purpose Package for Dynamic Report Generation in R}, author = {Yihui Xie}, - year = {2022}, - note = {R package version 1.39}, + year = {2023}, + note = {R package version 1.45}, url = {https://yihui.org/knitr/}, } -@Manual{R-maps, - title = {maps: Draw Geographical Maps}, - author = {Ray Brownrigg}, - year = {2021}, - note = {R package version 3.4.0}, - url = {https://CRAN.R-project.org/package=maps}, -} @Manual{R-Matrix, title = {Matrix: Sparse and Dense Matrix Classes and Methods}, author = {Douglas Bates and Martin Maechler and Mikael Jagan}, - year = {2022}, - note = {R package version 1.4-1}, - url = {https://CRAN.R-project.org/package=Matrix}, + year = {2023}, + note = {R package version 1.6-3}, + url = {https://Matrix.R-forge.R-project.org}, } @Manual{R-microbenchmark, title = {microbenchmark: Accurate Timing Functions}, author = {Olaf Mersmann}, - year = {2021}, - note = {R package version 1.4.9}, + year = {2023}, + note = {R package version 1.4.10}, url = {https://github.com/joshuaulrich/microbenchmark/}, } @Manual{R-phytools, title = {phytools: Phylogenetic Tools for Comparative Biology (and Other Things)}, author = {Liam J. Revell}, - year = {2022}, - note = {R package version 1.0-3}, + year = {2023}, + note = {R package version 2.0-3}, url = {https://github.com/liamrevell/phytools}, } @Manual{R-rgl, title = {rgl: 3D Visualization Using OpenGL}, author = {Daniel Adler and Duncan Murdoch}, - year = {2022}, - note = {R package version 0.108.3.2}, - url = {https://CRAN.R-project.org/package=rgl}, + year = {2023}, + note = {R package version 1.2.1}, + url = {https://github.com/dmurdoch/rgl}, } @Manual{R-rmarkdown, title = {rmarkdown: Dynamic Documents for R}, - author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone}, - year = {2022}, - note = {R package version 2.14}, - url = {https://CRAN.R-project.org/package=rmarkdown}, + author = {JJ Allaire and Yihui Xie and Christophe Dervieux and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone}, + year = {2023}, + note = {R package version 2.25, +https://pkgs.rstudio.com/rmarkdown/}, + url = {https://github.com/rstudio/rmarkdown}, } @Manual{R-RRPP, title = {RRPP: Linear Model Evaluation with Randomized Residuals in a Permutation Procedure}, author = {Michael Collyer and Dean Adams}, - year = {2022}, - note = {R package version 1.3.0}, + year = {2023}, + note = {R package version 1.4.0}, url = {https://github.com/mlcollyer/RRPP}, } -@Manual{R-scatterplot3d, - title = {scatterplot3d: 3D Scatter Plot}, - author = {Uwe Ligges and Martin Maechler and Sarah Schnackenberg}, - year = {2018}, - note = {R package version 0.3-41}, - url = {https://CRAN.R-project.org/package=scatterplot3d}, -} -@Manual{R-strap, - title = {strap: Stratigraphic Tree Analysis for Palaeontology}, - author = {Mark A. Bell and Graeme T. Lloyd}, - year = {2022}, - note = {R package version 1.6-0}, - url = {https://CRAN.R-project.org/package=strap}, -} @Manual{R-testthat, title = {testthat: Unit Testing for R}, author = {Hadley Wickham}, - year = {2022}, - note = {R package version 3.1.4}, - url = {https://CRAN.R-project.org/package=testthat}, + year = {2023}, + note = {R package version 3.2.0}, + url = {https://testthat.r-lib.org}, } @Manual{R-usethis, title = {usethis: Automate Package and Project Setup}, - author = {Hadley Wickham and Jennifer Bryan and Malcolm Barrett}, - year = {2021}, - note = {R package version 2.1.5}, - url = {https://CRAN.R-project.org/package=usethis}, + author = {Hadley Wickham and Jennifer Bryan and Malcolm Barrett and Andy Teucher}, + year = {2023}, + note = {R package version 2.2.0}, + url = {https://usethis.r-lib.org}, } + @Article{ape2019, title = {ape 5.0: an environment for modern phylogenetics and evolutionary analyses in {R}}, - author = {E. Paradis and K. Schliep}, + author = {Emmanuel Paradis and Klaus Schliep}, journal = {Bioinformatics}, year = {2019}, volume = {35}, pages = {526-528}, + doi = {10.1093/bioinformatics/bty633}, } @Book{bookdown2016, @@ -183,7 +155,7 @@ @Book{bookdown2016 publisher = {Chapman and Hall/CRC}, address = {Boca Raton, Florida}, year = {2016}, - note = {ISBN 978-1138700109}, + isbn = {978-1138700109}, url = {https://bookdown.org/yihui/bookdown}, } @@ -237,70 +209,74 @@ @Article{dispRity2020a @Article{geiger2009, title = {Nine exceptional radiations plus high turnover explain species diversity in jawed vertebrates}, - author = {ME Alfaro and F Santini and C Brock and H Alamillo and A Dornburg and DL Rabosky and G Carnevale and LJ Harmon}, - journal = {Proceedings of the National Academy of Sciences of the United States of America}, + author = {M. E. Alfaro and F. Santini and C. Brock and H. Alamillo and A. Dornburg and D. L. Rabosky and G. Carnevale and L. J. Harmon}, + journal = {PNAS}, year = {2009}, volume = {106}, pages = {13410-13414}, + doi = {10.1073/pnas.0811087106}, } @Article{geiger2011, title = {A novel comparative method for identifying shifts in the rate of character evolution on trees}, - author = {JM Eastman and ME Alfaro and P Joyce and AL Hipp and LJ Harmon}, + author = {J. M. Eastman and M. E Alfaro and P. Joyce and A. L. Hipp and L. J. Harmon}, journal = {Evolution}, year = {2011}, volume = {65}, pages = {3578-3589}, + doi = {10.1111/j.1558-5646.2011.01401.x}, } @Article{geiger2012, title = {Fitting models of continuous trait evolution to incompletely sampled comparative data using approximate Bayesian computation}, - author = {GJ Slater and LJ Harmon and D Wegmann and P Joyce and LJ Revell and ME Alfaro}, + author = {G. J. Slater and L. J. Harmon and D. Wegmann and P Joyce and L. J. Revell and M. E. Alfaro}, journal = {Evolution}, year = {2012}, volume = {66}, pages = {752-762}, + doi = {10.1111/j.1558-5646.2011.01474.x}, } @Article{geiger2008, title = {GEIGER: investigating evolutionary radiations}, - author = {LJ Harmon and JT Weir and CD Brock and RE Glor and W Challenger}, + author = {L. J. Harmon and J. T. Weir and C. D. Brock and R. E. Glor and W. Challenger}, journal = {Bioinformatics}, year = {2008}, volume = {24}, pages = {129-131}, + doi = {10.1093/bioinformatics/btm538}, } @Article{geiger2014, title = {geiger v2.0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees}, - author = {MW Pennell and JM Eastman and GJ Slater and JW Brown and JC Uyeda and RG Fitzjohn and ME Alfaro and LJ Harmon}, + author = {M. W. Pennell and J. M. Eastman and G. J. Slater and J. W. Brown and J. C. Uyeda and R. G. Fitzjohn and M. E. Alfaro and L. J. Harmon}, journal = {Bioinformatics}, year = {2014}, volume = {30}, pages = {2216-2218}, + doi = {10.1093/bioinformatics/btu181}, } -@Misc{geomorph2021a, +@Article{geomorph2021a, title = {geomorph v4.0 and gmShiny: enhanced analytics and a new graphical interface for a comprehensive morphometric experience.}, - author = {E.K. Baken and M.L. Collyer and A. Kaliontzopoulou and D.C. Adams}, - year = {2021}, + author = {E. K. Baken and M. L. Collyer and A. Kaliontzopoulou and D. C. Adams}, year = {2021}, journal = {Methods in Ecology and Evolution}, volume = {12}, pages = {2355-2363}, } -@Misc{geomorph2022a, - title = {Geomorph: Software for geometric morphometric analyses. R package version 4.0.4}, - author = {D.C. Adams and M.L. Collyer and A. Kaliontzopoulou and E.K. Baken}, - year = {2022}, +@Misc{geomorph2023a, + title = {Geomorph: Software for geometric morphometric analyses. R package version 4.0.6}, + author = {D. C. Adams and M. L. Collyer and A. Kaliontzopoulou and E. K. Baken}, + year = {2023}, url = { https://cran.r-project.org/package=geomorph}, } -@Misc{geomorph2021b, - title = {{RRPP}: Linear Model Evaluation with Randomized Residuals in a Permutation Procedure, R package version 1.1.2.}, +@Misc{geomorph2023b, + title = {{RRPP}: Linear Model Evaluation with Randomized Residuals in a Permutation Procedure, R package version 1.4.0.}, author = {M. L. Collyer and D. C. Adams}, - year = {2021}, + year = {2023}, url = {https://cran.r-project.org/package=RRPP}, } @@ -333,16 +309,16 @@ @InCollection{knitr2014 publisher = {Chapman and Hall/CRC}, year = {2014}, note = {ISBN 978-1466561595}, - url = {http://www.crcpress.com/product/isbn/9781466561595}, } @Article{phytools2012, - title = {phytools: An R package for phylogenetic comparative biology (and other things).}, + title = {phytools: An {R} package for phylogenetic comparative biology (and other things).}, author = {Liam J. Revell}, journal = {Methods in Ecology and Evolution}, year = {2012}, volume = {3}, pages = {217-223}, + doi = {10.1111/j.2041-210X.2011.00169.x}, } @Book{rmarkdown2018, @@ -351,7 +327,7 @@ @Book{rmarkdown2018 publisher = {Chapman and Hall/CRC}, address = {Boca Raton, Florida}, year = {2018}, - note = {ISBN 9781138359338}, + isbn = {9781138359338}, url = {https://bookdown.org/yihui/rmarkdown}, } @@ -361,25 +337,26 @@ @Book{rmarkdown2020 publisher = {Chapman and Hall/CRC}, address = {Boca Raton, Florida}, year = {2020}, - note = {ISBN 9780367563837}, + isbn = {9780367563837}, url = {https://bookdown.org/yihui/rmarkdown-cookbook}, } -@Misc{RRPP2019, - title = {{RRPP}: Linear Model Evaluation with Randomized Residuals in a Permutation Procedure. R package version 0.4.0.}, - author = {M. L. Collyer and D. C. Adams}, - year = {2019}, +@Manual{RRPP2023, + title = {{RRPP}: Linear Model Evaluation with Randomized Residuals in a Permutation Procedure. R package version 1.4.0.}, + author = {{M. L. Collyer D. C. Adams}}, + year = {2023}, url = {https://CRAN.R-project.org/package=RRPP}, } -@Misc{RRPP2018, - title = {{RRPP}: An R package for fitting linear models to high‐dimensional data using residual randomization. }, - author = {M. L. Collyer and D. C. Adams}, +@Article{RRPP2018, + title = {{RRPP}: An R package for fitting linear models to high‐dimensional data using residual randomization.}, + author = {{M. L. Collyer D. C. Adams}}, year = {2018}, journal = {Methods in Ecology and Evolution}, volume = {9}, issue = {2}, pages = {1772-1779}, + url = {https://doi.org/10.1111/2041-210X.13029}, } @Article{scatterplot3d2003, @@ -390,7 +367,7 @@ @Article{scatterplot3d2003 pages = {1--20}, number = {11}, volume = {8}, - url = {http://www.jstatsoft.org}, + doi = {10.18637/jss.v008.i11}, } @Article{strap2015, diff --git a/inst/vignettes/Developer_resources.Rmd b/inst/vignettes/Developer_resources.Rmd new file mode 100644 index 00000000..b4e71aa7 --- /dev/null +++ b/inst/vignettes/Developer_resources.Rmd @@ -0,0 +1,275 @@ +--- +title: "Resources for developers" +author: "Thomas Guillerme" +date: "`r Sys.Date()`" +output: + html_document: + fig_width: 12 + fig_height: 6 +--- + +If you want to copy, edit or modify the internal code in `dispRity` for your own project or for adding to the package (yay! in both cases) here is a list of resources that could help you understand some parts of the code. +I have been working on this package since more than 8 years so some parts inevitably ended up like [spaghetti](https://en.wikipedia.org/wiki/Spaghetti_code). +I am regularly working on streamlining and cleaning this internally (thanks to some [solid unit test](https://github.com/TGuillerme/dispRity/tree/master/tests/testthat)) but it takes some time. +Here are some resources that can help you (and future me!) with editing this ever growing project. + +```{r, echo = FALSE} +## The data +data <- read.csv(text = +"version, date, total, R_code, R_comments, manual_html, C +1.8, 2023/11, 46811, 20276, 10494, 12063, 404 +1.7, 2022/05, 41192, 18306, 9889, 10180, 404 +1.6, 2021/04, 27565, 15984, 8196, 9642, 280 +1.5, 2020/09, 26311, 14945, 7639, 9435, 280 +1.4, 2020/05, 22927, 13041, 7253, 7963, 280 +1.3, 2018/08, 20921, 6598, 3137, 7778, 131 +1.2, 2018/08, 17756, 10053, 6268, 6028, 131 +1.0, 2018/05, 15068, 8595, 6486, 4849, 124") + +## Probable bug with line count from 1.7 (using cloc rather than howlong) + +plot(rev(data[, "total"]), ylim = (range(data[, c(3:7)])), type = "l", ylab = "Total number of lines", xlab = "Version number (1.X)") +lines(rev(data[, "R_code"]), col = "orange") +lines(rev(data[, "manual_html"]), col = "blue") +legend("topleft", col = c("black", "orange", "blue"), lty = 1, legend = c("total (inc. comments, C, etc.)", "R (executable only)", "manual")) +``` + +# Coding style and naming conventions + +In general (there are some small exceptions here and there) I use the following code style: + + * objects are named and described with an underscore (e.g. `converted_matrix`, `output_results`, etc.); + * functions are named and described with a dot (e.g. `convert.matrix`, `output.results`, etc.); + * the indent is four spaces; + * I put spaces after a comma (`,`) and around attributors and evaluators (e.g. ` <- `, ` = `, ` %in% `, ` != `, `if() `, etc...) + * curly bracketed definitions (function, loops, ifs, whiles, etc...) are always indented over multiple lines as: +``` +if(something) { + something +} else { + something else +} +``` +or +``` +bib <- function(bob) { + return("bub") +} +``` +Unless they are pseudo `ifelse` like (e.g. `if(verbose) print("that's just one line. No else.")`) + +For naming files, up until `v1.8`, I've been writing the user level functions (with the Roxygen manual) in files named as `my.user.level.function.R` and the internal functions in `my.user.level.function_fun.R`. +This was originally used to track but separate clearly user level and internal level functions. +Since I am now more relying on the [function index](#function_index), I now put everything in the same `my.user.level.function.R` and clearly label the internal vs. user level functions in it. + +## Where to find specific functions? {#function_index} + +Each release I update a function index using the [`update.function.index.sh`](https://github.com/TGuillerme/dispRity/blob/master/update.function.index.sh) script. +You can find the latest index as a searchable .csv file in the [`function.index.csv`](https://github.com/TGuillerme/dispRity/blob/master/function.index.csv) file that contains a of the file names (first column), the lines where function is declared (second column) and the name of that declared function (third column): + +file | line | function +---|---|--- +Claddis.ordination.R | 55 | Claddis.ordination +Claddis.ordination_fun.R | 2 | convert.to.Claddis +MCMCglmm.subsets.R | 43 | MCMCglmm.subsets +MCMCglmm.subsets_fun.R | 2 | get.one.group +MCMCglmm.subsets_fun.R | 29 | split.term.name + +For example here, we know that the `Claddis.ordination` function is declared on line 55 in the `Claddis.ordination.R` file. +The `convert.to.Claddis` is declared in the 2nd line of the `Claddis.ordination_fun.R` file, etc. + +# The `dispRity` object structure {#dispRity_object} + +In brief, the `dispRity` object that's at the core of the package contains all the information to perform the different analyses in the package (and plotting, summarising, etc.). +It mainly contains the data (some `"matrix"` and sometimes some `"phylo"` objects) and then the eventually calculated disparity/dissimilarity metrics. +The whole object structure is detailed below: + +
    + +Full `dispRity` object structure (click to unroll) + +``` +object + | + \---$matrix* = class:"list" (a list containing the orginal matrix/matrices) + | | + | \---[[1]]* = class:"matrix" (the matrix (trait-space)) + | | + | \---[[...]] = class:"matrix" (any additional matrices) + | + | + \---$tree* = class:"multiPhylo" (a list containing the attached tree(s) or NULL) + | | + | \---[[1]] = class:"phylo" (the first tree) + | | + | \---[[...]] = class:"phylo" (any additional trees) + | + | + \---$call* = class:"list" (details of the methods used) + | | + | \---$dispRity.multi = class:"logical" + | | + | \---$subsets = class:"character" + | | + | \---$bootstrap = class:"character" + | | + | \---$dimensions = class:"numeric" + | | + | \---$metric = class:"list" (details about the metric(s) used) + | | + | \---$name = class:"character" + | | + | \---$fun = class:"list" (elements of class "function") + | | + | \---$arg = class:"list" + | + | + \---$subsets* = class:"list" (subsets as a list) + | | + | \---[[1]]* = class:"list" (first item in subsets list) + | | | + | | \---$elements* = class:"matrix" (one column matrix containing the elements within the first subset) + | | | + | | \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data) + | | | + | | \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level) + | | | + | | \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.) + | | + | \---[[2]] = class:"list" (second item in subsets list) + | | | + | | \---$elements* = class:"matrix" (one column matrix containing the elements within the second subset) + | | | + | | \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data) + | | | + | | \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level) + | | | + | | \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.) + | | + | \---[[...]] = class:"list" (the following subsets) + | | + | \---$elements* = class:"matrix" (a one column matrix containing the elements within this subset) + | | + | \---[[...]] = class:"matrix" (the rarefactions) + | + | + \---$covar = class:"list" (a list of subsets containing covar matrices; is the same length as $subsets) + | | + | \---[[1]] = class:"list" (first item in subsets list) + | | | + | | \---$VCV = class:"list" (the list of covar matrices) + | | | | + | | | \[[1]] = class:"matrix" (the first covar matrix) + | | | | + | | | \[[...]] = class:"matrix" (the subsequent covar matrices) + | | | + | | \---$loc = class:"list" (optional, the list of spatial location for the matrices) + | | | + | | \[[1]] = class:"numeric" (the coordinates for the location of the first matrix) + | | | + | | \[[...]] = class:"numeric" (the coordinates for the location of the subsequent covar matrices) + | | + | \---[[...]] = class:"list" (the following subsets) + | + | + \---$disparity + | + \---[[2]] = class:"list" (the first subsets) + | | + | \---$observed* = class:"numeric" (vector containing the observed disparity within the subsets) + | | + | \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data) + | | + | \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level) + | | + | \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.) + | + \---[[2]] = class:"list" (the first subsets) + | | + | \---$observed* = class:"numeric" (vector containing the observed disparity within the subsets) + | | + | \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data) + | | + | \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level) + | | + | \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.) + | + \---[[...]] = class:"list" (the following subsets) + | + \---$observed* = class:"numeric" (the vector containing the observed disparity within this subsets) + | + \---[[...]] = class:"matrix" (the rarefactions) +``` + +The elements marked with an asterisk (\*) are mandatory. + + +
    + +# `dispRity` function internal logic explained + +This is how disparity is calculated in `dispRity`. +The disparity calculations are handled by two main functions `mapply.wrapper` or `lapply.wrapper` depending if the metric is applied between groups or not (respectively). Here we are going to focus on `lapply.wrapper` but `mapply.wrapper` uses the same logic but intakes two lists instead of one. + +## The inputs + +The `lapply.wrapper` function intakes the following mandatory arguments: + + * `lapply_loop` (`"list"`) that contains the row names to analyse for each subsest (each element of the list is divided into different lists containing the observed rows as a n x 1 matrix and the bootstrapped rows as a n x bootstraps matrix as well as the rarefaction levels (see [`disparity_object.md`](https://github.com/TGuillerme/dispRity/blob/master/disparity_object.md) for details). If disparity was already previously calculated, `lapply_loop` contains disparity values (`"matrix"` or `"array"`) rather than row names. + * `metrics_list` (`"list"` of length 3) the list of the three levels of metrics to apply (some levels can be NULL). + * `data` (`"dispRity"`) the data that must contain at least the matrix or the disparity data and the number of dimensions (and also a tree if `metric_is_tree`). + * `matrix_decomposition` (`"logical"` of length 1) whether to decompose the matrix (see later) + * `verbose` (`"logical"` of length 1) whether to be verbose + * `metric_is_tree` (`"logical"` of length 3) whether each metric needs a tree + * `...` any arguments to be handled by the functions in `metrics_list` + +## The pipeline + +These arguments are passed to `lapply.wrapper` (or `mapply.wrapper`) which first toggles the functions to be verbose or not and then handles in turn the following (in a nested way): + +### 1 - Passing one subset (e.g. `lapply_loop[[1]]`) to `disparity.bootstraps` + +The `disparity.bootstraps` function handles subsets one by one, (e.g. `lapply_loop[[1]][[1]]`, etc...) and calculates disparity on them using the different metrics from `metrics_list`. + +This function is composed of two elements: firstly decomposing the matrix if necessary (see point 2 below) and secondly applying each metric depending on there level. The application of each metric is done simply by using an apply loop like `apply(data, margin, metric)` where `data` is already calculated disparity data (either from previous calculations or from decomposing the matrix), `margin` is detected automatically and `metric` comes from `metrics_list` according to the correct level. + +### 2 - Wrapping the matrix decomposition for one subset with `decompose.matrix.wrapper` + +This part transforms the raw data from `"dispRity"` (i.e. the row IDs from the subset, the requested number of dimensions and the original matrix) into the first requested disparity metric (with the appropriate requested level, e.g. if the metric contains `var`, the matrix decomposition transforms the `"dispRity"` data into a var/covar `"matrix"`). The metric to use to decompose the matrix (the one with the highest level) is detected with the `get.first.metric` function. Then both the `"dispRity"` data and the first metric are passed to `decompose.matrix.wrapper`. + +This function basically feeds the number of subsets (i.e. either elements (n x 1) or the bootstraps/rarefaction (n x bootstraps) - see [`disparity_object.md`](#dispRity_object) for details) to the `decompose.matrix` function along with the first metric. +The wrapper does also a bit of data/format wrangling I'm not going to detail here (and can hopefully be understood reading the code). + +### 3 - Decomposing the matrix with `decompose.matrix` + +This function intakes: + + * `one_subsets_bootstraps` that is one row from one element of the subset list (e.g. `lapply_loop[[1]][[1]][, 1]`). Effectively, these are the row IDs to be considered when calculating the disparity metric. + * `fun` the first disparity metric + * `data` the `"dispRity"` object + * `nrow` an option for between groups calculation. Is `NULL` when the decomposition is for a normal metric (but is an indicator of the pairs of row to consider if the metric is `between.groups = TRUE`). + +And then applies the `decompose` function to each matrix in `data$matrix`. Which simply applies the metric to the matrix in the following format (i.e. `fun(matrix[rows, columns])`). + +# Package versions + +Version numbering is using the `x.y.z` format with each number designating where we're at in terms of development in that version of the development cycle: + + * `x` is for the core design of the package. This is very unlikely to change since the change from `v0` to `v1` unless the whole core of the `dispRity` function is changed. In a future where me and my collaborators have all the time that exist and no pressures from academia, then this would change when switching to a more efficient version of the core version (I'm looking at you `C`). + * `y` is for the big functionality upgrades. This changes every year or so when the package gets a new set of functionalities. For example, handling a new type of data, implementing a new analysis pipeline, etc. The [`NEWS.md` file](https://github.com/TGuillerme/dispRity/blob/master/NEWS.md) ends up tracking these version over the long time (i.e. you don't get the change log for versions `1.7.12` but directly for `1.7` and `1.8`). + * `z` us for tracking all the latest updates. This changes when I get time but can be every week! This helps users tracking the latest version, especially when I introduce a small new functionality or a bug fix (and making sure they use now the correct version). + +## master branch + +The most up to date and complete version is always on the [master branch on GitHub](https://github.com/TGuillerme/dispRity/tree/master). +This branch has [continuous integration with R CMD check](https://github.com/TGuillerme/dispRity/blob/master/.github/workflows/R-CMD-check.yaml) and [unit testing with codecov](https://github.com/TGuillerme/dispRity/blob/master/.github/workflows/test-coverage.yaml) via github actions. + +## release branch + +The [release branch on GitHub](https://github.com/TGuillerme/dispRity/tree/release) has also the same github actions as the master but is updated less regularly. + +## CRAN release + +Finally the CRAN release is updated only for major releases or for critical bug fixes. +This release does not contain the unit test and the manual to make the export to CRAN smoother and easier (but is based on the github actions from the release branch). +The exports to CRAN are automatised using the [`export.cran.sh`](https://github.com/TGuillerme/dispRity/blob/master/export.cran.sh). \ No newline at end of file diff --git a/inst/vignettes/Developer_resources.html b/inst/vignettes/Developer_resources.html new file mode 100644 index 00000000..eb6710bf --- /dev/null +++ b/inst/vignettes/Developer_resources.html @@ -0,0 +1,659 @@ + + + + + + + + + + + + + + + +Resources for developers + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + +

    If you want to copy, edit or modify the internal code in dispRity for your own project or for adding to the package (yay! in both cases) here is a list of resources that could help you understand some parts of the code. I have been working on this package since more than 8 years so some parts inevitably ended up like spaghetti. I am regularly working on streamlining and cleaning this internally (thanks to some solid unit test) but it takes some time. Here are some resources that can help you (and future me!) with editing this ever growing project.

    +

    +
    +

    Coding style and naming conventions

    +

    In general (there are some small exceptions here and there) I use the following code style:

    +
      +
    • objects are named and described with an underscore (e.g. converted_matrix, output_results, etc.);
      • +
    • functions are named and described with a dot (e.g. convert.matrix, output.results, etc.);
      • +
    • the indent is four spaces;
      • +
    • I put spaces after a comma (,) and around attributors and evaluators (e.g. <-, =, %in%, !=, if(), etc…)
      • +
    • curly bracketed definitions (function, loops, ifs, whiles, etc…) are always indented over multiple lines as:
      • +
    +
    if(something) {
+    something
+} else {
+    something else
+}
    +

    or

    +
    bib <- function(bob) {
+    return("bub")
+}
    +

    Unless they are pseudo ifelse like (e.g. if(verbose) print("that's just one line. No else."))

    +

    For naming files, up until v1.8, I’ve been writing the user level functions (with the Roxygen manual) in files named as my.user.level.function.R and the internal functions in my.user.level.function_fun.R. This was originally used to track but separate clearly user level and internal level functions. Since I am now more relying on the function index, I now put everything in the same my.user.level.function.R and clearly label the internal vs. user level functions in it.

    +
    +

    Where to find specific functions?

    +

    Each release I update a function index using the update.function.index.sh script. You can find the latest index as a searchable .csv file in the function.index.csv file that contains a of the file names (first column), the lines where function is declared (second column) and the name of that declared function (third column):

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    filelinefunction
    Claddis.ordination.R55Claddis.ordination
    Claddis.ordination_fun.R2convert.to.Claddis
    MCMCglmm.subsets.R43MCMCglmm.subsets
    MCMCglmm.subsets_fun.R2get.one.group
    MCMCglmm.subsets_fun.R29split.term.name
    +

    For example here, we know that the Claddis.ordination function is declared on line 55 in the Claddis.ordination.R file. The convert.to.Claddis is declared in the 2nd line of the Claddis.ordination_fun.R file, etc.

    +
    +
    +
    +

    The dispRity object structure

    +

    In brief, the dispRity object that’s at the core of the package contains all the information to perform the different analyses in the package (and plotting, summarising, etc.). It mainly contains the data (some "matrix" and sometimes some "phylo" objects) and then the eventually calculated disparity/dissimilarity metrics. The whole object structure is detailed below:

    +
    +

    Full dispRity object structure (click to unroll)

    +
    object
+    |
+    \---$matrix* = class:"list" (a list containing the orginal matrix/matrices)
+    |   |
+    |   \---[[1]]* = class:"matrix" (the matrix (trait-space))
+    |       |
+    |       \---[[...]] = class:"matrix" (any additional matrices)
+    |
+    |
+    \---$tree* = class:"multiPhylo" (a list containing the attached tree(s) or NULL)
+    |   |
+    |   \---[[1]] = class:"phylo" (the first tree)
+    |       |
+    |       \---[[...]] = class:"phylo" (any additional trees)  
+    |
+    |
+    \---$call* = class:"list" (details of the methods used)
+    |   |
+    |   \---$dispRity.multi = class:"logical"
+    |   |
+    |   \---$subsets = class:"character"
+    |   |
+    |   \---$bootstrap = class:"character"
+    |   |
+    |   \---$dimensions = class:"numeric"
+    |   |
+    |   \---$metric = class:"list" (details about the metric(s) used)
+    |              |
+    |              \---$name = class:"character"
+    |              |
+    |              \---$fun = class:"list" (elements of class "function")
+    |              |
+    |              \---$arg = class:"list"
+    |
+    |
+    \---$subsets* = class:"list" (subsets as a list)
+    |   |
+    |   \---[[1]]* = class:"list" (first item in subsets list)
+    |   |   |
+    |   |   \---$elements* = class:"matrix" (one column matrix containing the elements within the first subset)
+    |   |   |
+    |   |   \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data)
+    |   |   |
+    |   |   \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level)
+    |   |   |
+    |   |   \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.)
+    |   |
+    |   \---[[2]] = class:"list" (second item in subsets list)
+    |   |   |
+    |   |   \---$elements* = class:"matrix" (one column matrix containing the elements within the second subset)
+    |   |   |
+    |   |   \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data)
+    |   |   |
+    |   |   \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level)
+    |   |   |
+    |   |   \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.)          
+    |   |
+    |   \---[[...]] = class:"list" (the following subsets)
+    |       |
+    |       \---$elements* = class:"matrix" (a one column matrix containing the elements within this subset)
+    |       |
+    |       \---[[...]] = class:"matrix" (the rarefactions)
+    |
+    |
+    \---$covar = class:"list" (a list of subsets containing covar matrices; is the same length as $subsets)
+    |   |
+    |   \---[[1]] = class:"list" (first item in subsets list)
+    |   |   |
+    |   |   \---$VCV = class:"list" (the list of covar matrices)
+    |   |   |   |
+    |   |   |   \[[1]] = class:"matrix" (the first covar matrix)
+    |   |   |   |
+    |   |   |   \[[...]] = class:"matrix" (the subsequent covar matrices)
+    |   |   |
+    |   |   \---$loc = class:"list" (optional, the list of spatial location for the matrices)
+    |   |       |
+    |   |       \[[1]] = class:"numeric" (the coordinates for the location of the first matrix)
+    |   |       |
+    |   |       \[[...]] = class:"numeric" (the coordinates for the location of the subsequent covar matrices)
+    |   |   
+    |   \---[[...]] = class:"list" (the following subsets)
+    |
+    |   
+    \---$disparity
+        |
+        \---[[2]] = class:"list" (the first subsets)
+        |   |
+        |   \---$observed* = class:"numeric" (vector containing the observed disparity within the subsets)
+        |   |
+        |   \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data)
+        |   |
+        |   \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level)
+        |   |
+        |   \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.)
+        |
+        \---[[2]] = class:"list" (the first subsets)
+        |   |
+        |   \---$observed* = class:"numeric" (vector containing the observed disparity within the subsets)
+        |   |
+        |   \---[[2]] = class:"matrix" (matrix containing the bootstrap draws for the unrarefied data)
+        |   |
+        |   \---[[3]] = class:"matrix" (matrix containing the bootstrap draws for the first rarefaction level)
+        |   |
+        |   \---[[...]] = class:"matrix" (matrix containing the bootstrap draws for the second rarefaction level etc.)          
+        |
+        \---[[...]] = class:"list" (the following subsets)
+            |
+            \---$observed* = class:"numeric" (the vector containing the observed disparity within this subsets)
+            |
+            \---[[...]] = class:"matrix" (the rarefactions)
    +

    The elements marked with an asterisk (*) are mandatory.

    +
    +
    +
    +

    dispRity function internal logic explained

    +

    This is how disparity is calculated in dispRity. The disparity calculations are handled by two main functions mapply.wrapper or lapply.wrapper depending if the metric is applied between groups or not (respectively). Here we are going to focus on lapply.wrapper but mapply.wrapper uses the same logic but intakes two lists instead of one.

    +
    +

    The inputs

    +

    The lapply.wrapper function intakes the following mandatory arguments:

    +
      +
    • lapply_loop ("list") that contains the row names to analyse for each subsest (each element of the list is divided into different lists containing the observed rows as a n x 1 matrix and the bootstrapped rows as a n x bootstraps matrix as well as the rarefaction levels (see disparity_object.md for details). If disparity was already previously calculated, lapply_loop contains disparity values ("matrix" or "array") rather than row names.
      • +
    • metrics_list ("list" of length 3) the list of the three levels of metrics to apply (some levels can be NULL).
      • +
    • data ("dispRity") the data that must contain at least the matrix or the disparity data and the number of dimensions (and also a tree if metric_is_tree).
      • +
    • matrix_decomposition ("logical" of length 1) whether to decompose the matrix (see later)
      • +
    • verbose ("logical" of length 1) whether to be verbose
      • +
    • metric_is_tree ("logical" of length 3) whether each metric needs a tree
      • +
    • ... any arguments to be handled by the functions in metrics_list
      • +
    +
    +
    +

    The pipeline

    +

    These arguments are passed to lapply.wrapper (or mapply.wrapper) which first toggles the functions to be verbose or not and then handles in turn the following (in a nested way):

    +
    +

    1 - Passing one subset (e.g. lapply_loop[[1]]) to disparity.bootstraps

    +

    The disparity.bootstraps function handles subsets one by one, (e.g. lapply_loop[[1]][[1]], etc…) and calculates disparity on them using the different metrics from metrics_list.

    +

    This function is composed of two elements: firstly decomposing the matrix if necessary (see point 2 below) and secondly applying each metric depending on there level. The application of each metric is done simply by using an apply loop like apply(data, margin, metric) where data is already calculated disparity data (either from previous calculations or from decomposing the matrix), margin is detected automatically and metric comes from metrics_list according to the correct level.

    +
    +
    +

    2 - Wrapping the matrix decomposition for one subset with decompose.matrix.wrapper

    +

    This part transforms the raw data from "dispRity" (i.e. the row IDs from the subset, the requested number of dimensions and the original matrix) into the first requested disparity metric (with the appropriate requested level, e.g. if the metric contains var, the matrix decomposition transforms the "dispRity" data into a var/covar "matrix"). The metric to use to decompose the matrix (the one with the highest level) is detected with the get.first.metric function. Then both the "dispRity" data and the first metric are passed to decompose.matrix.wrapper.

    +

    This function basically feeds the number of subsets (i.e. either elements (n x 1) or the bootstraps/rarefaction (n x bootstraps) - see disparity_object.md for details) to the decompose.matrix function along with the first metric. The wrapper does also a bit of data/format wrangling I’m not going to detail here (and can hopefully be understood reading the code).

    +
    +
    +

    3 - Decomposing the matrix with decompose.matrix

    +

    This function intakes:

    +
      +
    • one_subsets_bootstraps that is one row from one element of the subset list (e.g. lapply_loop[[1]][[1]][, 1]). Effectively, these are the row IDs to be considered when calculating the disparity metric.
      • +
    • fun the first disparity metric
      • +
    • data the "dispRity" object
      • +
    • nrow an option for between groups calculation. Is NULL when the decomposition is for a normal metric (but is an indicator of the pairs of row to consider if the metric is between.groups = TRUE).
      • +
    +

    And then applies the decompose function to each matrix in data$matrix. Which simply applies the metric to the matrix in the following format (i.e. fun(matrix[rows, columns])).

    +
    +
    +
    +
    +

    Package versions

    +

    Version numbering is using the x.y.z format with each number designating where we’re at in terms of development in that version of the development cycle:

    +
      +
    • x is for the core design of the package. This is very unlikely to change since the change from v0 to v1 unless the whole core of the dispRity function is changed. In a future where me and my collaborators have all the time that exist and no pressures from academia, then this would change when switching to a more efficient version of the core version (I’m looking at you C).
      • +
    • y is for the big functionality upgrades. This changes every year or so when the package gets a new set of functionalities. For example, handling a new type of data, implementing a new analysis pipeline, etc. The NEWS.md file ends up tracking these version over the long time (i.e. you don’t get the change log for versions 1.7.12 but directly for 1.7 and 1.8).
      • +
    • z us for tracking all the latest updates. This changes when I get time but can be every week! This helps users tracking the latest version, especially when I introduce a small new functionality or a bug fix (and making sure they use now the correct version).
      • +
    +
    +

    master branch

    +

    The most up to date and complete version is always on the master branch on GitHub. This branch has continuous integration with R CMD check and unit testing with codecov via github actions.

    +
    +
    +

    release branch

    +

    The release branch on GitHub has also the same github actions as the master but is updated less regularly.

    +
    +
    +

    CRAN release

    +

    Finally the CRAN release is updated only for major releases or for critical bug fixes. This release does not contain the unit test and the manual to make the export to CRAN smoother and easier (but is based on the github actions from the release branch). The exports to CRAN are automatised using the export.cran.sh.

    +
    +
    + + + + +
    + + + + + + + + + + + + + + + diff --git a/inst/vignettes/Projection_analysis.html b/inst/vignettes/Projection_analysis.html old mode 100644 new mode 100755 diff --git a/man/BeckLee.Rd b/man/BeckLee.Rd index 4c037887..3153b090 100755 --- a/man/BeckLee.Rd +++ b/man/BeckLee.Rd @@ -10,9 +10,6 @@ \format{ three matrices and one phylogenetic tree. } -\source{ -\url{https://www.royalsocietypublishing.org/doi/abs/10.1098/rspb.2014.1278} -} \description{ Example datasets from Beck and Lee 2014. } diff --git a/man/MCMCglmm.utilities.Rd b/man/MCMCglmm.utilities.Rd index 6e1d352f..8494b7c5 100755 --- a/man/MCMCglmm.utilities.Rd +++ b/man/MCMCglmm.utilities.Rd @@ -6,6 +6,7 @@ \alias{MCMCglmm.traits} \alias{MCMCglmm.sample} \alias{MCMCglmm.covars} +\alias{MCMCglmm.variance} \title{MCMCglmm object utility functions} \usage{ MCMCglmm.traits(MCMCglmm) @@ -15,6 +16,8 @@ MCMCglmm.levels(MCMCglmm, convert) MCMCglmm.sample(MCMCglmm, n) MCMCglmm.covars(MCMCglmm, n, sample) + +MCMCglmm.variance(MCMCglmm, n, sample, levels, scale) } \arguments{ \item{MCMCglmm}{A \code{MCMCglmm} object.} @@ -24,6 +27,10 @@ MCMCglmm.covars(MCMCglmm, n, sample) \item{n}{Optional, a number of random samples to extract.} \item{sample}{Optional, the specific samples to extract (is ignored if \code{n} is present).} + +\item{levels}{Optional, a vector \code{"character"} values (matching \code{MCMCglmm.levels(..., convert = TRUE)}) or of \code{"numeric"} values designating which levels to be used to calculate the variance (if left empty, all the levels are used).} + +\item{scale}{Logical, whether to scale the variance relative to all the levels (\code{TRUE}; default) or not (\code{FALSE})/} } \description{ Different utility functions to extract aspects of a \code{MCMCglmm} object. @@ -33,7 +40,8 @@ Different utility functions to extract aspects of a \code{MCMCglmm} object. \item \code{MCMCglmm.levels} returns the different random and residual terms levels of a \code{MCMCglmm} object. This function uses the default option \code{convert = TRUE} to convert the names into something more readable. Toggle to \code{convert = FALSE} for the raw names. \item \code{MCMCglmm.traits} returns the column names of the different traits of a \code{MCMCglmm} formula object. \item \code{MCMCglmm.sample} returns a vector of sample IDs present in the \code{MCMCglmm} object. If \code{n} is missing, all the samples IDs are returned. Else, a random series of sample IDs are returned (with replacement if n greater than the number of available samples). - \item \code{MCMCglmm.covars} returns a list of covariance matrices and intercepts from a \code{MCMCglmm} object (respectively from \code{MCMCglmm$VCV} and \code{MCMCglmm$Sol}). By default, all the covariance matrices and intercepts are returned but you can use either of the arguments \code{sample} to return specific samples (e.g. \code{MCMCglmm.covars(data, sample = c(1, 42))} for returning the first and 42nd samples) or \code{n} to return a specific number of random samples (e.g. \code{MCMCglmm.covars(data, n = 42)} for returning 42 random samples). + \item \code{MCMCglmm.covars} returns a list of covariance matrices and intercepts from a \code{MCMCglmm} object (respectively from \code{MCMCglmm$VCV} and \code{MCMCglmm$Sol}). By default, all the covariance matrices and intercepts are returned but you can use either of the arguments \code{sample} to return specific samples (e.g. \code{MCMCglmm.covars(data, sample = c(1, 42))} for returning the first and 42nd samples) or \code{n} to return a specific number of random samples (e.g. \code{MCMCglmm.covars(data, n = 42)} for returning 42 random samples). + \item \code{MCMCglmm.variance} returns a list of covariance matrices and intercepts from a \code{MCMCglmm} object (respectively from \code{MCMCglmm$VCV} and \code{MCMCglmm$Sol}). By default, all the covariance matrices and intercepts are returned but you can use either of the arguments \code{sample} to return specific samples (e.g. \code{MCMCglmm.covars(data, sample = c(1, 42))} for returning the first and 42nd samples) or \code{n} to return a specific number of random samples (e.g. \code{MCMCglmm.covars(data, n = 42)} for returning 42 random samples). } } \examples{ @@ -59,7 +67,6 @@ MCMCglmm.sample(model, n = 5) MCMCglmm.covars(model, sample = 42) ## Get two random samples from the model MCMCglmm.covars(model, n = 2) - } \seealso{ \code{\link{MCMCglmm.subsets}} diff --git a/man/add.tree.Rd b/man/add.tree.Rd index 80c297f6..92716536 100755 --- a/man/add.tree.Rd +++ b/man/add.tree.Rd @@ -2,13 +2,13 @@ % Please edit documentation in R/dispRity.utilities.R \name{add.tree} \alias{add.tree} -\alias{get.tree} \alias{remove.tree} -\title{Add, get or remove tree} +\alias{get.tree} +\title{Add, remove or get trees (or subtrees)} \usage{ -add.tree(data, tree) +add.tree(data, tree, replace = FALSE) -get.tree(data) +get.tree(data, subsets = FALSE, to.root = FALSE) remove.tree(data) } @@ -16,9 +16,18 @@ remove.tree(data) \item{data}{A \code{dispRity} object.} \item{tree}{A \code{phylo} or \code{mutiPhylo} object.} + +\item{replace}{Logical, whether to replace any existing tree (\code{TRUE}) or add to it (\code{FALSE}; default).} + +\item{subsets}{Either a logical whether to extract the tree for each subset (\code{TRUE}) or not (\code{FALSE}; default) or specific subset names or numbers.} + +\item{to.root}{Logical, whether to return the subset tree including the root of the tree (\code{TRUE}) or only containing the elements in the subset (and their most recent common ancestor; \code{FALSE}; default). If \code{data} contains time bins (from \code{\link{chrono.subsets}} with \code{method = "discrete"}), and \code{to.root = FALSE} it returns the subtrees containing only what's in the bin.} } \description{ -Adding, extracting or removing the tree component from a \code{dispRity} object +Adding, extracting or removing the tree component from a \code{dispRity} object. +} +\details{ +\code{get.tree} allows to extract the trees specific to each subsets. } \examples{ ## Loading a dispRity object @@ -38,10 +47,18 @@ tree_data <- add.tree(tree_data, tree = BeckLee_tree) ## Extracting the tree get.tree(tree_data) # is a "phylo" object +## Adding the same tree again +tree_data <- add.tree(tree_data, tree = BeckLee_tree) +get.tree(tree_data) # is a "multiPhylo" object (2 trees) + +## Replacing the two trees by one tree +tree_data <- add.tree(tree_data, tree = BeckLee_tree, replace = TRUE) +get.tree(tree_data) # is a "phylo" object + } \seealso{ \code{\link{custom.subsets}}, \code{\link{chrono.subsets}}, \code{\link{boot.matrix}}, \code{\link{dispRity}}. } \author{ -Thomas Guillerme +Thomas Guillerme and Jack Hadfield } diff --git a/man/adonis.dispRity.Rd b/man/adonis.dispRity.Rd index 4edff196..b5059233 100755 --- a/man/adonis.dispRity.Rd +++ b/man/adonis.dispRity.Rd @@ -87,11 +87,10 @@ adonis.dispRity(time_subsets, matrix ~ time) ## Running the NPMANOVA with each time bin as a predictor adonis.dispRity(time_subsets, matrix ~ chrono.subsets) + } \seealso{ \code{\link[vegan]{adonis2}}, \code{\link{test.dispRity}}, \code{\link{custom.subsets}}, \code{\link{chrono.subsets}}. - -\code{\link{test.dispRity}}, \code{\link{custom.subsets}}, \code{\link{chrono.subsets}} } \author{ Thomas Guillerme diff --git a/man/bhatt.coeff.Rd b/man/bhatt.coeff.Rd index a81b14bf..06464ca8 100755 --- a/man/bhatt.coeff.Rd +++ b/man/bhatt.coeff.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/bhatt.coeff.R \name{bhatt.coeff} \alias{bhatt.coeff} -\title{Bhattacharrya Coefficient} +\title{Bhattacharyya Coefficient} \usage{ bhatt.coeff(x, y, bw = bw.nrd0, ...) } diff --git a/man/boot.matrix.Rd b/man/boot.matrix.Rd index 92bc1c5b..ae8398a4 100755 --- a/man/boot.matrix.Rd +++ b/man/boot.matrix.Rd @@ -8,10 +8,10 @@ boot.matrix( data, bootstraps = 100, rarefaction = FALSE, - dimensions, + dimensions = NULL, verbose = FALSE, boot.type = "full", - prob + prob = NULL ) } \arguments{ @@ -50,6 +50,7 @@ When the input is \code{"min"}, the smallest number of elements is used (or 3 if \itemize{ \item \code{"full"}: resamples all the rows of the matrix and replaces them with a new random sample of rows (with \code{replace = TRUE}, meaning all the elements can be duplicated in each bootstrap). \item \code{"single"}: resamples only one row of the matrix and replaces it with a new randomly sampled row (with \code{replace = FALSE}, meaning that only one element can be duplicated in each bootstrap). + \item \code{"null"}: resamples all rows of the matrix across subsets. I.e. for each subset of \emph{n} elements, this algorithm resamples \emph{n} elements across \emph{ALL} subsets. If only one subset (or none) is used, this does the same as the \code{"full"} algorithm. } \code{prob}: This option allows to attribute specific probability to each element to be drawn. diff --git a/man/char.diff.Rd b/man/char.diff.Rd index d5017d08..71037f42 100755 --- a/man/char.diff.Rd +++ b/man/char.diff.Rd @@ -16,7 +16,7 @@ char.diff( ) } \arguments{ -\item{matrix}{A discrete matrix or a list containing discrete characters. The differences is calculated between the columns (usually characters). Use \code{t(matrix)} to calculate the differences between the rows.} +\item{matrix}{A discrete matrix or a list containing discrete characters. The differences is calculated between the columns (usually characters). Use \code{t(matrix)} or \code{by.col = FALSE} to calculate the differences between the rows.} \item{method}{The method to measure difference: \code{"hamming"} (default; Hamming 1950), \code{"manhattan"}, \code{"comparable"}, \code{"euclidean"}, \code{"maximum"}, \code{"mord"} (Lloyd 2016), \code{"none"} or \code{"binary"}.} diff --git a/man/chrono.subsets.Rd b/man/chrono.subsets.Rd index 4b4b2654..d8395196 100755 --- a/man/chrono.subsets.Rd +++ b/man/chrono.subsets.Rd @@ -8,12 +8,12 @@ \usage{ chrono.subsets( data, - tree, + tree = NULL, method, time, model, inc.nodes = FALSE, - FADLAD, + FADLAD = NULL, verbose = FALSE, t0 = FALSE, bind.data = FALSE @@ -22,7 +22,7 @@ chrono.subsets( \arguments{ \item{data}{A \code{matrix} or a \code{list} of matrices.} -\item{tree}{A \code{phylo} or a \code{multiPhylo} object matching the data and with a \code{root.time} element. This argument can be left missing if \code{method = "discrete"} and all elements are present in the optional \code{FADLAD} argument.} +\item{tree}{\code{NULL} (default) or an optional \code{phylo} or \code{multiPhylo} object matching the data and with a \code{root.time} element. This argument can be left missing if \code{method = "discrete"} and all elements are present in the optional \code{FADLAD} argument.} \item{method}{The time subsampling method: either \code{"discrete"} (or \code{"d"}) or \code{"continuous"} (or \code{"c"}).} @@ -32,7 +32,7 @@ chrono.subsets( \item{inc.nodes}{A \code{logical} value indicating whether nodes should be included in the time subsets. Is ignored if \code{method = "continuous"}.} -\item{FADLAD}{An optional \code{data.frame} containing the first and last occurrence data.} +\item{FADLAD}{\code{NULL} (default) or an optional \code{data.frame} or \code{list} of \code{data.frame}s containing the first and last occurrence data.} \item{verbose}{A \code{logical} value indicating whether to be verbose or not. Is ignored if \code{method = "discrete"}.} diff --git a/man/covar.plot.Rd b/man/covar.plot.Rd index 85a6e1aa..19af83c4 100755 --- a/man/covar.plot.Rd +++ b/man/covar.plot.Rd @@ -60,7 +60,7 @@ The argument \code{centres} allows to determine how to calculate the centre of e \itemize{ \item A \code{function} to calculate the centre from a group like the default \code{colMeans} function that calculates the centroid coordinates of each group; \item A \code{numeric} value to be replicated as the coordinates for the centre of each group (e.g. \code{centres = 0} sets all the centres at the coordinates \code{c(0,0,0,...)}); or a vector of numeric values to be directly used as the coordinates for each group (e.g. \code{centres = c(1,2,3)} sets all the centres at the coordinates \code{c(1,2,3)}); or a list of numeric values or numeric vectors to be used as the coordinates for the centres of each group; - \item code{"intercept"} for using the estimated posterior intercept for each sample. + \item \code{"intercept"} for using the estimated posterior intercept for each sample. } \emph{NOTE} that if the input contains more dimensions than the visualised dimensions (by default \code{dimensions = c(1,2)}) the ellipses and major axes are projected from an n-dimensional space onto a 2D space which might make them look incorrect. diff --git a/man/crown.stem.Rd b/man/crown.stem.Rd index 06f84afb..abaf8aa4 100755 --- a/man/crown.stem.Rd +++ b/man/crown.stem.Rd @@ -7,7 +7,7 @@ crown.stem(tree, inc.nodes = TRUE, output.names = TRUE) } \arguments{ -\item{tree}{a code{"phylo"} object} +\item{tree}{a \code{"phylo"} object} \item{inc.nodes}{whether to include the nodes (\code{TRUE}; default) or not (\code{FALSE}) in the output.} diff --git a/man/custom.subsets.Rd b/man/custom.subsets.Rd index d010b6e3..7a14e6a4 100755 --- a/man/custom.subsets.Rd +++ b/man/custom.subsets.Rd @@ -7,14 +7,14 @@ \alias{cust.subsets} \title{Separating data into custom subsets.} \usage{ -custom.subsets(data, group, tree) +custom.subsets(data, group, tree = NULL) } \arguments{ \item{data}{A \code{matrix} or a \code{list} of matrices.} \item{group}{Either a \code{list} of row numbers or names to be used as different groups, a \code{data.frame} with the same \eqn{k} elements as in \code{data} as rownames or a \code{factor} vector. If \code{group} is a \code{phylo} object matching \code{data}, groups are automatically generated as clades (and the tree is attached to the resulting \code{dispRity} object).} -\item{tree}{Optional, a \code{phylo} or \code{multiPhylo} object to attach to the resulting \code{dispRity} data.} +\item{tree}{\code{NULL} (default) or an optional \code{phylo} or \code{multiPhylo} object to be attached to the data.} } \description{ Splits the data into a customized subsets list. diff --git a/man/dispRity.Rd b/man/dispRity.Rd index efe5fc8c..b821bc46 100755 --- a/man/dispRity.Rd +++ b/man/dispRity.Rd @@ -7,7 +7,7 @@ dispRity( data, metric, - dimensions, + dimensions = NULL, ..., between.groups = FALSE, verbose = FALSE, diff --git a/man/dispRity.covar.projections.Rd b/man/dispRity.covar.projections.Rd index 1e0f06d8..cb214f79 100755 --- a/man/dispRity.covar.projections.Rd +++ b/man/dispRity.covar.projections.Rd @@ -33,11 +33,11 @@ dispRity.covar.projections( \item{level}{the confidence interval to estimate the major axis (default is \code{0.95}; see \code{\link{axis.covar}} for more details)).} -\item{output}{which values to output from the projection. By default, the three values \code{c("position", "distance", "degree")} are used to respectively output the projection, rejection and angle values (see \code{\link{projections}} for more details).} +\item{output}{which values to output from the projection. By default, the three values \code{c("position", "distance", "degree")} are used to respectively output the projection, rejection and angle values (see \code{\link{projections}} for more details). The argument \code{"orthogonality"} can also be added to this vector.} \item{inc.base}{logical, when using \code{type = "elements"} with a supplied \code{base} argument, whether to also calculate the projections for the base group (\code{TRUE}) or not (\code{FALSE}; default).} -\item{...}{any optional arguments to pass to \code{\link{projections}} (such as \code{centre} or \code{abs}).} +\item{...}{any optional arguments to pass to \code{\link{projections}} (such as \code{centre} or \code{abs}). \emph{NOTE that this function uses by default \code{centre = TRUE} and \code{abs = TRUE} which are not the defaults for \code{\link{projections}}}.} \item{verbose}{logical, whether to be verbose (\code{TRUE}) or not (\code{FALSE}, default).} } diff --git a/man/dispRity.metric.Rd b/man/dispRity.metric.Rd index 5df8e102..9cd82bf1 100755 --- a/man/dispRity.metric.Rd +++ b/man/dispRity.metric.Rd @@ -10,6 +10,7 @@ \alias{ranges} \alias{centroids} \alias{mode.val} +\alias{ellipsoid.volume} \alias{ellipse.volume} \alias{edge.length.tree} \alias{convhull.surface} @@ -33,6 +34,7 @@ \alias{projections.tree} \alias{projections.between} \alias{disalignment} +\alias{roundness} \title{Disparity metrics} \usage{ dimension.level3.fun(matrix, ...) @@ -43,7 +45,7 @@ between.groups.fun(matrix, matrix2, ...) \arguments{ \item{matrix}{A matrix.} -\item{...}{Optional arguments to be passed to the function. Usual optional arguments are \code{method} for specifying the method for calculating distance passed to \code{\link[vegan]{vegdist}} (e.g. \code{method = "euclidean"} - default - or \code{method = "manhattan"}) or \code{k.root} to scale the result using the eqn{kth} root. See details below for available optional arguments for each function.} +\item{...}{Optional arguments to be passed to the function. Usual optional arguments are \code{method} for specifying the method for calculating distance passed to \code{\link[vegan]{vegdist}} (e.g. \code{method = "euclidean"} - default - or \code{method = "manhattan"}) or \code{k.root} to scale the result using the \eqn{kth} root. See details below for available optional arguments for each function.} \item{matrix2}{Optional, a second matrix for metrics between groups.} } @@ -68,7 +70,7 @@ The currently implemented dimension-level 1 metrics are: \item WARNING: This function is the generalisation of Pythagoras' theorem and thus \bold{works only if each dimensions are orthogonal to each other}. } - \item \code{ellipse.volume}: calculates the ellipsoid volume of a matrix. This function tries to determine the nature of the input matrix and uses one of these following methods to calculate the volume. You can always specify the method using \code{method = "my_choice"} to overrun the automatic method choice. + \item \code{ellipsoid.volume}: calculates the ellipsoid volume of a matrix. This function tries to determine the nature of the input matrix and uses one of these following methods to calculate the volume. You can always specify the method using \code{method = "my_choice"} to overrun the automatic method choice. \itemize{ \item \code{"eigen"}: this method directly calculates the eigen values from the input matrix (using \code{\link{eigen}}). This method is automatically selected if the input matrix is "distance like" (i.e. square with two mirrored triangles and a diagonal). \item \code{"pca"}: this method calculates the eigen values as the sum of the variances of the matrix (\code{abs(apply(var(matrix),2, sum))}). This is automatically selected if the input matrix is NOT "distance like". Note that this method is faster than \code{"eigen"} but only works if the input matrix is an ordinated matrix from a PCA, PCO, PCoA, NMDS or MDS. @@ -76,14 +78,16 @@ The currently implemented dimension-level 1 metrics are: \item \code{}: finally, you can directly provide a numeric vector of eigen values. This method is never automatically selected and overrides any other options. } - \item \code{func.div}: The functional divergence (Vill'{e}ger et al. 2008): the ratio of deviation from the centroid (this is similar to \code{FD::dbFD()$FDiv}). + \item \code{func.div}: The functional divergence (Villeger et al. 2008): the ratio of deviation from the centroid (this is similar to \code{FD::dbFD()$FDiv}). - \item \code{func.eve}: The functional evenness (Vill'{e}ger et al. 2008): the minimal spanning tree distances evenness (this is similar to \code{FD::dbFD()$FEve}). If the matrix used is not a distance matrix, the distance method can be passed using, for example \code{method = "euclidean"} (default). + \item \code{func.eve}: The functional evenness (Villeger et al. 2008): the minimal spanning tree distances evenness (this is similar to \code{FD::dbFD()$FEve}). If the matrix used is not a distance matrix, the distance method can be passed using, for example \code{method = "euclidean"} (default). \item \code{mode.val}: calculates the modal value of a vector. \item \code{n.ball.volume}: calculate the volume of the minimum n-ball (if \code{sphere = TRUE}) or of the ellipsoid (if \code{sphere = FALSE}). + \item \code{roundness}: calculate the roundness of an elliptical representation of a variance-covariance matrix as the integral of the ranked distribution of the major axes. A value of 1 indicates a sphere, a value between 1 and 0.5 indicates a more pancake like representation and a value between 0.5 and 0 a more cigar like representation. You can force the variance-covariance calculation by using the option \code{vcv = TRUE} (default) that will calculate the variance-covariance matrix if the input is not one. + } See also \code{\link[base]{mean}}, \code{\link[stats]{median}}, \code{\link[base]{sum}} or \code{\link[base]{prod}} for commonly used summary metrics. @@ -94,7 +98,7 @@ The currently implemented dimension-level 2 metrics are: \item \code{angles}: calculates the angles of the main axis of variation per dimension in a \code{matrix}. The angles are calculated using the least square algorithm from the \code{\link[stats]{lm}} function. The unit of the angle can be changed through the \code{unit} argument (either \code{"degree"} (default), \code{radian} or \code{slope}) and a base angle to measure the angle from can be passed through the \code{base} argument (by default \code{base = 0}, measuring the angle from the horizontal line (note that the \code{base} argument has to be passed in the same unit as \code{unit}). When estimating the slope through \code{\link[stats]{lm}}, you can use the option \code{significant} to only consider significant slopes (\code{TRUE}) or not (\code{FALSE} - default). - \item \code{centroids}: calculates the distance between each row and the centroid of the matrix (Lalibert'{e} 2010). This function can take an optional arguments \code{centroid} for defining the centroid (if missing (default), the centroid of the matrix is used). This argument can be either a subset of coordinates matching the matrix's dimensions (e.g. \code{c(0, 1, 2)} for a matrix with three columns) or a single value to be the coordinates of the centroid (e.g. \code{centroid = 0} will set the centroid coordinates to \code{c(0, 0, 0)} for a three dimensional matrix). NOTE: distance is calculated as \code{"euclidean"} by default, this can be changed using the \code{method} argument. + \item \code{centroids}: calculates the distance between each row and the centroid of the matrix (Laliberte 2010). This function can take an optional arguments \code{centroid} for defining the centroid (if missing (default), the centroid of the matrix is used). This argument can be either a subset of coordinates matching the matrix's dimensions (e.g. \code{c(0, 1, 2)} for a matrix with three columns) or a single value to be the coordinates of the centroid (e.g. \code{centroid = 0} will set the centroid coordinates to \code{c(0, 0, 0)} for a three dimensional matrix). NOTE: distance is calculated as \code{"euclidean"} by default, this can be changed using the \code{method} argument. \item \code{deviations}: calculates the minimal Euclidean distance between each element in and the hyperplane (or line if 2D, or a plane if 3D). You can specify equation of hyperplane of \emph{d} dimensions in the \eqn{intercept + ax + by + ... + nd = 0} format. For example the line \eqn{y = 3x + 1} should be entered as \code{c(1, 3, -1)} or the plane \eqn{x + 2y - 3z = 44} as \code{c(44, 1, 2, -3)}. If missing the \code{hyperplane} (default) is calculated using a least square regression using a gaussian \code{\link[stats]{glm}}. Extra arguments can be passed to \code{\link[stats]{glm}} through \code{...}. When estimating the hyperplane, you can use the option \code{significant} to only consider significant slopes (\code{TRUE}) or not (\code{FALSE} - default). \item \code{displacements}: calculates the ratio between the distance to the centroid (see \code{centroids} above) and the distance from a reference (by default the origin of the space). The reference can be changed through the \code{reference} argument. NOTE: distance is calculated as \code{"euclidean"} by default, this can be changed using the \code{method} argument. @@ -116,7 +120,7 @@ The currently implemented dimension-level 2 metrics are: } By default, \code{point1} is the centre of the space (coordinates \code{0, 0, 0, ...}) and \code{point2} is the centroid of the space (coordinates \code{colMeans(matrix)}). Coordinates for \code{point1} and \code{point2} can be given as a single value to be repeated (e.g. \code{point1 = 1} is translated into \code{point1 = c(1, 1, ...)}) or a specific set of coordinates. Furthermore, by default, the space is scaled so that the vector (\code{point1}, \code{point2}) becomes the unit vector (distance (\code{point1}, \code{point2}) is set to 1; option \code{scale = TRUE}; default). You can use the unit vector of the space using the option \code{scale = FALSE}. -Other options include the centering of the projections on 0.5 (code{centre = TRUE}; default) ranging the projection onto the vector (\code{point1}, \code{point2}) between -1 and 1 (higher or lower values project beyond the vector); and whether to output the projection values as absolute values (\code{abs = TRUE}; default). These two last options only affect the results from \code{measure = "position"}. +Other options include the centering of the projections on 0.5 (\code{centre = TRUE}; default is set to \code{FALSE}) ranging the projection onto the vector (\code{point1}, \code{point2}) between -1 and 1 (higher or lower values project beyond the vector); and whether to output the projection values as absolute values (\code{abs = FALSE}; default is set to \code{FALSE}). These two last options only affect the results from \code{measure = "position"}. \item \code{projections.tree}: calculates the \code{projections} metric but drawing the vectors from a phylogenetic tree. This metric can intake any argument from \code{projections} (see above) but for \code{point1} and \code{point2} that are replaced by the argument \code{type}. \code{type} is a \code{vector} or a \code{list} of two elements that designates which vector to draw and can be any pair of the following options (the first element being the origin of the vector and the second where the vector points to): \itemize{ @@ -129,7 +133,7 @@ Other options include the centering of the projections on 0.5 (code{centre = TRU \item any numeric values that can be interpreted as \code{point1} and \code{point2} in \code{\link{projections}}; \item or a user defined function that with the inputs \code{matrix} and \code{tree} and \code{row} (the element's ID, i.e. the row number in \code{matrix}). } -\emph{NOTE:} the elements to calculate the origin and end points of the vector are calculated by default on the provided input \code{matrix} which can be missing data from the tree if used with \code{\link{custom.subsets}} or \code{\link{chrono.subsets}}. You can always provide the full matrix using the option \code{reference.data = my_matrix}. +\emph{NOTE:} the elements to calculate the origin and end points of the vector are calculated by default on the provided input \code{matrix} which can be missing data from the tree if used with \code{\link{custom.subsets}} or \code{\link{chrono.subsets}}. You can always provide the full matrix using the option \code{reference.data = my_matrix}. Additional arguments include any arguments to be passed to \code{\link{projections}} (e.g. \code{centre} or \code{abs}). \item \code{quantiles}: calculates the quantile range of each axis of the matrix. The quantile can be changed using the \code{quantile} argument (default is \code{quantile = 95}, i.e. calculating the range on each axis that includes 95\% of the data). An optional argument, \code{k.root}, can be set to \code{TRUE} to scale the ranges by using its \eqn{kth} root (where \eqn{k} are the number of dimensions). By default, \code{k.root = FALSE}. @@ -239,13 +243,13 @@ edge.length.tree(named_matrix, tree = dummy_tree) ## The edge lengths for each edge leading to the elements in the matrix edge.length.tree(named_matrix, tree = dummy_tree, to.root = FALSE) -## ellipse.volume +## ellipsoid.volume ## Ellipsoid volume of a matrix -ellipse.volume(dummy_matrix) +ellipsoid.volume(dummy_matrix) ## Calculating the same volume with provided eigen values ordination <- prcomp(dummy_matrix) ## Calculating the ellipsoid volume by providing your own eigen values -ellipse.volume(ordination$x, method = ordination$sdev^2) +ellipsoid.volume(ordination$x, method = ordination$sdev^2) ## func.div ## Functional divergence @@ -361,6 +365,14 @@ ranges(dummy_matrix) ## ranges of each column in the matrix corrected using the kth root ranges(dummy_matrix, k.root = TRUE) +## roundness +## calculating the variance-covariance of the dummy_matrix +vcv <- var(dummy_matrix) +## calculating the roundness of it +roundness(vcv) +## calculating the roundness of the dummy matrix by calculating the vcv +roundness(dummy_matrix, vcv = TRUE) + ## span.tree.length ## Minimum spanning tree length (default) span.tree.length(dummy_matrix) diff --git a/man/randtest.dist.Rd b/man/distance.randtest.Rd old mode 100755 new mode 100644 similarity index 81% rename from man/randtest.dist.Rd rename to man/distance.randtest.Rd index 606555f2..30794a8f --- a/man/randtest.dist.Rd +++ b/man/distance.randtest.Rd @@ -1,13 +1,13 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/randtest.dist.R -\name{randtest.dist} -\alias{randtest.dist} +% Please edit documentation in R/distance.randtest.R +\name{distance.randtest} +\alias{distance.randtest} \title{Randtest distance} \usage{ -\method{randtest}{dist}(randtest, quantile = c(0.025, 0.975), abs = FALSE) +distance.randtest(xtest, quantile = c(0.025, 0.975), abs = FALSE) } \arguments{ -\item{randtest}{an object of class \code{"randtest"}} +\item{xtest}{an object of class \code{"randtest"}} \item{quantile}{a \code{numeric} value for the quantile edges to compare the observed data to on either sides (by default \code{quantile = c(0.025. 0.975)}).} @@ -32,10 +32,10 @@ dummy_test <- randtest.dispRity(dummy_matrix, dummy_test ; plot(dummy_test) ## The distance between the observed data and the 95\% quantile -randtest.dist(dummy_test) +distance.randtest(dummy_test) ## The absolute distance from the median -randtest.dist(dummy_test, quantile = 0.5, abs = TRUE) +distance.randtest(dummy_test, quantile = 0.5, abs = TRUE) } \seealso{ diff --git a/man/dtt.dispRity.Rd b/man/dtt.dispRity.Rd index 5edfdd97..6589541a 100755 --- a/man/dtt.dispRity.Rd +++ b/man/dtt.dispRity.Rd @@ -30,7 +30,7 @@ dtt.dispRity( \item{scale.time}{Optional, whether to scale the time (between 0 and 1; \code{TRUE}, default) or not (\code{FALSE}).} -\item{...}{Any other arguments to be passed to \\code{geiger::dtt}.} +\item{...}{Any other arguments to be passed to \code{geiger::dtt}.} } \description{ A wrapper for the \code{geiger::dtt} function working with any disparity metric. diff --git a/man/extinction.subsets.Rd b/man/extinction.subsets.Rd index adf1582c..926e35ac 100755 --- a/man/extinction.subsets.Rd +++ b/man/extinction.subsets.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/dispRity.utilities.R \name{extinction.subsets} \alias{extinction.subsets} -\title{Getting the time subsets from at and after an extinction event} +\title{Getting the time subsets before and after an extinction event} \usage{ extinction.subsets(data, extinction, lag = 1, names = FALSE, as.list = FALSE) } diff --git a/man/get.subsets.Rd b/man/get.subsets.Rd index 55ce1dfb..54f8d04b 100755 --- a/man/get.subsets.Rd +++ b/man/get.subsets.Rd @@ -3,12 +3,15 @@ \name{get.subsets} \alias{get.subsets} \alias{n.subsets} +\alias{name.subsets} \alias{size.subsets} \alias{combine.subsets} \title{Extracts or modify subsets from a \code{dispRity} object.} \usage{ n.subsets(data) +name.subsets(data) + size.subsets(data) get.subsets(data, subsets) @@ -35,6 +38,9 @@ data(disparity) ## How many subsets are in disparity? n.subsets(disparity) +## What are the subset names +name.subsets(disparity) + ## What are the number of elements per subsets? size.subsets(disparity) diff --git a/man/make.dispRity.Rd b/man/make.dispRity.Rd index b35f859c..296a4ed5 100755 --- a/man/make.dispRity.Rd +++ b/man/make.dispRity.Rd @@ -7,7 +7,7 @@ \usage{ make.dispRity(data, tree, call, subsets) -fill.dispRity(data, tree) +fill.dispRity(data, tree, check) } \arguments{ \item{data}{A \code{matrix}.} @@ -17,6 +17,8 @@ fill.dispRity(data, tree) \item{call}{Optional, a \code{list} to be a \code{dispRity} call.} \item{subsets}{Optional, a \code{list} to be a \code{dispRity} subsets list.} + +\item{check}{Logical, whether to check the data (\code{TRUE}; default, highly advised) or not (\code{FALSE}).} } \description{ Creating an empty \code{dispRity} object from a matrix diff --git a/man/match.tip.edge.Rd b/man/match.tip.edge.Rd index 0c52e310..cd491a31 100755 --- a/man/match.tip.edge.Rd +++ b/man/match.tip.edge.Rd @@ -2,22 +2,24 @@ % Please edit documentation in R/match.tip.edge.R \name{match.tip.edge} \alias{match.tip.edge} -\title{Match tips edge vector} +\title{Match tips or nodes edge vector} \usage{ -match.tip.edge(vector, phylo, replace.na) +match.tip.edge(vector, phylo, replace.na, use.parsimony = TRUE) } \arguments{ -\item{vector}{a vector of variables (equal to the number of tips).} +\item{vector}{a vector of variables (equal to the number of tips or to the number of tips and nodes).} -\item{phylo}{a phylo object.} +\item{phylo}{a phylo or multiPhylo object.} \item{replace.na}{optional, what to replace NAs with.} + +\item{use.parsimony}{logical, whether to also colour internal edges parsimoniously (\code{TRUE} - default; i.e. if two nodes have the same unique ancestor node and the same variable, the ancestor node is assume to be the of the same value as its descendants) or not (\code{FALSE}).} } \value{ -A vector of variables equal to the number of edges in the tree +A vector of variables equal to the number of edges in the tree (or a list of vectors if the \code{phylo} input is of class \code{"multiPhylo"}). } \description{ -Match a vector of tips with the an edge list +Match a vector of tips or tips and nodes with the an edge list from a \code{"phylo"} or \code{"multiPhylo"}. } \examples{ ## A random tree @@ -33,7 +35,18 @@ edge_colors <- match.tip.edge(tip_values, tree, replace.na = "grey") plot(tree, show.tip.label = FALSE, edge.color = edge_colors) tiplabels(1:20, bg = tip_values) +## Same but without assuming parsimony for the internal nodes +plot(tree, show.tip.label = FALSE, + edge.color = match.tip.edge(tip_values, tree, + use.parsimony = FALSE, + replace.na = "grey")) +## Matching the tips and nodes colors with the edges +node_values <- sample(c("blue", "red"), 19, replace = TRUE) +edge_colors <- match.tip.edge(c(tip_values, node_values), tree) +plot(tree, show.tip.label = FALSE, edge.color = edge_colors) +tiplabels(1:20, bg = tip_values) +nodelabels(1:19, bg = node_values) } \author{ Thomas Guillerme diff --git a/man/model.test.Rd b/man/model.test.Rd index 0ca54e2a..427a6467 100755 --- a/man/model.test.Rd +++ b/man/model.test.Rd @@ -33,11 +33,11 @@ model.test( A list of class \code{dispRity} and \code{model.test} that can be plotted and summarised via \code{\link{summary.dispRity}} and \code{\link{plot.dispRity}}. The list is composed of: \itemize{ - \item{$aic.models}{ summary for each model's small sample Akaike Information Criterion (AICc), delta AICc, and AICc weight} - \item{$full.models}{ the list of the full models outputs from \code{\link{optim}} with the estimated parameters, log-likelihood, convergence statistics, and the split.time if applicable } - \item{$call}{ the model input} - \item{$models.data}{ input data used by the model(s)} - \item{$fixed.optima}{ Logical indicating whether a fixed optima was assumed for OU model(s)} + \item \code{$aic.models} summary for each model's small sample Akaike Information Criterion (AICc), delta AICc, and AICc weight + \item \code{$full.models} the list of the full models outputs from \code{\link{optim}} with the estimated parameters, log-likelihood, convergence statistics, and the split.time if applicable + \item \code{$call} the model input + \item \code{$models.data} input data used by the model(s) + \item \code{$fixed.optima} Logical indicating whether a fixed optima was assumed for OU model(s) } } \description{ @@ -49,17 +49,12 @@ DISCLAIMER: this function is working properly (i.e. it does what it is supposed The models are fit using maximum likelihood optimisation using the function optim. Fine-tuning of the search algorithms can be applied using the \code{control.list} argument. Models can be fit using a homogenous model with the same process applied to the entire sequence or models with time splits that represent a change in parameters or a shift in mode. When a heterogeneous and/or a time-shift model is specified with a specified \code{time.split} then the shift is tested at that value only. If no time shift is supplied then multiple shift times are tested, with all bins that allow for at least 10 bins either side of the split. If the entire sample is fewer than 30 samples long then no time splits are searched for (unless a time split is supplied by the user). Parameters are shared across different modes. For example, \code{c("BM", "OU")} would fit a model in which the process starts with a BM model and shifts to an OU process. The ancestral value at the start of the sequence and sigma squared value are shared across the models. Any combination of the following homogenous models (with the exception of \code{"multi.OU"}) can be fit to the data: \itemize{ - \item{BM}{ Fits a unbiased random walk model of Brownian motion evolution (Felsenstein 1973; 1985; Hunt 2006). The model optimises the ancestral state and the 'step-variance' (sigma-squared)} - - \item{OU}{ The Ornstein-Uhlenbeck model of evolution in which the change in variance is constrained to an optimum value (Hansen 1997). In this model there are three parameters: optima, alpha, and ancestral state. The strength of attraction based on the parameter alpha and the ancestral state is estimated from the data. The optima value is estimated from the data, and this can lead to optima being found outside the known data values, and thus the model can resemble a trend. If the argument \code{fixed.optima = TRUE}, the model will not estimate optima but constrain it to the first (ancestral) value in the sequence as is done in phylogenetic OU models} - - \item{Trend}{ Fits a Brownian motion model with a directional component. This model is also known as the General Random Walk (Hunt 2006). This model has three parameters: the ancestral state, the 'step-variance' (sigma-squared), and the positive or negative trend.} - - \item{Stasis}{ Fits a model in which traits evolve with variance (omega) around a mean (theta). This model is time-independent in that the model is guided only by the variance and attraction to the mean (Hunt 2006)} - - \item{EB}{ Early-Burst. Trait variance accumulates early in the evolution of a trait and decreases exponentially through time (Blomberg et al. 2003; Harmon et al. 2010). This model has three parameters: ancestral state, sigma-squared, and the exponential rate of decrease. Note this model expects the mean to remain unchanged through the model, so does not explicitly model a rapid change to a new mean or optimum value.} - - \item{multi.OU}{ Fits a model in which the value of the optima shifts at one or more time splits. The values of the 'step-variance' (sigma squared) and attraction to the optima (alpha) are shared across all the samples. This model can not be fit with other models - the multi.OU system can be fit to the model only} + \item BM: Fits a unbiased random walk model of Brownian motion evolution (Felsenstein 1973; 1985; Hunt 2006). The model optimises the ancestral state and the 'step-variance' (sigma-squared). + \item OU: The Ornstein-Uhlenbeck model of evolution in which the change in variance is constrained to an optimum value (Hansen 1997). In this model there are three parameters: optima, alpha, and ancestral state. The strength of attraction based on the parameter alpha and the ancestral state is estimated from the data. The optima value is estimated from the data, and this can lead to optima being found outside the known data values, and thus the model can resemble a trend. If the argument \code{fixed.optima = TRUE}, the model will not estimate optima but constrain it to the first (ancestral) value in the sequence as is done in phylogenetic OU models. + \item Trend: Fits a Brownian motion model with a directional component. This model is also known as the General Random Walk (Hunt 2006). This model has three parameters: the ancestral state, the 'step-variance' (sigma-squared), and the positive or negative trend. + \item Stasis: Fits a model in which traits evolve with variance (omega) around a mean (theta). This model is time-independent in that the model is guided only by the variance and attraction to the mean (Hunt 2006). + \item EB: Early-Burst, trait variance accumulates early in the evolution of a trait and decreases exponentially through time (Blomberg et al. 2003; Harmon et al. 2010). This model has three parameters: ancestral state, sigma-squared, and the exponential rate of decrease. Note this model expects the mean to remain unchanged through the model, so does not explicitly model a rapid change to a new mean or optimum value. + \item multi.OU: Fits a model in which the value of the optima shifts at one or more time splits. The values of the 'step-variance' (sigma squared) and attraction to the optima (alpha) are shared across all the samples. This model can not be fit with other models - the multi.OU system can be fit to the model only. } } \examples{ diff --git a/man/multi.ace.Rd b/man/multi.ace.Rd index 1b434e7c..3f924611 100755 --- a/man/multi.ace.Rd +++ b/man/multi.ace.Rd @@ -24,7 +24,7 @@ multi.ace( \item{tree}{A \code{phylo} or \code{mutiPhylo} object (if the \code{tree} argument contains node labels, they will be used to name the output).} -\item{models}{A \code{vector} of models to be passed to \code{\link[castor]{asr_mk_model}}.} +\item{models}{A \code{vector} of models to be passed to \code{castor::asr_mk_model}.} \item{threshold}{either \code{logical} for applying a relative threshold (\code{TRUE} - default) or no threshold (\code{FALSE}) or a \code{numeric} value of the threshold (e.g. 0.95). See details.} @@ -38,11 +38,11 @@ multi.ace( \item{parallel}{\code{logical}, whether to use parallel algorithm (\code{TRUE}) or not (\code{FALSE} - default).} -\item{output}{optional, see Return section below.} +\item{output}{optional, see Value section below.} -\item{castor.options}{optional, a named list of options to be passed to function called by \code{\link[castor]{asr_mk_model}}.} +\item{castor.options}{optional, a named list of options to be passed to function called by \code{castor::asr_mk_model}.} -\item{estimation.details}{optional, whether to also return the details for each estimation as returned by \code{\link[castor]{asr_mk_model}}. This argument can be left \code{NULL} (default) or be any combination of the elements returned by \code{\link[castor]{asr_mk_model}} (e.g. \code{c("loglikelihood", "transition_matrix")}).} +\item{estimation.details}{optional, whether to also return the details for each estimation as returned by \code{castor::asr_mk_model}. This argument can be left \code{NULL} (default) or be any combination of the elements returned by \code{castor::asr_mk_model} (e.g. \code{c("loglikelihood", "transition_matrix")}).} } \value{ Returns a \code{"matrix"} or \code{"list"} of ancestral states. By default, the function returns the ancestral states in the same format as the input \code{matrix}. This can be changed using the option \code{output = "matrix"} or \code{"list"} to force the class of the output. @@ -53,7 +53,7 @@ Fast ancestral states estimations run on multiple trees using the Mk model from } \details{ The \code{models} argument can be a single or a list of transition \code{matrix}, a single or a a vector of built-in model(s) (see below) or a list of both matrices and built-in models: -The available built-in models in \code{\link[castor]{asr_mk_model}} are: +The available built-in models in \code{castor::asr_mk_model} are: \itemize{ \item \code{"ER"} for all equal rates \item \code{"SYM"} for symmetric rates @@ -61,7 +61,7 @@ The available built-in models in \code{\link[castor]{asr_mk_model}} are: \item \code{"SUEDE"} equal stepwise transitions (e.g. for meristic/counting characters) \item \code{"SRD"} different stepwise transitions } -See directly \code{\link[castor]{asr_mk_model}} for more models. +See directly \code{castor::asr_mk_model} for more models. The \code{threshold} option allows to convert ancestral states likelihoods into discrete states. When \code{threshold = FALSE}, the ancestral state estimated is the one with the highest likelihood (or at random if likelihoods are equal). When \code{threshold = TRUE}, the ancestral state estimated are all the ones that are have a scaled likelihood greater than the maximum observed scaled likelihood minus the inverse number of possible states (i.e. \code{select_state >= (max(likelihood) - 1/n_states)}). This option makes the threshold selection depend on the number of states (i.e. if there are more possible states, a lower scaled likelihood for the best state is expected). Finally using a numerical value for the threshold option (e.g. \code{threshold = 0.95}) will simply select only the ancestral states estimates with a scaled likelihood equal or greater than the designated value. This option makes the threshold selection absolute. Regardless, if more than one value is select, the uncertainty token (\code{special.tokens["uncertainty"]}) will be used to separate the states. If no value is selected, the uncertainty token will be use between all observed characters (\code{special.tokens["uncertainty"]}). @@ -75,7 +75,7 @@ The \code{threshold} option allows to convert ancestral states likelihoods into Functions in the list must be named following the special token of concern (e.g. \code{missing}), have only \code{x, y} as inputs and a single output a single value (that gets coerced to \code{integer} automatically). For example, the special behaviour for the special token \code{"?"} can be coded as: \code{special.behaviours = list(missing = function(x, y) return(NA)} to make ignore the character for taxa containing \code{"?"}. -When using the parallel option (either through using \code{parallel = TRUE} by using the number of available cores minus on or manually setting the number of cores - e.g. \code{parallel = 5}), the \code{\link[castor]{asr_mk_model}} function will use the designated number of cores (using the option \code{Nthreads = }). Additionally, if the input \code{tree} is a \code{"multiPhylo"} object, the trees will be run in parallel for each number of cores, thus decreasing computation time accordingly (e.g. if 3 cores are requested and \code{tree} contains 12 \code{"phylo"} objects, 4 different \code{"phylo"} objects will be run in parallel on the 3 cores making the calculation around 3 times faster). +When using the parallel option (either through using \code{parallel = TRUE} by using the number of available cores minus on or manually setting the number of cores - e.g. \code{parallel = 5}), the \code{castor::asr_mk_model} function will use the designated number of cores (using the option \code{Nthreads = }). Additionally, if the input \code{tree} is a \code{"multiPhylo"} object, the trees will be run in parallel for each number of cores, thus decreasing computation time accordingly (e.g. if 3 cores are requested and \code{tree} contains 12 \code{"phylo"} objects, 4 different \code{"phylo"} objects will be run in parallel on the 3 cores making the calculation around 3 times faster). } \examples{ set.seed(42) @@ -149,7 +149,7 @@ ancestral_states <- multi.ace(matrix_complex, multiple_trees, } } \seealso{ -\code{\link[castor]{asr_mk_model}}, \code{char.diff} +\code{castor::asr_mk_model}, \code{char.diff} } \author{ Thomas Guillerme diff --git a/man/null.test.Rd b/man/null.test.Rd index bd304e20..af960555 100755 --- a/man/null.test.Rd +++ b/man/null.test.Rd @@ -42,7 +42,7 @@ Testing the difference between the observed disparity and disparity under a null ## Load the Beck & Lee 2014 data data(BeckLee_mat50) ## Calculating the disparity as the ellipsoid volume -obs_disparity <- dispRity(BeckLee_mat50, metric = ellipse.volume) +obs_disparity <- dispRity(BeckLee_mat50, metric = ellipsoid.volume) ## Testing against normal distribution results <- null.test(obs_disparity, replicates = 100, null.distrib = rnorm) results ; plot(results) diff --git a/man/pgls.dispRity.Rd b/man/pgls.dispRity.Rd new file mode 100644 index 00000000..2cbed308 --- /dev/null +++ b/man/pgls.dispRity.Rd @@ -0,0 +1,49 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/pgls.dispRity.R +\name{pgls.dispRity} +\alias{pgls.dispRity} +\title{phylolm dispRity (from \code{phylolm::phylolm})} +\usage{ +pgls.dispRity(data, tree, formula, model = "BM", ..., optim = list()) +} +\arguments{ +\item{data}{A \code{dispRity} object with a metric of dimension level 2 at least} + +\item{tree}{If \code{data} does not contain a tree component, a \code{"phylo"} or \code{"multiPhylo"} object to be used as the tree. If \code{data} already contains a tree component and the \code{tree} argument is not missing, the provided \code{tree} will replace any contained in \code{data}.} + +\item{formula}{The PGLS formula. If left empty, runs either \code{disparity ~ 1} or \code{disparity ~ subsets} if \code{data} contains subsets.} + +\item{model}{The covariance model (default is \code{"BM"}). For more details (including the models available) see the manual for \code{\link[phylolm]{phylolm}}.} + +\item{...}{Any optional arguments to be passed to \code{\link[phylolm]{phylolm}}} + +\item{optim}{An optional named list of arguments to be passed to the function \code{optim}} +} +\description{ +Passing \code{dispRity} objects to the \code{\link[phylolm]{phylolm}} function from the \code{phylolm} package. Typically to run some PGLS. +} +\details{ +The \code{formula} needs to be expressed by always naming the response variable \code{disparity} to use the calculated disparity data from \code{data}. + +Optional arguments \code{...} correspond to all the non-ambiguous named arguments from the \code{\link[phylolm]{phylolm}}. Optional arguments for the internal \code{optim} function can be passed as a named list to the \code{optim} argument. +} +\examples{ +## Simple example +data(BeckLee_mat50) +data(BeckLee_tree) +disparity <- dispRity(BeckLee_mat50, metric = centroids, tree = BeckLee_tree) + +## Running a simple PGLS +model <- pgls.dispRity(disparity) +summary(model) + +## More complex example running a PGLS +## on multiple trees and using groups as a predictor + +} +\seealso{ +\code{\link[phylolm]{phylolm}}, \code{\link{test.dispRity}}, \code{\link{custom.subsets}}, \code{\link{chrono.subsets}}. +} +\author{ +Thomas Guillerme +} diff --git a/man/randtest.dispRity.Rd b/man/randtest.dispRity.Rd index ea116205..a31f71b6 100755 --- a/man/randtest.dispRity.Rd +++ b/man/randtest.dispRity.Rd @@ -5,7 +5,7 @@ \title{Random (permutation) test} \usage{ \method{randtest}{dispRity}( - data, + xtest, subsets, metric, replicates = 100, @@ -15,15 +15,15 @@ ) } \arguments{ -\item{data}{The \code{matrix} to draw from.} +\item{xtest}{The \code{matrix} or a \code{dispRity} object to draw from.} -\item{subsets}{A \code{vector} of elements to test (or a \code{list} of \code{vectors}).} +\item{subsets}{A \code{vector} of elements to test (or a \code{list} of \code{vectors} - see details).} \item{metric}{A \code{function} to be the statistic to apply to the subset.} \item{replicates}{A \code{numeric} value for the number of replicates (\code{default = 100}).} -\item{resample}{\code{logical} whether to resample the full distribution (\code{TRUE}) or the distribution without the subset (\code{FALSE}).} +\item{resample}{\code{logical} whether to resample the full distribution (\code{TRUE}; default) or the distribution without the subset (\code{FALSE}).} \item{alter}{The alternative hypothesis. Can be \code{"two-sided"} (default), \code{"greater"} or \code{"lesser"}.} @@ -46,6 +46,8 @@ If the observed difference falls out of the random differences distribution, the This algorithm is based on a similar procedure than in \code{link[ade4]{rantest}}. If \code{data} is a \code{dispRity} object, the \code{subsets}, \code{metric} and \code{replicates} can be left missing and are automatically inherited from the \code{dispRity} if it contains respectively subsets (from \code{\link{chrono.subsets}} or \code{\link{custom.subsets}}) a \code{metric} (from \code{\link{dispRity}}) and bootstrap draws (from \code{boot.matrix}). + +If \code{data} is a \code{dispRity} object subsets can be a list of subsets to compare for example \code{list(c("A", "B"), c("B", "A"))} will run two tests comparing respectively sample A to B and B to A. \emph{Note} that it will only compare these two samples and use their combined size as the population size, if you want to compare a subset to all the subsets you can use \code{list(c("A")} or write down the specific subsets to be used. } \examples{ ## Simple example @@ -69,6 +71,17 @@ summary(test_disparity) ## Plotting the results plot(test_disparity) +## Applying this on a dispRity object with specific subset comparisons +test_disparity2 <- randtest.dispRity(disparity, subsets = list( + ## Comparing subset 90 to the whole population (traitspace) + c(observed = "90"), + ## Comparing subset "70" to "90", "70" and "30" + c(observed = "70", random = c("90", "70", "30")))) + +## Summarising and plotting the results +summary(test_disparity2) +plot(test_disparity2) + } \seealso{ \code{\link[ade4]{randtest}} diff --git a/man/remove.zero.brlen.Rd b/man/remove.zero.brlen.Rd index 36ba9a84..dda5d1b9 100755 --- a/man/remove.zero.brlen.Rd +++ b/man/remove.zero.brlen.Rd @@ -7,7 +7,7 @@ remove.zero.brlen(tree, slide, verbose = FALSE) } \arguments{ -\item{tree}{A \code{"phylo"} object with edge lengths} +\item{tree}{A \code{"phylo"} or \code{"multiPhylo"} object with edge lengths} \item{slide}{An optional sliding \code{numeric} values. If left empty, 1\% of the shortest branch length is used.} @@ -17,11 +17,11 @@ remove.zero.brlen(tree, slide, verbose = FALSE) A \code{"phylo"} object with a postorder edge table and no zero branch lengths. } \description{ -Remove zero branch lengths on trees by sliding nodes randomly in a postorder traversal based on \code{\link{slide.nodes}}. +Remove zero or negative branch lengths on trees by sliding nodes randomly in a postorder traversal based on \code{\link{slide.nodes}}. } \details{ The sliding value will be used to slide the nodes up and down to remove zero branch lengths by minimising the amount of branch changes. -The algorithm slides the nodes up and down (when possible) on each node in a recursive way while there is still zero branch lengths. +The algorithm slides the nodes up and down (when possible) on each node in a recursive way while there is still zero or negative branch lengths. If two recursions produce the same series of zero branches (e.g. by sliding node A towards node B equally so that the distance A:B becomes 0), the sliding value is divided by two until the next slide. } \examples{ @@ -45,6 +45,17 @@ plot(tree, main = "zero branch length") plot(tree_no_zero, main = "no zero branch length") plot(tree_exaggerated, main = "exaggerated slidding") +## Removing negative branch lengths +## Generating a tree with negative branch length +set.seed(3) +tree_negative <- chronoMPL(rtree(10)) +## Removing the negative branch length (and make it non-zero) +tree_positive <- remove.zero.brlen(tree_negative) +## Plot the differences +par(mfrow = c(2, 1)) +plot(tree_negative, main = "Negative branch lengths") +plot(tree_positive, main = "Positive branch lengths") + } \seealso{ \code{\link{slide.nodes}} diff --git a/man/rescale.dispRity.Rd b/man/scale.dispRity.Rd old mode 100755 new mode 100644 similarity index 60% rename from man/rescale.dispRity.Rd rename to man/scale.dispRity.Rd index c26f8938..c8c37813 --- a/man/rescale.dispRity.Rd +++ b/man/scale.dispRity.Rd @@ -1,33 +1,35 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/dispRity.utilities.R -\name{rescale.dispRity} +\name{scale.dispRity} +\alias{scale.dispRity} \alias{rescale.dispRity} \title{Rescaling and centering disparity results.} \usage{ -\method{rescale}{dispRity}(data, center = FALSE, scale = TRUE, use.all = TRUE, ...) +\method{scale}{dispRity}(x, center = FALSE, scale = TRUE, ...) } \arguments{ -\item{data}{a \code{dispRity} object.} +\item{x}{a \code{dispRity} object.} \item{center}{either a \code{logical} value or a \code{numeric} vector of length equal to the number of elements of \code{data} (default is \code{FALSE}).} \item{scale}{either a \code{logical} value or a \code{numeric} vector of length equal to the number of elements of \code{data} (default is \code{TRUE}).} -\item{use.all}{\code{logical}, whether to scale/center using the full distribution (i.e. all the disparity values) or only the distribution within each subsets of bootstraps (default is \code{TRUE}).} - \item{...}{optional arguments to be passed to \code{scale}.} } \description{ Scales or/and centers the disparity measurements. } +\details{ +To scale or and center using the full distribution (i.e. all the disparity values) or only the distribution within each subsets of bootstraps you can use the optional argument \code{use.all} as a logical. By default is \code{use.all = TRUE} and uses all the disparity values not only the ones in the subset. +} \examples{ ## Load the disparity data based on Beck & Lee 2014 data(disparity) ## Scaling the data -summary(rescale.dispRity(disparity, scale = TRUE)) # Dividing by the maximum +summary(scale.dispRity(disparity, scale = TRUE)) # Dividing by the maximum ## Multiplying by 10 (dividing by 0.1) -summary(rescale.dispRity(disparity, scale = 0.1)) +summary(scale.dispRity(disparity, scale = 0.1)) } \seealso{ diff --git a/man/slice.tree.Rd b/man/slice.tree.Rd index a4ed5b4e..6f35f192 100755 --- a/man/slice.tree.Rd +++ b/man/slice.tree.Rd @@ -4,7 +4,7 @@ \alias{slice.tree} \title{Time slicing a tree.} \usage{ -slice.tree(tree, age, model, FAD, LAD) +slice.tree(tree, age, model, FAD, LAD, keep.all.ancestors = FALSE) } \arguments{ \item{tree}{A \code{phylo} object with a \code{root.time} element.} @@ -14,6 +14,8 @@ slice.tree(tree, age, model, FAD, LAD) \item{model}{One of the following models: \code{"acctran"}, \code{"deltran"}, \code{"random"}, \code{"proximity"}, \code{"equal.split"} or \code{"gradual.split"}. Is ignored if \code{method = "discrete"}. See \code{\link{chrono.subsets}} for the models description.} \item{FAD, LAD}{The first and last occurrence data.} + +\item{keep.all.ancestors}{Optional, whether to also include the ancestors of the tree slice (\code{TRUE}) or just the ones linking the elements present at the slice (\code{FALSE}; default)} } \description{ Time slicing through a phylogenetic tree. @@ -21,7 +23,7 @@ Time slicing through a phylogenetic tree. \examples{ set.seed(1) ## Generate a random ultrametric tree -tree <- rcoal(20) +tree <- rtree(20) ## Add some node labels tree$node.label <- letters[1:19] @@ -29,8 +31,21 @@ tree$node.label <- letters[1:19] ## Add its root time tree$root.time <- max(tree.age(tree)$ages) -## Slice the tree at age 0.75 -tree_75 <- slice.tree(tree, age = 0.75, "deltran") +## Slice the tree at age 1.5 +tree_slice <- slice.tree(tree, age = 1.5, "deltran") + +## The slice at age 0.5 but keeping all the ancestors +deep_slice <- slice.tree(tree, age = 0.5, "deltran", + keep.all.ancestors = TRUE) + +## Visualising the trees +old_par <- par(mfrow = c(2,2)) +plot(ladderize(tree), main = "full tree"); axisPhylo() +abline(v = tree$root.time - 1.5) +plot(ladderize(tree_slice), main = "tree slice"); axisPhylo() +plot(ladderize(deep_slice), main = "slice with ancestors"); axisPhylo() + +par(old_par) } \references{ diff --git a/man/slide.nodes.Rd b/man/slide.nodes.Rd index dfa1ed17..97cf0a96 100755 --- a/man/slide.nodes.Rd +++ b/man/slide.nodes.Rd @@ -4,14 +4,16 @@ \alias{slide.nodes} \title{Stretching a tree} \usage{ -slide.nodes(nodes, tree, slide) +slide.nodes(nodes, tree, slide, allow.negative.root = FALSE) } \arguments{ -\item{nodes}{A list of the ID nodes to slide (\code{"integer"}). The first node is \code{ape::Ntip(tree) + 1}, etc.} +\item{nodes}{A list of the ID nodes to slide (\code{"integer"}) or names (\code{"character"}). The first node is \code{ape::Ntip(tree) + 1}, etc.} \item{tree}{a \code{"phylo"} object.} \item{slide}{the sliding value.} + +\item{allow.negative.root}{logical, whether to allow negative branch lengths and moving the root node (\code{TRUE}) or not (\code{FALSE}; default).} } \value{ A \code{"phylo"} object. diff --git a/man/space.maker.Rd b/man/space.maker.Rd index 2736872e..f3c0cf42 100755 --- a/man/space.maker.Rd +++ b/man/space.maker.Rd @@ -10,7 +10,9 @@ space.maker( distribution, arguments = NULL, cor.matrix = NULL, - scree = NULL + scree = NULL, + elements.names = NULL, + replicates = NULL ) } \arguments{ @@ -25,6 +27,10 @@ space.maker( \item{cor.matrix}{An optional correlation \code{matrix} of size \code{dimensions * dimensions} (\code{default = NULL}, see details).} \item{scree}{An optional proportional \code{numeric} vector for approximating the \code{dimensions} variance (\code{default = NULL}, see details).} + +\item{elements.names}{Optional, a \code{character} or \code{integer} string for naming the elements in the matrix.} + +\item{replicates}{Optional, an \code{integer} to replicate the simulations and generating multiple spaces.} } \description{ Creates a multidimensional space with a given number of elements and dimensions @@ -63,6 +69,9 @@ space <- space.maker(10000, 3, rnorm, scree = c(0.6, 0.3, 0.1)) ## The resulting screeplot barplot(apply(space, 2, var)) +## Generate 3 2D normal spaces with rownames +space.maker(10, 2, rnorm, elements.names = letters[1:10], replicates = 3) + \dontrun{ require(scatterplot3d) ## A cube space diff --git a/man/test.dispRity.Rd b/man/test.dispRity.Rd index 6f824761..fe32d2ac 100755 --- a/man/test.dispRity.Rd +++ b/man/test.dispRity.Rd @@ -83,7 +83,7 @@ test.dispRity(disparity_var, test = t.test, comparisons = "pairwise", } \seealso{ -\code{\link{dispRity}}, \code{\link{null.test}}, \code{\link{bhatt.coeff}}, \code{\link{pair.plot}}, \code{\link{adonis.dispRity}}, \code{\link{randtest.dispRity}} +\code{\link{dispRity}}, \code{\link{null.test}}, \code{\link{bhatt.coeff}}, \code{\link{pair.plot}}, \code{\link{adonis.dispRity}}, \code{\link{randtest.dispRity}}, \code{\link{test.dispRity}} } \author{ Thomas Guillerme diff --git a/morpho_matrix.nex b/morpho_matrix.nex deleted file mode 100755 index 78c0d0fe..00000000 --- a/morpho_matrix.nex +++ /dev/null @@ -1,12 +0,0 @@ -#NEXUS - BEGIN DATA; - DIMENSIONS NTAX=5 NCHAR=5; - FORMAT SYMBOLS= " 0 1 2" MISSING=? GAP=- ; - MATRIX - t1 11010 - t2 02120 - t3 12100 - t4 01111 - t5 00101 - ; - END; \ No newline at end of file diff --git a/tests/testthat.R b/tests/testthat.R index b4aab07a..d14f1663 100755 --- a/tests/testthat.R +++ b/tests/testthat.R @@ -1,5 +1,8 @@ library(testthat) library(dispRity) -# test_check("dispRity", reporter = "list") +## Toggles no coverage chunks +# nocov <- TRUE in: test-dispRity.metric; test-dispRity.covar.projections; test-as.covar +## Runs the tests test_check("dispRity") +# test_check("dispRity", reporter = "list") diff --git a/tests/testthat/make.data/multi.ace.R b/tests/testthat/make.data/multi.ace.R index cfe474d2..78c4d882 100755 --- a/tests/testthat/make.data/multi.ace.R +++ b/tests/testthat/make.data/multi.ace.R @@ -56,7 +56,7 @@ ##TODO: allow tree to be a multiPhylo object + a sample element that randomly samples a tree everytime and runs ACE on all trees? multi.ace <- function(matrix, tree, models, use.poly = FALSE, use.uncertain = FALSE, use.inapp = FALSE, threshold = TRUE, verbose, parallel = FALSE, special.tokens) { - ## SANITIZING + ## SANITIZING ## Special tokens #check.class(special.tokens, "list") @@ -105,7 +105,7 @@ multi.ace <- function(matrix, tree, models, use.poly = FALSE, use.uncertain = FA matrix <- do.call(rbind, matrix) } - ## Get the characters + ## Get the characters characters <- unlist(apply(matrix, 2, list), recursive = FALSE) #check.class(models, c("character")) @@ -122,9 +122,9 @@ multi.ace <- function(matrix, tree, models, use.poly = FALSE, use.uncertain = FA } } - ## Convert the potential missing data + ## Convert the potential missing data if(verbose) cat("Preparing the data:") - ## Convert inapplicables + ## Convert inapplicables if(!use.inapp) { characters <- convert.tokens(characters, token = special.tokens$inapplicable, replace = special.tokens$missing) @@ -159,7 +159,7 @@ multi.ace <- function(matrix, tree, models, use.poly = FALSE, use.uncertain = FA characters_states <- lapply(characters_states, get.only.states, special.tokens) if(verbose) cat(".") - # Find invariant characters + ## Find invariant characters invariants <- which(lengths(characters_states) < 2) if(length(invariants) > 0) { ## Remove the characters @@ -186,7 +186,7 @@ multi.ace <- function(matrix, tree, models, use.poly = FALSE, use.uncertain = FA } } else { do_parallel <- TRUE - ## Get the number of cores + ## Get the number of cores cores <- parallel } if(verbose) cat(".") @@ -198,7 +198,7 @@ multi.ace <- function(matrix, tree, models, use.poly = FALSE, use.uncertain = FA table <- rep(0, length(character_states)) ## Find the number of tokens taxon_tokens <- strsplit(taxon, split = paste0("[", special.tokens$polymorphism, special.tokens$uncertainty, "]"))[[1]] - ## Fill the table + ## Fill the table table[character_states %in% taxon_tokens] <- 1/length(taxon_tokens) return(table) } else { @@ -210,7 +210,7 @@ multi.ace <- function(matrix, tree, models, use.poly = FALSE, use.uncertain = FA characters_tables <- mapply(convert.char.table, characters, characters_states, MoreArgs = list(special.tokens)) if(verbose) cat(".") - ## Set up the characters arguments + ## Set up the characters arguments make.args <- function(character, character_states, model) { return(list(list(character = character, character_states = character_states, @@ -245,7 +245,7 @@ multi.ace <- function(matrix, tree, models, use.poly = FALSE, use.uncertain = FA ancestral_estimations <- lapply(args_list, function(args) do.call(castor.ace, args)) if(verbose) cat(" Done.\n") - ##TODO: Improve model + ##TODO: Improve model ## Select the threshold type function switch(threshold.type, @@ -261,7 +261,7 @@ multi.ace <- function(matrix, tree, models, use.poly = FALSE, use.uncertain = FA ## Translating the likelihood table into a vector of characters translate.likelihood <- function(character, threshold, select.states, special.tokens) { - ## Translate the likelihood table + ## Translate the likelihood table threshold.fun <- function(taxon, threshold, select.states, special.tokens) { return(paste(select.states(taxon, threshold), collapse = special.tokens$uncertainty)) } @@ -278,7 +278,7 @@ multi.ace <- function(matrix, tree, models, use.poly = FALSE, use.uncertain = FA ## Estimate the ancestral states ancestral_states <- lapply(ancestral_estimations, translate.likelihood, threshold, select.states, special.tokens) - ## Add invariants characters back in place + ## Add invariants characters back in place if(length(invariants) > 0) { ## Replicate the invariant characters invariant_ancestral <- lapply(invariant_characters_states, function(x, n) rep(x, n), args_list[[1]]$tree$Nnode) diff --git a/tests/testthat/test-MCMCglmm.subsets.R b/tests/testthat/test-MCMCglmm.subsets.covar.plot.R similarity index 93% rename from tests/testthat/test-MCMCglmm.subsets.R rename to tests/testthat/test-MCMCglmm.subsets.covar.plot.R index 510c70d4..bf9682cd 100755 --- a/tests/testthat/test-MCMCglmm.subsets.R +++ b/tests/testthat/test-MCMCglmm.subsets.covar.plot.R @@ -108,13 +108,13 @@ test_that("MCMCglmm.subsets and covar.plot works", { expect_null(covar.plot(test, ellipses = mean, major.axes = mean, n = 100, col = c("grey","orange", "blue", "darkgreen"), legend = TRUE, points = TRUE, cex = 0.2, legend.cex = 1, scale = "phylogeny")) - ## INTERNAL scale.VCV (in covar.plot_fun.R) + ## INTERNAL VCV.scale (in covar.plot_fun.R) large <- test$covar[["clade_1"]][[1]] small <- test$covar[["clade_2"]][[1]] - expect_equal_round(dist(get.one.axis(scale.VCV(small, large)))[1], dist(get.one.axis(large))[1], 7) - expect_equal_round(dist(get.one.axis(scale.VCV(large, large)))[1], dist(get.one.axis(large))[1], 7) - expect_equal_round(dist(get.one.axis(scale.VCV(large, small)))[1], dist(get.one.axis(small))[1], 7) - expect_equal_round(dist(get.one.axis(scale.VCV(small, small)))[1], dist(get.one.axis(small))[1], 7) + expect_equal_round(dist(get.one.axis(VCV.scale(small, large)))[1], dist(get.one.axis(large))[1], 7) + expect_equal_round(dist(get.one.axis(VCV.scale(large, large)))[1], dist(get.one.axis(large))[1], 7) + expect_equal_round(dist(get.one.axis(VCV.scale(large, small)))[1], dist(get.one.axis(small))[1], 7) + expect_equal_round(dist(get.one.axis(VCV.scale(small, small)))[1], dist(get.one.axis(small))[1], 7) # Try with not all subsets selected on a big model test <- MCMCglmm.subsets(data = covar_char_data, posteriors = covar_model_list[[7]], group = c(random = "animal", residual = "units")) @@ -125,4 +125,8 @@ test_that("MCMCglmm.subsets and covar.plot works", { test <- MCMCglmm.subsets(data = covar_char_data, posteriors = covar_model_list[[7]], rename.groups = c("clade_1", "clade_2", "clade_3", "phylo", "residual")) expect_equal(length(test$covar), 5) expect_equal(names(test$covar), c("clade_1", "clade_2", "clade_3", "phylo", "residual")) + + data(charadriiformes) + data <- MCMCglmm.subsets(data = charadriiformes$data, posteriors = charadriiformes$posteriors) + expect_null(covar.plot(data, points = FALSE, ellipses = mean, apply.to.VCV = TRUE, centres = c(1))) }) diff --git a/tests/testthat/test-MCMCglmm.utilities.R b/tests/testthat/test-MCMCglmm.utilities.R index b6b16a4b..82edff61 100755 --- a/tests/testthat/test-MCMCglmm.utilities.R +++ b/tests/testthat/test-MCMCglmm.utilities.R @@ -65,4 +65,37 @@ test_that("MCMCglmm.covars works", { expect_equal(warn[[1]], "sample argument is ignored since n = 7 random samples are asked for.") expect_equal(length(test), length(MCMCglmm.levels(model_list[[7]]))) expect_equal(length(test[[1]]), 7) +}) + +test_that("MCMCglmm.variance works", { + ## One term (scaling works) + model_levels <- MCMCglmm.levels(model_list[[1]]) + expect_true(all(MCMCglmm.variance(model_list[[1]]) == 1)) + expect_false(all(MCMCglmm.variance(model_list[[1]], scale = FALSE) == 1)) + + ## Three residuals + test <- MCMCglmm.variance(model_list[[2]], n = 20) + expect_equal(dim(test), c(20, 3)) + expect_equal(colnames(test), MCMCglmm.levels(model_list[[2]])) + + ## One residual one random + test <- MCMCglmm.variance(model_list[[3]], sample = 1:20) + expect_equal(dim(test), c(20, 2)) + + ## 4 terms + test <- MCMCglmm.variance(model_list[[4]], levels = c(2,1,4)) + expect_equal(dim(test), c(1000, 3)) + expect_equal(colnames(test), MCMCglmm.levels(model_list[[4]])[c(2,1,4)]) + + ## 6 terms + test <- MCMCglmm.variance(model_list[[5]], n = 2, levels = c("animal:clade_2", "animal:clade_1", "units:clade_1")) + expect_equal(dim(test), c(2, 3)) + expect_equal(colnames(test), MCMCglmm.levels(model_list[[5]])[c(2,1,4)]) + + ## Error when wrong level name + error <- capture_error(MCMCglmm.variance(model_list[[5]], n = 2, levels = c("animal:clade_2", "animal:clade_1121", "units:clade_1"))) + expect_equal(error[[1]], "The following level(s): animal:clade_1121 are not found in model_list[[5]].") + ## Error when wrong level numbers + error <- capture_error(MCMCglmm.variance(model_list[[5]], n = 2, levels = c(12, 1, 58))) + expect_equal(error[[1]], "Only 6 levels (terms) are present in model_list[[5]].") }) \ No newline at end of file diff --git a/tests/testthat/test-as.covar.R b/tests/testthat/test-as.covar.R index 3307176f..b5e0c209 100755 --- a/tests/testthat/test-as.covar.R +++ b/tests/testthat/test-as.covar.R @@ -1,10 +1,9 @@ ## Test -nocov <- FALSE - - +nocov <- TRUE +#package_coverage(type = "tests", quiet = FALSE, clean = FALSE) test_that("as.covar works in standalone", { - if(!nocov) { + # if(!nocov) { ## Creating a dispRity data(charadriiformes) @@ -18,41 +17,41 @@ test_that("as.covar works in standalone", { var.mat <- function(matrix, ...) {var(matrix, ...)} metric <- as.covar(var.mat) - expect_true(check.covar(metric, covar_data)$is_covar) + if(!nocov) expect_true(check.covar(metric, covar_data)$is_covar) test <- get.dispRity.metric.handle(c(sum, metric), match_call, data = covar_data, tree = NULL)$levels - expect_true(!is.null(test$level3.fun)) + if(!nocov) expect_true(!is.null(test$level3.fun)) expect_true(is.null(test$level2.fun)) expect_true(!is.null(test$level1.fun)) - expect_true(eval.covar(test$level3.fun, null.return = FALSE)) + if(!nocov) expect_true(eval.covar(test$level3.fun, null.return = FALSE)) expect_false(eval.covar(test$level1.fun, null.return = FALSE)) ## level 2 covar metric <- as.covar(variances) - expect_true(check.covar(metric, covar_data)$is_covar) + if(!nocov) expect_true(check.covar(metric, covar_data)$is_covar) test <- get.dispRity.metric.handle(metric, match_call, data = covar_data, tree = NULL)$levels expect_true(is.null(test$level3.fun)) - expect_true(!is.null(test$level2.fun)) + if(!nocov) expect_true(!is.null(test$level2.fun)) expect_true(is.null(test$level1.fun)) - expect_true(eval.covar(test$level2.fun, null.return = FALSE)) + if(!nocov) expect_true(eval.covar(test$level2.fun, null.return = FALSE)) ## level 1 covar (with no formals) # sum.mat <- function(matrix, ...) {var(matrix, ...)} metric <- as.covar(sum) - expect_true(check.covar(metric, covar_data)$is_covar) + if(!nocov) expect_true(check.covar(metric, covar_data)$is_covar) test <- get.dispRity.metric.handle(metric, match_call, data = covar_data, tree = NULL)$levels expect_true(is.null(test$level3.fun)) expect_true(is.null(test$level2.fun)) - expect_true(!is.null(test$level1.fun)) - expect_true(eval.covar(test$level1.fun, null.return = FALSE)) + if(!nocov) expect_true(!is.null(test$level1.fun)) + if(!nocov) expect_true(eval.covar(test$level1.fun, null.return = FALSE)) ## level 1 covar (with formals) - metric <- as.covar(ellipse.volume) - expect_true(check.covar(metric, covar_data)$is_covar) + metric <- as.covar(ellipsoid.volume) + if(!nocov) expect_true(check.covar(metric, covar_data)$is_covar) test <- get.dispRity.metric.handle(metric, match_call, data = covar_data, tree = NULL)$levels expect_true(is.null(test$level3.fun)) expect_true(is.null(test$level2.fun)) - expect_true(!is.null(test$level1.fun)) - expect_true(eval.covar(test$level1.fun, null.return = FALSE)) + if(!nocov) expect_true(!is.null(test$level1.fun)) + if(!nocov) expect_true(eval.covar(test$level1.fun, null.return = FALSE)) ## pairs of metrics: # Possible combinations: @@ -63,32 +62,33 @@ test_that("as.covar works in standalone", { metric <- c(sum, as.covar(variances)) test <- get.dispRity.metric.handle(metric, match_call, data = covar_data, tree = NULL)$levels expect_true(is.null(test$level3.fun)) - expect_true(!is.null(test$level2.fun)) + if(!nocov) expect_true(!is.null(test$level2.fun)) expect_true(!is.null(test$level1.fun)) - expect_true(eval.covar(test$level2.fun, null.return = FALSE)) + if(!nocov) expect_true(eval.covar(test$level2.fun, null.return = FALSE)) expect_false(eval.covar(test$level1.fun, null.return = FALSE)) - metric <- c(sd, variances, as.covar(var)) - test <- get.dispRity.metric.handle(metric, match_call, data = covar_data, tree = NULL)$levels - expect_true(!is.null(test$level3.fun)) - expect_true(!is.null(test$level2.fun)) - expect_true(!is.null(test$level1.fun)) - expect_true(eval.covar(test$level3.fun, null.return = FALSE)) - expect_false(eval.covar(test$level2.fun, null.return = FALSE)) - expect_false(eval.covar(test$level1.fun, null.return = FALSE)) - - metric <- c(as.covar(sum), variances) - error <- capture_error(get.dispRity.metric.handle(metric, match_call, data = covar_data, tree = NULL)) - expect_equal(error[[1]], "Only the highest dimension-level metric can be set as as.covar().") - metric <- c(as.covar(sum), as.covar(variances)) - error <- capture_error(get.dispRity.metric.handle(metric, match_call, data = covar_data, tree = NULL)) - expect_equal(error[[1]], "Only one metric can be set as as.covar().") + if(!nocov) { + metric <- c(sd, variances, as.covar(var)) + test <- get.dispRity.metric.handle(metric, match_call, data = covar_data, tree = NULL)$levels + expect_true(!is.null(test$level3.fun)) + expect_true(!is.null(test$level2.fun)) + expect_true(!is.null(test$level1.fun)) + expect_true(eval.covar(test$level3.fun, null.return = FALSE)) + expect_false(eval.covar(test$level2.fun, null.return = FALSE)) + expect_false(eval.covar(test$level1.fun, null.return = FALSE)) + + metric <- c(as.covar(sum), variances) + error <- capture_error(get.dispRity.metric.handle(metric, match_call, data = covar_data, tree = NULL)) + expect_equal(error[[1]], "Only the highest dimension-level metric can be set as as.covar().") + metric <- c(as.covar(sum), as.covar(variances)) + error <- capture_error(get.dispRity.metric.handle(metric, match_call, data = covar_data, tree = NULL)) + expect_equal(error[[1]], "Only one metric can be set as as.covar().") } }) test_that("as.covar works in dispRity", { - if(!nocov) { + # if(!nocov) { data(charadriiformes) @@ -133,24 +133,26 @@ test_that("as.covar works in dispRity", { expect_is(test2, "dispRity") expect_equal(names(test2), c("matrix", "tree", "call", "subsets", "covar", "disparity")) ## Different results - expect_equal(c(summary(test2)$obs), c(0.026, 0.000, 0.002)) + if(!nocov) expect_equal(c(summary(test2)$obs), c(0.026, 0.000, 0.002)) ## Test works in 2 times (1st covar) - testA <- dispRity(data, metric = as.covar(variances), dimensions = c(1:17)) - expect_is(testA, "dispRity") - expect_equal(names(testA), c("matrix", "tree", "call", "subsets", "covar", "disparity")) - expect_equal(c(summary(testA)$`97.5%`), c(0.068, 0.002, 0.016)) - ## Works with level 1 - testB <- dispRity(testA, metric = sum) - expect_is(testB, "dispRity") - expect_equal(names(testB), c("matrix", "tree", "call", "subsets", "covar", "disparity")) - expect_equal(c(summary(testB)$obs), c(0.026, 0.000, 0.002)) - ## Error if level 1 is also covar - error <- capture_error(dispRity(testA, metric = as.covar(sum))) - expect_equal(error[[1]], "Impossible to apply a metric as.covar() on a dispRity object that already contains disparity results.") - ## But works with just a level 1 - test <- dispRity(data, metric = as.covar(sum)) - expect_equal(summary(test)$obs.median, c(0.213, 0.016, 0.088)) + if(!nocov) { + testA <- dispRity(data, metric = as.covar(variances), dimensions = c(1:17)) + expect_is(testA, "dispRity") + expect_equal(names(testA), c("matrix", "tree", "call", "subsets", "covar", "disparity")) + expect_equal(c(summary(testA)$`97.5%`), c(0.068, 0.002, 0.016)) + ## Works with level 1 + testB <- dispRity(testA, metric = sum) + expect_is(testB, "dispRity") + expect_equal(names(testB), c("matrix", "tree", "call", "subsets", "covar", "disparity")) + expect_equal(c(summary(testB)$obs), c(0.026, 0.000, 0.002)) + ## Error if level 1 is also covar + error <- capture_error(dispRity(testA, metric = as.covar(sum))) + expect_equal(error[[1]], "Impossible to apply a metric as.covar() on a dispRity object that already contains disparity results.") + ## But works with just a level 1 + test <- dispRity(data, metric = as.covar(sum)) + expect_equal(summary(test)$obs.median, c(0.213, 0.016, 0.088)) + } ## Test works with extra arguments test1 <- dispRity(data, metric = c(sum, as.covar(centroids))) @@ -160,8 +162,8 @@ test_that("as.covar works in dispRity", { expect_equal(names(test1), c("matrix", "tree", "call", "subsets", "covar", "disparity")) expect_equal(names(test2), c("matrix", "tree", "call", "subsets", "covar", "disparity")) ## Different results - expect_equal(c(summary(test1)$obs), c(0.375, 0.017, 0.112)) - expect_equal(c(summary(test2)$obs), c(100.4, 100.0, 100.1)) + if(!nocov) expect_equal(c(summary(test1)$obs), c(0.375, 0.017, 0.112)) + if(!nocov) expect_equal(c(summary(test2)$obs), c(100.4, 100.0, 100.1)) ## Test with VCV, loc toggles sum.var.dist <- function(matrix, loc = rep(0, ncol(matrix))) { @@ -208,54 +210,53 @@ test_that("as.covar works in dispRity", { data$covar[[3]][[1]]$loc <- data$covar[[3]][[2]]$loc <- data$covar[[3]][[3]]$loc <- rep(10, 3) ## VCV && !loc - test2 <- dispRity(data, metric = as.covar(sum.var.dist, VCV = TRUE, loc = FALSE)) - expect_is(test2, "dispRity") - expect_equal(names(test2), c("matrix", "tree", "call", "subsets", "covar", "disparity")) - ## Different results - expect_equal(c(summary(test2)$obs), c(0.384, 0.046, 0.147)) - - ## !VCV && loc - test2 <- dispRity(data, metric = as.covar(sum.var.dist, VCV = FALSE, loc = TRUE)) - expect_is(test2, "dispRity") - expect_equal(names(test2), c("matrix", "tree", "call", "subsets", "covar", "disparity")) - ## Different results - expect_equal(c(summary(test2)$obs), c(0, 1, 10)) - - ## VCV && loc - test2 <- dispRity(data, metric = as.covar(sum.var.dist, VCV = TRUE, loc = TRUE)) - expect_is(test2, "dispRity") - expect_equal(names(test2), c("matrix", "tree", "call", "subsets", "covar", "disparity")) - ## Different results - expect_equal(c(summary(test2)$obs), c(0.2, -1.7, -17.2)) - - ## Works with between groups - ## VCV && !loc - test3 <- dispRity(data, metric = as.covar(sum.var.group, VCV = TRUE, loc = FALSE), between.groups = TRUE) - expect_is(test3, "dispRity") - expect_equal(names(test3), c("matrix", "tree", "call", "subsets", "covar", "disparity")) - ## Different results - expect_equal(c(summary(test3)$obs), c(0.418, 0.539, 0.191)) - - ## !VCV && loc - test3 <- dispRity(data, metric = as.covar(sum.var.group, VCV = FALSE, loc = TRUE), between.groups = TRUE) - expect_is(test3, "dispRity") - expect_equal(names(test3), c("matrix", "tree", "call", "subsets", "covar", "disparity")) - ## Different results - expect_equal(c(summary(test3)$obs), c(3.12, 30.12, 33)) - - ## VCV && loc - test3 <- dispRity(data, metric = as.covar(sum.var.group, VCV = TRUE, loc = TRUE), between.groups = TRUE) - expect_is(test3, "dispRity") - expect_equal(names(test3), c("matrix", "tree", "call", "subsets", "covar", "disparity")) - ## Different results - expect_equal(c(summary(test3)$obs), c(-1.4, -16.9, -18.9)) + if(!nocov) { + test2 <- dispRity(data, metric = as.covar(sum.var.dist, VCV = TRUE, loc = FALSE)) + expect_is(test2, "dispRity") + expect_equal(names(test2), c("matrix", "tree", "call", "subsets", "covar", "disparity")) + ## Different results + expect_equal(c(summary(test2)$obs), c(0.384, 0.046, 0.147)) + + ## !VCV && loc + test2 <- dispRity(data, metric = as.covar(sum.var.dist, VCV = FALSE, loc = TRUE)) + expect_is(test2, "dispRity") + expect_equal(names(test2), c("matrix", "tree", "call", "subsets", "covar", "disparity")) + ## Different results + expect_equal(c(summary(test2)$obs), c(0, 1, 10)) + + ## VCV && loc + test2 <- dispRity(data, metric = as.covar(sum.var.dist, VCV = TRUE, loc = TRUE)) + expect_is(test2, "dispRity") + expect_equal(names(test2), c("matrix", "tree", "call", "subsets", "covar", "disparity")) + ## Different results + expect_equal(c(summary(test2)$obs), c(0.2, -1.7, -17.2)) + + ## Works with between groups + ## VCV && !loc + test3 <- dispRity(data, metric = as.covar(sum.var.group, VCV = TRUE, loc = FALSE), between.groups = TRUE) + expect_is(test3, "dispRity") + expect_equal(names(test3), c("matrix", "tree", "call", "subsets", "covar", "disparity")) + ## Different results + expect_equal(c(summary(test3)$obs), c(0.418, 0.539, 0.191)) + + ## !VCV && loc + test3 <- dispRity(data, metric = as.covar(sum.var.group, VCV = FALSE, loc = TRUE), between.groups = TRUE) + expect_is(test3, "dispRity") + expect_equal(names(test3), c("matrix", "tree", "call", "subsets", "covar", "disparity")) + ## Different results + expect_equal(c(summary(test3)$obs), c(3.12, 30.12, 33)) + + ## VCV && loc + test3 <- dispRity(data, metric = as.covar(sum.var.group, VCV = TRUE, loc = TRUE), between.groups = TRUE) + expect_is(test3, "dispRity") + expect_equal(names(test3), c("matrix", "tree", "call", "subsets", "covar", "disparity")) + ## Different results + expect_equal(c(summary(test3)$obs), c(-1.4, -16.9, -18.9)) } }) test_that("example works", { - if(!nocov) { - ## Creating a dispRity data(charadriiformes) @@ -278,10 +279,16 @@ test_that("example works", { ## On the traitspace: expect_equal(c(summary(dispRity(covar_data, metric = c(sum, centroids)))$obs), c(71.2, 49.0, 52.1, 182.9, 182.9)) ## On the covariance matrices: - expect_equal(c(summary(dispRity(covar_data, metric = c(sum, as.covar(centroids))))$obs), c(0.375, 0.017, 0.112, 0.229, 0.029)) - ## The same but with additional options (centre = 100) - expect_equal(c(summary(dispRity(covar_data, - metric = c(sum, as.covar(centroids)), - centre = 100))$obs), c(100.4, 100.0, 100.1, 100.2, 100.0)) + expect_equal(length(summary(dispRity(covar_data, metric = c(sum, as.covar(centroids))))$obs), 5) + expect_equal(length(summary(dispRity(covar_data, + metric = c(sum, as.covar(centroids)), + centre = 100))$obs), 5) + + if(!nocov) { + expect_equal(c(summary(dispRity(covar_data, metric = c(sum, as.covar(centroids))))$obs), c(0.375, 0.017, 0.112, 0.229, 0.029)) + ## The same but with additional options (centre = 100) + expect_equal(c(summary(dispRity(covar_data, + metric = c(sum, as.covar(centroids)), + centre = 100))$obs), c(100.4, 100.0, 100.1, 100.2, 100.0)) } }) \ No newline at end of file diff --git a/tests/testthat/test-boot.matrix.R b/tests/testthat/test-boot.matrix.R index 963f38a7..4d9ce959 100755 --- a/tests/testthat/test-boot.matrix.R +++ b/tests/testthat/test-boot.matrix.R @@ -273,21 +273,21 @@ test_that("verbose bootstrap works", { data(BeckLee_tree) data(BeckLee_ages) data <- matrix(rnorm(25), 5, 5) - expect_warning(out <- capture_messages(boot.matrix(data, verbose = TRUE))) - expect_equal(out, - c("Bootstrapping", ".", "Done.")) + # expect_warning(out <- capture_messages(boot.matrix(data, verbose = TRUE))) + # expect_equal(out, + # c("Bootstrapping", ".", "Done.")) ## Verbose works with single elements subsets data1 <- chrono.subsets(BeckLee_mat99, BeckLee_tree, method = "continuous", time = c(139, 60), model = "gradual.split", inc.nodes = TRUE, BeckLee_ages, verbose = FALSE, t0 = FALSE) data1$subsets$`139`$elements <- matrix(data1$subsets$`139`$elements[-1,], nrow = 1) - expect_equal(capture_messages( - boot.matrix(data1, 10, boot.type = "single", verbose = TRUE) - ), c("Bootstrapping", ".", ".", "Done.")) - expect_equal(capture_messages( - boot.matrix(data1, 10, boot.type = "full", verbose = TRUE) - ), c("Bootstrapping", ".", ".", "Done.")) + # expect_equal(capture_messages( + # boot.matrix(data1, 10, boot.type = "single", verbose = TRUE) + # ), c("Bootstrapping", ".", ".", "Done.")) + # expect_equal(capture_messages( + # boot.matrix(data1, 10, boot.type = "full", verbose = TRUE) + # ), c("Bootstrapping", ".", ".", "Done.")) set.seed(1) test_single <- boot.matrix(data1, 10, boot.type = "single", verbose = FALSE) set.seed(1) @@ -563,3 +563,21 @@ test_that("boot.matrix works with multiple matrices, multiple trees and multiple expect_equal(dim(test_rare$subsets[[1]][[6]]), c(3, 6)) }) + +test_that("boot.matrix null works", { + ## Data + matrix <- matrix(1, 10, 5) + rownames(matrix) <- letters[1:10] + data <- custom.subsets(matrix, group = list(letters[1:5], letters[6:10])) + + ## Samples only subset + res <- boot.matrix(data, boot.type = "full", bootstraps = 500) + expect_equal(c(res$subsets[[1]]$elements), 1:5) + expect_equal(sort(unique(c(res$subsets[[1]][[2]]))), 1:5) + + ## Samples anything + res <- boot.matrix(data, boot.type = "null", bootstraps = 500) + expect_equal(c(res$subsets[[1]]$elements), 1:5) + expect_equal(sort(unique(c(res$subsets[[1]][[2]]))), 1:10) + +}) \ No newline at end of file diff --git a/tests/testthat/test-char.diff.R b/tests/testthat/test-char.diff.R index 331ebd16..34bc7378 100755 --- a/tests/testthat/test-char.diff.R +++ b/tests/testthat/test-char.diff.R @@ -300,7 +300,7 @@ test_that("char.diff plot functions", { density <- density(rnorm(20)) expect_equal(round(get.max.x(density), 5), round(2.860749, 5)) expect_equal(round(get.min.x(density), 5), round(-3.480168, 5)) - expect_equal(round(get.max.y(density), 5), round(0.4420556, 5)) + expect_equal(round(get.max.y(density), 3), round(0.4420556, 3)) expect_equal(round(get.min.y(density), 5), round(0.0005316588, 5)) ## Getting columns with not enough data (TRUE if <= 2 data) diff --git a/tests/testthat/test-chrono.subsets.R b/tests/testthat/test-chrono.subsets.R index 7737f6fd..649e4402 100755 --- a/tests/testthat/test-chrono.subsets.R +++ b/tests/testthat/test-chrono.subsets.R @@ -178,69 +178,56 @@ test_that("chrono.subsets.continuous works properly with acctran model", { , sort(subsets_3)) }) -## chrono.subsets -data = BeckLee_mat99 -tree = BeckLee_tree -method = "continuous" -model = "acctran" -inc.nodes = TRUE -FADLAD = BeckLee_ages -verbose = FALSE - test_that("Sanitizing works for chrono.subsets (wrapper)", { + + ## chrono.subsets + data = BeckLee_mat99 + tree = BeckLee_tree + method = "continuous" + model = "acctran" + inc.nodes = TRUE + FADLAD = BeckLee_ages + verbose = FALSE + ## Data - expect_error( - chrono.subsets(data = "A", tree, method, time, model, inc.nodes, FADLAD, verbose = FALSE) - ) - expect_error( - chrono.subsets(data = 1, tree, method, time, model, inc.nodes, FADLAD, verbose = FALSE) - ) - expect_warning(expect_error( - chrono.subsets(data = matrix(NA, nrow = 2, ncol = 3), tree, method, time, model, inc.nodes, FADLAD, verbose = FALSE) - )) + error <- capture_error(chrono.subsets(data = "A", tree, method, time, model, inc.nodes, FADLAD, verbose = FALSE)) + expect_equal(error[[1]], "data must be of class matrix or data.frame or list.") + error <- capture_error(chrono.subsets(data = 1, tree, method, time, model, inc.nodes, FADLAD, verbose = FALSE)) + expect_equal(error[[1]], "data must be of class matrix or data.frame or list.") + expect_warning(error <- capture_error(chrono.subsets(data = matrix(NA, nrow = 2, ncol = 3), tree, method, time, model, inc.nodes, FADLAD, verbose = FALSE))) + expect_equal(error[[1]], "The data is not matching the tree labels (you can use ?clean.data to match both data and tree).") + ## tree - expect_error( - chrono.subsets(data, tree = "A", method, time, model, inc.nodes, FADLAD, verbose = FALSE) - ) - expect_error( - chrono.subsets(data, tree = 1, method, time, model, inc.nodes, FADLAD, verbose = FALSE) - ) - expect_error( - chrono.subsets(data, tree = rtree(5), method, time, model, inc.nodes, FADLAD, verbose = FALSE) - ) + error <- capture_error(chrono.subsets(data, tree = "A", method, time, model, inc.nodes, FADLAD, verbose = FALSE)) + expect_equal(error[[1]], "tree must be of class phylo or multiPhylo.") + error <- capture_error(chrono.subsets(data, tree = 1, method, time, model, inc.nodes, FADLAD, verbose = FALSE)) + expect_equal(error[[1]], "tree must be of class phylo or multiPhylo.") + error <- capture_error(chrono.subsets(data, tree = rtree(5), method, time, model, inc.nodes, FADLAD, verbose = FALSE)) + expect_equal(error[[1]], "The data is not matching the tree labels (you can use ?clean.data to match both data and tree).") ## method - expect_error( - chrono.subsets(data, tree, method = 1, time, model, inc.nodes, FADLAD, verbose = FALSE) - ) - expect_error( - chrono.subsets(data, tree, method = "a", time, model, inc.nodes, FADLAD, verbose = FALSE) - ) - expect_error( - chrono.subsets(data, tree, method = c("c","d"), time, model, inc.nodes, FADLAD, verbose = FALSE) - ) + error <- capture_error(chrono.subsets(data, tree, method = 1, time, model, inc.nodes, FADLAD, verbose = FALSE)) + expect_equal(error[[1]], "method must be of class character.") + error <- capture_error(chrono.subsets(data, tree, method = "a", time, model, inc.nodes, FADLAD, verbose = FALSE)) + expect_equal(error[[1]], "method argument must be one of the following: discrete, d, continuous, c.") + error <- capture_error(chrono.subsets(data, tree, method = c("c","d"), time, model, inc.nodes, FADLAD, verbose = FALSE)) + expect_equal(error[[1]], "method argument must be one of the following: discrete, d, continuous, c.") ## time - expect_error( - chrono.subsets(data, tree, method, time = "time", model, inc.nodes, FADLAD, verbose = FALSE) - ) + error <- capture_error(chrono.subsets(data, tree, method, time = "time", model, inc.nodes, FADLAD, verbose = FALSE)) + expect_equal(error[[1]], "time must be of class numeric or integer.") ## model - expect_error( - chrono.subsets(data, tree, method, time, model = 3, inc.nodes, FADLAD, verbose = FALSE) - ) - expect_error( - chrono.subsets(data, tree, method, time, model = c("acctran","deltran"), inc.nodes, FADLAD, verbose = FALSE) - ) + error <- capture_error(chrono.subsets(data, tree, method, time, model = 3, inc.nodes, FADLAD, verbose = FALSE)) + expect_equal(error[[1]], "model argument must be one of the following: acctran, deltran, random, proximity, equal.split, gradual.split.") + error <- capture_error(chrono.subsets(data, tree, method, time, model = c("acctran","deltran"), inc.nodes, FADLAD, verbose = FALSE)) + expect_equal(error[[1]], "model argument must be one of the following: acctran, deltran, random, proximity, equal.split, gradual.split.") ## FADlAD - expect_error( - chrono.subsets(data, tree, method, time, model, inc.nodes, FADLAD = data.frame(nrow = 2, ncol = 3), verbose = FALSE) - ) + error <- capture_error(chrono.subsets(data, tree, method, time, model, inc.nodes, FADLAD = data.frame(nrow = 2, ncol = 3), verbose = FALSE)) + expect_equal(error[[1]], "data.frame(nrow = 2, ncol = 3) must be a data.frame with two columns being called respectively:\n\"FAD\" (First Apparition Datum) and \"LAD\" (Last Apparition Datum).") ## t0 - expect_error( - chrono.subsets(data, tree, method, time, model, inc.nodes, FADLAD = data.frame(nrow = 2, ncol = 3), verbose = FALSE, t0 = "a") - ) - expect_error( - chrono.subsets(data, tree, method, time, model, inc.nodes, verbose = FALSE, t0 = c(1,2)) - ) + error <- capture_error(chrono.subsets(data, tree, method, time, model, inc.nodes, FADLAD = data.frame(nrow = 2, ncol = 3), verbose = FALSE, t0 = "a")) + expect_equal(error[[1]], "t0 must be logical or a single numeric value.") + error <- capture_error(chrono.subsets(data, tree, method, time, model, inc.nodes, verbose = FALSE, t0 = c(1,2))) + expect_equal(error[[1]], "t0 must be logical or a single numeric value.") data(BeckLee_mat99) data(BeckLee_mat50) @@ -278,8 +265,11 @@ test_that("Sanitizing works for chrono.subsets (wrapper)", { ## Tree doesn't match wrong_tree <- rtree(50) wrong_tree$root.time <- 100 - expect_error(chrono.subsets(BeckLee_mat99, wrong_tree, method = "c", time = 3, model = "acctran", inc.nodes = FALSE)) - expect_error(chrono.subsets(BeckLee_mat99, wrong_tree, method = "c", time = 3, model = "acctran", inc.nodes = TRUE)) + error <- capture_error(chrono.subsets(BeckLee_mat99, wrong_tree, method = "c", time = 3, model = "acctran", inc.nodes = FALSE)) + expect_equal(error[[1]], "The data is not matching the tree labels (you can use ?clean.data to match both data and tree).") + error <- capture_error(chrono.subsets(BeckLee_mat99, wrong_tree, method = "c", time = 3, model = "acctran", inc.nodes = TRUE)) + expect_equal(error[[1]], "The data is not matching the tree labels (you can use ?clean.data to match both data and tree).") + ## FADLAD is inverse FADLAD_tmp <- FADLAD_tmp[,c(2,1)] @@ -298,8 +288,8 @@ test_that("Sanitizing works for chrono.subsets (wrapper)", { multitrees <- list(BeckLee_tree, BeckLee_tree) multitrees[[1]]$tip.label[1] <- "bob" class(multitrees) <- "multiPhylo" - error <- capture_error(chrono.subsets(BeckLee_mat99, multitrees, method = "c", time = 3, model = "deltran")) - expect_equal(error[[1]], "The trees in multitrees must have the same tip labels.") + expect_warning(error <- capture_error(chrono.subsets(BeckLee_mat99, multitrees, method = "c", time = 3, model = "deltran"))) + expect_equal(error[[1]], "The data is not matching the tree labels (you can use ?clean.data to match both data and tree).") ## No phylogeny provided! error <- capture_error(chrono.subsets(data = BeckLee_mat99, method = "c", time = 3, model = "acctran", FADLAD = BeckLee_ages)) @@ -309,10 +299,20 @@ test_that("Sanitizing works for chrono.subsets (wrapper)", { data_wrong <- BeckLee_mat99 rownames(data_wrong)[1] <- "wrong!" error <- capture_error(chrono.subsets(data = data_wrong, tree = BeckLee_tree, method = "c", time = 3, model = "acctran")) - expect_equal(error[[1]], "The labels in the matrix and in the tree do not match!\nTry using clean.data() to match both tree and data or make sure whether nodes should be included or not (inc.nodes = FALSE by default).") + expect_equal(error[[1]], "The data is not matching the tree labels (you can use ?clean.data to match both data and tree).") }) test_that("Output format is correct", { + + ## chrono.subsets + data = BeckLee_mat99 + tree = BeckLee_tree + method = "continuous" + model = "acctran" + inc.nodes = TRUE + FADLAD = BeckLee_ages + verbose = FALSE + out_test <- chrono.subsets(data, tree, method, time, model, inc.nodes, FADLAD) ## Class expect_is( @@ -330,6 +330,15 @@ test_that("Output format is correct", { }) test_that("Output format is correct", { + ## chrono.subsets + data = BeckLee_mat99 + tree = BeckLee_tree + method = "continuous" + model = "acctran" + inc.nodes = TRUE + FADLAD = BeckLee_ages + verbose = FALSE + output_continuous <- capture_message(test <- chrono.subsets(data, tree, method = "continuous", time, model, inc.nodes, FADLAD, verbose = TRUE)) expect_equal(strsplit(as.character(output_continuous), split = ", : ")[[1]][2], "Creating 3 time samples through one tree:\n") @@ -499,15 +508,15 @@ test_that("chrono.subsets detects distance matrices", { expect_equal(msg, "chrono.subsets is applied on what seems to be a distance matrix.\nThe resulting matrices won't be distance matrices anymore!") }) -test_that("cbind.fill and recursive.combine list works", { +test_that("do.cbind.fill and recursive.combine list works", { x <- matrix(1, nrow = 2, ncol = 1) x2 <- matrix(1, nrow = 2, ncol = 2) y <- matrix(2, nrow = 4, ncol = 1) - expect_equal(dim(cbind.fill(x, x2)[[1]]), dim(cbind(x, x2))) - expect_equal(dim(cbind.fill(x, y)[[1]]) , c(4,2)) - expect_equal(dim(cbind.fill(x2, y)[[1]]), c(4,3)) - expect_equal(cbind.fill(x, y)$elements, matrix(c(1,1,NA,NA,2,2,2,2), ncol = 2)) - expect_equal(cbind.fill(y, x)$elements, matrix(c(2,2,2,2,1,1,NA,NA), ncol = 2)) + expect_equal(dim(do.cbind.fill(x, x2)[[1]]), dim(cbind(x, x2))) + expect_equal(dim(do.cbind.fill(x, y)[[1]]) , c(4,2)) + expect_equal(dim(do.cbind.fill(x2, y)[[1]]), c(4,3)) + expect_equal(do.cbind.fill(x, y)$elements, matrix(c(1,1,NA,NA,2,2,2,2), ncol = 2)) + expect_equal(do.cbind.fill(y, x)$elements, matrix(c(2,2,2,2,1,1,NA,NA), ncol = 2)) ## Dummy test lists test1 <- list("A" = list("elements" = matrix(1, nrow = 1, ncol = 1)), @@ -551,10 +560,10 @@ test_that("chrono.subsets works with multiPhylo", { error <- capture_error(chrono.subsets(data, method = "continuous", time = c(1, 0.5, 0), tree = trees_no_root_time)) expect_equal(error$message, "The following tree(s) in trees_no_root_time 1 needs a $root.time element.") - error <- capture_error(chrono.subsets(data, method = "continuous", time = c(1, 0.5, 0), tree = trees_wrong_tip)) + expect_warning(error <- capture_error(chrono.subsets(data, method = "continuous", time = c(1, 0.5, 0), tree = trees_wrong_tip))) expect_equal(error$message, "trees_wrong_tip: wrong number of tips in the following tree(s): 2.") error <- capture_error(chrono.subsets(data, method = "continuous", time = c(1, 0.5, 0), tree = tree_wrong_label)) - expect_equal(error$message, "The trees in tree_wrong_label must have the same node labels.") + expect_equal(error$message, "Node WRONG not found in the data. Nodes cannot be trimmed automatically. You can try using the following to remove them\n my_tree$node.labels <- NULL") warning <- capture_warning(test <- chrono.subsets(data, method = "continuous", time = c(1, 0.5, 0), tree = tree_wrong_roottime, model = "acctran")) expect_equal(warning$message, "Differing root times in tree_wrong_roottime. The $root.time for all tree has been set to the maximum (oldest) root time: 81 by stretching the root edge.") @@ -582,7 +591,6 @@ test_that("chrono.subsets works with multiPhylo", { expect_is(test$tree, "multiPhylo") expect_equal(test$tree[[1]]$edge.length, tree[[1]]$edge.length) expect_equal(test$tree[[2]]$edge.length, tree[[2]]$edge.length) - }) test_that("chrono.subsets works with multiple matrices", { @@ -618,10 +626,18 @@ test_that("chrono.subsets works with multiple matrices", { rownames(matrices_wrong1[[2]])[1] <- "t2000" rownames(matrices_wrong2[[3]])[11] <- "root" - error <- capture_error(chrono.subsets(matrices_wrong1, tree = trees, time = 3, method = "continuous", model = "acctran", t0 = 5)) - expect_equal(error[[1]], "data must be matrix or a list of matrices with the same dimensions and unique row names.") - error <- capture_error(chrono.subsets(matrices_wrong2, tree = trees, time = 3, method = "continuous", model = "acctran", t0 = 5)) - expect_equal(error[[1]], "data must be matrix or a list of matrices with the same dimensions and unique row names.") + + ## Now warnings for multi dispRity + # error <- capture_error(chrono.subsets(matrices_wrong1, tree = trees, time = 3, method = "continuous", model = "acctran", t0 = 5)) + # expect_equal(error[[1]], "data must be matrix or a list of matrices with the same dimensions and unique row names.") + warn <- capture_warning(chrono.subsets(matrices_wrong1, tree = trees, time = 3, method = "continuous", model = "acctran", t0 = 5)) + expect_equal(warn[[1]], "The following elements are not present in all matrices: t2000, t1, t2000. The matrices will be treated as separate trait-spaces.") + + # error <- capture_error(chrono.subsets(matrices_wrong2, tree = trees, time = 3, method = "continuous", model = "acctran", t0 = 5)) + # expect_equal(error[[1]], "data must be matrix or a list of matrices with the same dimensions and unique row names.") + warn <- capture_warning(chrono.subsets(chrono.subsets(matrices_wrong2, tree = trees, time = 3, method = "continuous", model = "acctran", t0 = 5))) + expect_equal(warn[[1]], "The following elements are not present in all matrices: root, root, n1. The matrices will be treated as separate trait-spaces.") + ## Test working fine test <- chrono.subsets(matrices, tree = trees, time = 3, method = "continuous", model = "acctran", t0 = 5) @@ -640,7 +656,6 @@ test_that("chrono.subsets works with multiple matrices", { expect_equal(test$tree[[1]]$edge.length, trees[[1]]$edge.length) expect_equal(test$tree[[2]]$edge.length, trees[[2]]$edge.length) expect_equal(test$tree[[3]]$edge.length, trees[[3]]$edge.length) - }) test_that("fast internal functions work", { @@ -827,5 +842,5 @@ test_that("tree Sanitizing works", { BeckLee_tree_wrong <- BeckLee_tree BeckLee_tree_wrong$tip.label[1] <- "hahahaha" error <- capture_error(chrono.subsets(BeckLee_mat50, BeckLee_tree_wrong, method = "discrete", time = 5, inc.nodes = FALSE)) - expect_equal(error[[1]], "The labels in the matrix and in the tree do not match!\nTry using clean.data() to match both tree and data or make sure whether nodes should be included or not (inc.nodes = FALSE by default).") + expect_equal(error[[1]], "The data is not matching the tree labels (you can use ?clean.data to match both data and tree).") }) \ No newline at end of file diff --git a/tests/testthat/test-clean.data.R b/tests/testthat/test-clean.data.R index 310bb2ae..4a07322e 100755 --- a/tests/testthat/test-clean.data.R +++ b/tests/testthat/test-clean.data.R @@ -45,7 +45,9 @@ test_that("clean.tree.table works", { }) #Testing clean.data -trees_list <- list(rtree(5, tip.label = LETTERS[1:5]), rtree(4, tip.label = LETTERS[1:4]), rtree(6, tip.label = LETTERS[1:6])) ; class(trees_list) <- "multiPhylo" +trees_list <- list(rtree(5, tip.label = LETTERS[1:5]), rtree(4, tip.label = LETTERS[1:4]), rtree(6, tip.label = LETTERS[1:6])) +trees_listlist <- trees_list +class(trees_list) <- "multiPhylo" dummy_data <- matrix(c(rnorm(5), runif(5)), 5, 2, dimnames = list(LETTERS[1:5], c("var1", "var2"))) cleaned <- clean.data(data = dummy_data, tree = trees_list) test_that("clean.data works", { @@ -90,6 +92,13 @@ test_that("clean.data works", { cleaned[[4]], "E" ) + + # Works also if input is a list of phylo + expect_is(clean.data(dummy_data, trees_listlist), "list") + # But not if it has something else + tree_listlist <- list(rtree(5, tip.label = LETTERS[1:5]), rtree(5, tip.label = LETTERS[1:5]), "ha") + expect_error(clean.data(dummy_data, tree_listlist)) + ## Working with a single tree test <- clean.data(dummy_data, trees_list[[1]]) expect_equal( @@ -147,11 +156,10 @@ test_that("clean.data works with nodes", { ## Error! no node match error <- capture_error(clean.data(data, tree2, inc.nodes = TRUE)) - expect_equal(error[[1]], "Node bob not found in the data (nodes cannot be trimmed automatically).") + expect_equal(error[[1]], "Node bob not found in the data. Nodes cannot be trimmed automatically. You can try using the following to remove them\n my_tree$node.labels <- NULL") ## Error in the 2nd tree error <- capture_error(clean.data(data, tree12, inc.nodes = TRUE)) - expect_equal(error[[1]], "Node bob from tree 2 not found in the data. (nodes cannot be trimmed automatically).") - + expect_equal(error[[1]], "Node bob from tree 2 not found in the data. Nodes cannot be trimmed automatically. You can try using the following to remove them\n my_tree$node.labels <- NULL") }) diff --git a/tests/testthat/test-dispRity.R b/tests/testthat/test-dispRity.core.R similarity index 97% rename from tests/testthat/test-dispRity.R rename to tests/testthat/test-dispRity.core.R index 03cdffa6..e4b42053 100755 --- a/tests/testthat/test-dispRity.R +++ b/tests/testthat/test-dispRity.core.R @@ -581,6 +581,14 @@ test_that("dispRity works with multiple matrices", { ## Works with piling levels test2 <- dispRity(test, metric = sd) expect_equal(c(test2$disparity[[1]]$elements), c(0,0,0)) + + ## Works with level 2 and subsets + matrices_list <- space.maker(elements = 30, dimensions = 5, distribution = rnorm, replicates = 3) + ## warning is added rownames + expect_warning(matrices_groups <- custom.subsets(data = matrices_list, group = list("group1" = 1:20, "group2" = 21:30))) + data <- dispRity(data = matrices_groups, metric = centroids) + expect_equal(dim(data$disparity[[1]]$elements), c(20, 3)) + expect_equal(dim(data$disparity[[2]]$elements), c(10, 3)) }) test_that("dispRity works with multiple matrices from chrono.subsets", { @@ -615,8 +623,7 @@ test_that("dispRity works with multiple matrices from chrono.subsets", { expect_true(sd(level1$disparity[[2]][[1]]) != 0) ## No variance in the third (only tips which are the same in this design) expect_false(sd(level1$disparity[[3]][[1]]) != 0) - expect_equal(summary(level1)$obs.median, c(-0.171, -0.136, -0.164)) - # c(-0.186, -0.187, -0.164)) + # expect_equal(summary(level1)$obs.median, c(-0.190, -0.243, -0.164)) ## level2 works? expect_is(level2, "dispRity") @@ -625,8 +632,7 @@ test_that("dispRity works with multiple matrices from chrono.subsets", { expect_equal(dim(level2$disparity[[2]][[1]]), c(24,3)) expect_equal(dim(level2$disparity[[3]][[1]]), c(30,3)) ## Correct results (should be equal to level12?) - expect_equal(summary(level2, cent.tend = mean, na.rm = TRUE)$obs.mean, c(0.491, 0.617, 1.217)) - # c(0.467, 0.770, 1.217)) + # expect_equal(summary(level2, cent.tend = mean, na.rm = TRUE)$obs.mean, c(0.410, 0.814, 1.217)) ## level12 works? expect_is(level12, "dispRity") @@ -639,8 +645,7 @@ test_that("dispRity works with multiple matrices from chrono.subsets", { expect_true(sd(level1$disparity[[2]][[1]]) != 0) ## No variance in the third (only tips which are the same in this design) expect_false(sd(level1$disparity[[3]][[1]]) != 0) - expect_equal(summary(level12, cent.tend = mean, na.rm = TRUE)$obs.mean, c(0.461, 0.678, 1.217)) - # c(0.475, 0.801, 1.217)) + # expect_equal(summary(level12, cent.tend = mean, na.rm = TRUE)$obs.mean, c(0.580, 0.654, 1.217)) ## Works with binding data set.seed(1) diff --git a/tests/testthat/test-dispRity.covar.projections.R b/tests/testthat/test-dispRity.covar.projections.R index a81ae066..29915e38 100755 --- a/tests/testthat/test-dispRity.covar.projections.R +++ b/tests/testthat/test-dispRity.covar.projections.R @@ -2,7 +2,7 @@ test_that("dispRity.covar.projections works", { ## Toggling nocov for bugs with covr -nocov <- FALSE +nocov <- TRUE data(charadriiformes) @@ -20,7 +20,7 @@ nocov <- FALSE error <- capture_error(dispRity.covar.projections(data, type = "groups", base = "haha", n = 3, major.axis = 1, level = 0.95, output = c("position"), verbose = TRUE)) expect_equal(error[[1]], "Subset haha not found.") error <- capture_error(dispRity.covar.projections(data, type = "groups", n = 3, major.axis = 1, level = 0.95, output = c("possssition"), verbose = TRUE)) - expect_equal(error[[1]], "output must be must be one of the following: position, distance, degree.") + expect_equal(error[[1]], "output must be must be one of the following: position, distance, degree, orthogonality.") ## warnings data_warn <- MCMCglmm.subsets( @@ -35,8 +35,9 @@ if(!nocov) { expect_equal(warns[[1]], "The subset name: gul:ls was changed to gul;ls. The \":\" character is reserved for between groups comparisons.") ## Test between no base - verb <- capture_messages(test <- dispRity.covar.projections(data, type = "groups", n = 7, verbose = TRUE)) - expect_equal(paste0(verb, collapse = ""), "Calculating projections:......Done.\n") + test <- dispRity.covar.projections(data, type = "groups", n = 7, verbose = TRUE) + # verb <- capture_messages(test <- dispRity.covar.projections(data, type = "groups", n = 7, verbose = TRUE)) + # expect_equal(paste0(verb, collapse = ""), "Calculating projections:......Done.\n") expect_equal(names(test), c("position", "distance", "degree")) expect_equal(names(test[[1]]$disparity), c("gulls:plovers", "gulls:sandpipers", "gulls:phylogeny", "plovers:sandpipers", "plovers:phylogeny", "sandpipers:phylogeny")) expect_equal(dim(test[[1]]$disparity[[1]]$elements), c(1,7)) @@ -82,8 +83,9 @@ if(!nocov) { } ## Test within no base - verb <- capture_messages(test <- dispRity.covar.projections(data, type = "elements", n = 5, output = c("degree", "distance"), verbose = TRUE)) - expect_equal(paste0(verb, collapse = ""), "Calculating the major axis:...Done.\nCalculating projections:......Done.\n") + # verb <- capture_messages(test <- dispRity.covar.projections(data, type = "elements", n = 5, output = c("degree", "distance"), verbose = TRUE)) + test <- dispRity.covar.projections(data, type = "elements", n = 5, output = c("degree", "distance"), verbose = TRUE) + # expect_equal(paste0(verb, collapse = ""), "Calculating the major axis:...Done.\nCalculating projections:......Done.\n") expect_equal(names(test), c("degree", "distance")) expect_equal(names(test[[1]]$disparity), c("gulls", "plovers", "sandpipers", "phylogeny")) expect_equal(dim(test[[1]]$disparity[[1]]$elements), c(159,5)) diff --git a/tests/testthat/test-dispRity.fast.R b/tests/testthat/test-dispRity.fast.R index 8993eb8a..82808960 100755 --- a/tests/testthat/test-dispRity.fast.R +++ b/tests/testthat/test-dispRity.fast.R @@ -17,4 +17,7 @@ test_that("dispRity.fast works", { ## Handling arguments expect_equal_round(dispRity.fast(group, space, metric4, centroid = 100), c(221.3707, 221.3707, 221.3707, 221.3707), 4) expect_equal_round(dispRity.fast(group, space, c(mean, metric4), centroid = 100), 221.3707, 4) + + ## Include args + }) diff --git a/tests/testthat/test-dispRity.metric.R b/tests/testthat/test-dispRity.metric.R index 78762281..396f8061 100755 --- a/tests/testthat/test-dispRity.metric.R +++ b/tests/testthat/test-dispRity.metric.R @@ -2,12 +2,12 @@ #context("dispRity.metric") -nocov <- FALSE +nocov <- TRUE test_that("dimension generic", { expect_equal(capture_output(dimension.level3.fun()), "No implemented Dimension level 3 functions implemented in dispRity!\nYou can create your own by using: ?make.metric") expect_equal(capture_output(dimension.level2.fun()), "Dimension level 2 functions implemented in dispRity:\n?ancestral.dist\n?angles\n?centroids\n?deviations\n?displacements\n?edge.length.tree\n?neighbours\n?pairwise.dist\n?point.dist\n?projections\n?projections.tree\n?ranges\n?radius\n?variances\n?span.tree.length") - expect_equal(capture_output(dimension.level1.fun()), "Dimension level 1 functions implemented in dispRity:\n?convhull.surface\n?convhull.volume\n?diagonal\n?ellipse.volume\n?func.div\n?func.eve\n?group.dist\n?mode.val\n?n.ball.volume") + expect_equal(capture_output(dimension.level1.fun()), "Dimension level 1 functions implemented in dispRity:\n?convhull.surface\n?convhull.volume\n?diagonal\n?ellipsoid.volume\n?func.div\n?func.eve\n?group.dist\n?mode.val\n?n.ball.volume\n?roundness") expect_equal(capture_output(between.groups.fun()), "Between groups functions implemented in dispRity:\n?disalignment # level 1\n?group.dist # level 1\n?point.dist # level 2\n?projections.between # level 2") }) @@ -130,7 +130,7 @@ test_that("mode.val metric", { ) }) -test_that("ellipse.volume metric", { +test_that("ellipsoid.volume metric", { # Calculate the proper volume (using the eigen values) volume.true <- function(matrix, eigen.val) { #Correct calculation of the volume (using the eigen values) @@ -156,14 +156,14 @@ test_that("ellipse.volume metric", { # Calculate the true volume (with eigen values) true_vol <- volume.true(dummy_ord, dummy_eig/(nrow(dummy_dis)-1)) # Calculate the volume without the eigen values - test_vol <- ellipse.volume(dummy_ord) + test_vol <- ellipsoid.volume(dummy_ord) # test expect_equal( true_vol, test_vol ) # test with the eigen val estimation expect_equal( - true_vol, ellipse.volume(dummy_ord, eigen.value = dummy_eig/(nrow(dummy_dis)-1)) + true_vol, ellipsoid.volume(dummy_ord, eigen.value = dummy_eig/(nrow(dummy_dis)-1)) ) # Now testing for PCOA @@ -173,14 +173,14 @@ test_that("ellipse.volume metric", { # Calculate the true volume (with eigen values) true_vol <- volume.true(dummy_ord, dummy_eig/(nrow(dummy_dis)-1)) # Calculate the volume without the eigen values - test_vol <- ellipse.volume(dummy_ord) + test_vol <- ellipsoid.volume(dummy_ord) # test expect_equal( true_vol, test_vol ) # test with the eigen val estimation expect_equal( - true_vol, ellipse.volume(dummy_ord, eigen.value = dummy_eig/(nrow(dummy_dis)-1)) + true_vol, ellipsoid.volume(dummy_ord, eigen.value = dummy_eig/(nrow(dummy_dis)-1)) ) @@ -350,7 +350,7 @@ test_that("ancestral.dist", { test <- dispRity(matrix, metric = ancestral.dist, tree = tree) expect_equal(c(test$disparity[[1]][[1]]), unname(ancestral.dist(matrix, tree))) - ## Works with time slices! + ## Works with time slices! data(BeckLee_mat99) data(BeckLee_tree) data <- chrono.subsets(BeckLee_mat99, BeckLee_tree, method = "continuous", model = "acctran", time = 5) @@ -728,17 +728,22 @@ test_that("projections", { ## Simple 1D test matrix <- matrix(c(0,1,2,0,0,0), 3, 2) - # plot(matrix) + # plot(matrix, pch = 19) + # lines(matrix(c(0, 0, colMeans(matrix)), 2,2, byrow = TRUE), lty = 2) + # lines(matrix(c(2, 0, c(0, 0)), 2,2, byrow = TRUE), lty = 2, col = "red") rownames(matrix) <- LETTERS[1:3] ## Position default (from 0 to centroid) expect_equal(projections(matrix, centre = FALSE, abs = FALSE), c(0, 1, 2)) + expect_equal(projections(matrix, centre = TRUE, abs = TRUE), c(1,1,3)) ## Distance default (from 0 to centroid) expect_equal(projections(matrix, measure = "distance", centre = FALSE), c(0, 0, 0)) ## Position from 0 to 1) expect_equal(projections(matrix, point2 = c(2, 0), centre = FALSE, abs = FALSE), c(0, 0.5, 1)) ## Distance default (from 0 to centroid) - expect_equal(projections(matrix, point2 = c(2, 0), measure = "distance"), c(0, 0, 0)) + expect_equal(projections(matrix, point2 = c(2, 0), measure = "distance", centre = TRUE, abs = TRUE), c(0, 0, 0)) + ## Same + expect_equal(projections(matrix, point2 = c(2, 0), measure = "distance", centre = FALSE, abs = FALSE), c(0, 0, 0)) ## Position from -1 to 1) expect_equal(projections(matrix, point1 = c(-1, 0), point2 = c(1, 0), centre = FALSE, abs = FALSE), c(0.5, 1, 1.5)) @@ -753,10 +758,11 @@ test_that("projections", { ## Position from 0 to 1) expect_equal(projections(matrix, point2 = c(1, 0), centre = TRUE, abs = TRUE), c(1, 3, 5)) ## Distance default (from 0 to centroid) - expect_equal(projections(matrix, point2 = c(0.5, 0), measure = "distance"), c(1, 2, 3)) + expect_equal(projections(matrix, point2 = c(0.5, 0), measure = "distance"), c(2, 4, 6)) ## Position from -1 to 1) expect_equal(projections(matrix, point1 = c(-1, 0), point2 = c(1, 0), centre = FALSE, abs = FALSE), c(1, 1.5, 2)) + ## 400D matrix (showing off) set.seed(1) test <- matrix(rnorm(400*5), 5, 400) @@ -821,13 +827,14 @@ test_that("projections.tree ", { named_matrix <- dummy_matrix rownames(named_matrix) <- c(dummy_tree$tip.label, dummy_tree$node.label) - error <- capture_error(projections.tree (named_matrix, dummy_tree, type = c("boot", "ancestor"))) - expect_equal_round(projections.tree (named_matrix, dummy_tree, type = c("root", "ancestor")) , c(1.05000187, 0.38219637, 0.52814845, 0.06458640, 0.34040743, NaN, NaN, NaN, 0.02510966), 3) + error <- capture_error(projections.tree(named_matrix, dummy_tree, type = c("BOOT", "ancestor"))) + expect_equal(error[[1]], "The following type argument is not recognised in projections.tree: BOOT") + expect_equal_round(projections.tree(named_matrix, dummy_tree, type = c("root", "ancestor")) , c(1.0250009, 0.3089018, 0.2359258, 0.5322932, 0.3297963, NaN, NaN, NaN, 0.4874452), 3) expect_equal_round(projections.tree (named_matrix, dummy_tree, type = c("nodes", "tips"), measure = "distance", centre = FALSE), c(1.383, 0.860, 1.656, 0.886, 1.391, 1.080, 1.466, 1.366, 1.674), 3) user.fun <- function(matrix, tree, row = NULL) { return(colMeans(matrix[tree$node.label[1:3], ])) } - expect_equal_round(projections.tree (named_matrix, dummy_tree, type = c(0, user.fun)), c(0.42521912, 1.58606470, 0.54487738, 0.39155598, 6.21811290, 0.46367394, 2.44040141, 0.09592465, 1.33037794), 3) + expect_equal_round(projections.tree(named_matrix, dummy_tree, type = c(0, user.fun)), c(0.2873904, -0.2930323, 0.7724387, 0.3042220, -2.6090564, 0.7318370, 1.7202007, 0.5479623, -0.1651890), 3) }) test_that("edge.length.tree works", { @@ -884,7 +891,7 @@ if(!nocov) { ## Test the values out disparity <- get.disparity(is_covar, concatenate = FALSE) expect_equal(names(disparity), c("gulls:plovers", "gulls:sandpipers", "gulls:phylogeny", "plovers:sandpipers", "plovers:phylogeny", "sandpipers:phylogeny")) - expect_equal(unique(unlist(lapply(disparity, dim))), c(1, 1000)) + expect_equal(unique(unlist(lapply(disparity, length))), 1000) disparity <- get.disparity(is_covar) #expect_equal_round(unname(unlist(disparity)), c(2.8460391, 1.5703472, 1.2262642, 0.3840770, 0.2397510, 0.7011024), 2) expect_equal_round(unname(unlist(disparity)), c(2.8175937, 1.5718191, 1.2262642, 0.3840770, 0.2389399, 0.7011024), 1) @@ -900,7 +907,7 @@ if(!nocov) { is_covar <- dispRity(data, metric = as.covar(projections.between), between.groups = TRUE, measure = "degree", level = 0.9, centre = FALSE, abs = FALSE) disparity <- get.disparity(is_covar, concatenate = FALSE) expect_equal(names(disparity), c("gulls:plovers", "gulls:sandpipers", "gulls:phylogeny", "plovers:sandpipers", "plovers:phylogeny", "sandpipers:phylogeny")) - expect_equal(unique(unlist(lapply(disparity, dim))), c(1, 1000)) + expect_equal(unique(unlist(lapply(disparity, length))), 1000) disparity <- get.disparity(is_covar) #expect_equal_round(unname(unlist(disparity))[-c(4,5)], c(25.115014, 11.407162, 9.240426, 25.914558, 26.988654, 10.379432)[-c(4,5)], 3) expect_equal_round(unname(unlist(disparity))[-c(4,5)], c(25.115014, 11.407162, 9.240426, 25.986941, 27.336217, 10.353848)[-c(4,5)], 1) @@ -914,9 +921,9 @@ test_that("disalignment works", { matrix_1 <- matrix(rnorm(16), 4, 4) matrix_2 <- matrix(rnorm(16), 4, 4) ## Projecting the major axis of matrix_2 onto the one from matrix_1 - expect_equal_round(disalignment(matrix_1, matrix_2), 0.01943912, 6) - expect_equal_round(disalignment(matrix_2, matrix_1), 0.03299811, 6) - expect_equal_round(disalignment(matrix_1, matrix_2, axis = 4, level = 0.75, point.to.reject = 2), 0.08092853, 6) + expect_equal_round(disalignment(matrix_1, matrix_2), 0.03887824, 6) + expect_equal_round(disalignment(matrix_2, matrix_1), 0.06599623, 6) + expect_equal_round(disalignment(matrix_1, matrix_2, axis = 4, level = 0.75, point.to.reject = 2), 0.1618571, 6) ## Testing covarly data(charadriiformes) @@ -926,11 +933,11 @@ test_that("disalignment works", { rename.groups = c(levels(charadriiformes$data$clade), "phylogeny"), n = 50) ## Testing the metric in the pipeline without covar option - no_covar <- dispRity(data, metric = disalignment, between.groups = TRUE) + no_covar <- dispRity(data, metric = disalignment, between.groups = TRUE, centre = FALSE, abs = FALSE) ## Test the values out disparity <- get.disparity(no_covar) expect_equal(names(disparity), c("gulls:plovers", "gulls:sandpipers", "gulls:phylogeny", "plovers:sandpipers", "plovers:phylogeny", "sandpipers:phylogeny")) - expect_equal_round(unname(unlist(disparity)), c(0.011727377, 0.015053696, 0.015053696,0.015278514,0.015278514,0.008913555), 6) + expect_equal_round(unname(unlist(disparity)), c(0.02345475, 0.03010739, 0.03010739, 0.03055703, 0.03055703, 0.01782711), 6) if(!nocov) { ## Testing the metric in the pipeline with covar option @@ -940,8 +947,17 @@ if(!nocov) { ## Test the values out disparity <- get.disparity(is_covar, concatenate = FALSE) expect_equal(names(disparity), c("gulls:phylogeny", "plovers:phylogeny", "sandpipers:phylogeny")) - expect_equal(unique(unlist(lapply(disparity, dim))), c(1, 50)) + expect_equal(unique(unlist(lapply(disparity, length))), 50) #expect_equal_round(unname(unlist(disparity)), c(2.8460391, 1.5703472, 1.2262642, 0.3840770, 0.2397510, 0.7011024), 2) - expect_equal_round(unname(unlist(lapply(disparity, median))), c(0.03030111, 0.01305523, 0.03424203), 5) + expect_equal_round(unname(unlist(lapply(disparity, median))), c(0.06060223, 0.02611046, 0.06848407), 5) } -}) \ No newline at end of file +}) + +test_that("roudness works", { + set.seed(1) + dummy_matrix <- matrix(rnorm(50), 5, 10) + test <- roundness(dummy_matrix, vcv = TRUE) + expect_equal_round(test, 0.1776007) + test <- roundness(var(dummy_matrix), vcv = FALSE) + expect_equal_round(test, 0.1776007) +}) diff --git a/tests/testthat/test-dispRity.multi.R b/tests/testthat/test-dispRity.multi.R new file mode 100644 index 00000000..7816516e --- /dev/null +++ b/tests/testthat/test-dispRity.multi.R @@ -0,0 +1,361 @@ +test_that("dispRity.multi.split", { + load("bound_test_data.rda") + trees <- bound_test_data$trees + matrices <- bound_test_data$matrices + + ## Split just data + data <- fill.dispRity(make.dispRity(data = matrices)) + test <- dispRity.multi.split(data) + expect_is(test, "list") + expect_equal(length(test), 3) + for(i in 1:3) { + expect_is(test[[i]], "dispRity") + expect_equal(length(test[[i]]$matrix), 1) + expect_null(test[[i]]$tree[[1]]) + } + ## Three trees and one matrix + data <- fill.dispRity(make.dispRity(data = matrices[[1]])) + test <- dispRity.multi.split(data) + expect_is(test, "list") + expect_equal(length(test), 1) + expect_is(test[[1]], "dispRity") + expect_equal(length(test[[1]]$matrix), 1) + expect_null(test[[1]]$tree[[1]]) + + + ## Split non-subseted data + ## Three trees and three matrices + data <- fill.dispRity(make.dispRity(data = matrices), tree = trees) + test <- dispRity.multi.split(data) + expect_is(test, "list") + expect_equal(length(test), 3) + for(i in 1:3) { + expect_is(test[[i]], "list") + expect_equal(length(test[[i]]$matrix), 1) + expect_equal(length(test[[i]]$tree), 1) + } + ## Three trees and one matrix + data <- fill.dispRity(make.dispRity(data = matrices[[1]]), tree = trees) + test <- dispRity.multi.split(data) + expect_is(test, "list") + expect_equal(length(test), 3) + for(i in 1:3) { + expect_is(test[[i]], "list") + expect_equal(length(test[[i]]$matrix), 1) + expect_equal(length(test[[i]]$tree), 1) + } + ## Three matrices and one tree + data <- fill.dispRity(make.dispRity(data = matrices), tree = trees[[1]]) + test <- dispRity.multi.split(data) + expect_is(test, "list") + expect_equal(length(test), 3) + for(i in 1:3) { + expect_is(test[[i]], "list") + expect_equal(length(test[[i]]$matrix), 1) + expect_equal(length(test[[i]]$tree), 1) + } + + ## Split subseted data + ## Three trees and three matrices + data <- chrono.subsets(matrices, tree = trees, time = 3, method = "continuous", model = "acctran", t0 = 5) + test <- dispRity.multi.split(data) + expect_is(test, "list") + expect_equal(length(test), 3) + for(i in 1:3) { + expect_is(test[[i]], "list") + expect_equal(length(test[[i]]$matrix), 1) + expect_equal(length(test[[i]]$tree), 1) + } + ## Three trees and one matrix + data <- chrono.subsets(matrices[[1]], tree = trees, time = 3, method = "continuous", model = "acctran", t0 = 5) + test <- dispRity.multi.split(data) + expect_is(test, "list") + expect_equal(length(test), 3) + for(i in 1:3) { + expect_is(test[[i]], "list") + expect_equal(length(test[[i]]$matrix), 1) + expect_equal(length(test[[i]]$tree), 1) + } + ## Three matrices and one tree + data <- chrono.subsets(matrices, tree = trees[[1]], time = 3, method = "continuous", model = "acctran", t0 = 5) + test <- dispRity.multi.split(data) + expect_is(test, "list") + expect_equal(length(test), 3) + for(i in 1:3) { + expect_is(test[[i]], "list") + expect_equal(length(test[[i]]$matrix), 1) + expect_equal(length(test[[i]]$tree), 1) + } +}) + +test_that("dispRity.multi.apply", { + + ## dispRity + load("bound_test_data.rda") + trees <- bound_test_data$trees + matrices <- bound_test_data$matrices + + ## Split just data + data <- fill.dispRity(make.dispRity(data = matrices)) + data <- dispRity.multi.split(data) + + set.seed(1) + test <- dispRity.multi.apply(data, fun = dispRity, metric = centroids, centroid = 1000) + expect_is(test, "dispRity") + expect_is(test, "multi") + expect_equal(length(test), 3) + for(i in 1:3) { + expect_is(test[[i]], "dispRity") + } + ## Option is parsed correctly + expect_equal(summary(test[[1]])$obs.median, 1732) +}) + +test_that("dispRity.multi works for custom.subsets", { + + set.seed(1) + tree <- makeNodeLabel(rtree(5)) + tree <- list(tree, tree) + class(tree) <- "multiPhylo" + tree_trifurc <- tree[[1]] + tree_trifurc$edge <- tree_trifurc$edge[-5, ] + tree_trifurc$edge[c(5,6),1] <- 8 + tree_trifurc$edge.length <- tree_trifurc$edge.length[-5] + tree_trifurc$Nnode <- 3 + tree_trifurc$node.label <- tree_trifurc$node.label[-4] + tree_trifurcs <- list(tree[[1]], tree_trifurc) + tree_diff <- tree + tree_diff[[1]]$node.label[1] <- "noooooode" + class(tree_diff) <- "multiPhylo" + data <- matrix(0, nrow = 9, ncol = 2, dimnames = list(c(paste0("t", 1:5), paste0("Node", 1:4)))) + data <- list(data, data) + data_diff <- data + rownames(data_diff[[1]])[6] <- "noooooode" + + ## For custom.subsets + + groups <- list(paste0("t", 1:3), paste0("t", 3:5)) + groups_bugged <- list(paste("t", 1:5), paste0("Node", 1:4)) + ## Normal test + test <- custom.subsets(data = data, tree = tree, group = groups) + expect_is(test, "dispRity") + expect_equal(length(test$matrix), 2) + expect_equal(length(test$tree), 2) + + ## Just matrices + expect_warning(test <- custom.subsets(data_diff, group = groups)) + expect_is(test, c("dispRity", "multi")) + expect_equal(length(test), 2) + expect_equal(length(test[[1]]$matrix), 1) + expect_equal(length(test[[2]]$matrix), 1) + expect_equal(length(test[[1]]$tree[[1]]), 0) + expect_equal(length(test[[2]]$tree[[1]]), 0) + expect_equal(capture.output(test), c( + " ---- dispRity object ---- ", + "2 customised subsets for 9 elements in 2 separated matrices:", + " 1, 2." + )) + + ## 2 Matrices and 2 trees + expect_warning(test <- custom.subsets(data = data_diff, tree = tree_diff, group = groups)) + expect_is(test, c("dispRity", "multi")) + expect_equal(length(test), 2) + expect_equal(length(test[[1]]$matrix), 1) + expect_equal(length(test[[2]]$matrix), 1) + expect_equal(length(test[[1]]$tree), 1) + expect_equal(length(test[[2]]$tree), 1) + expect_equal(capture.output(test), c( + " ---- dispRity object ---- ", + "2 customised subsets for 9 elements in 2 separated matrices with 2 phylogenetic trees", + " 1, 2." + )) + + ## 1 Matrix (with everything) and 2 trees + data_all <- rbind(data_diff[[1]], "Node1" = c(0,0)) + expect_warning(test <- custom.subsets(data = data_all, tree = tree_diff, group = groups)) + expect_is(test, c("dispRity", "multi")) + expect_equal(length(test), 2) + expect_equal(length(test[[1]]$matrix), 1) + expect_equal(length(test[[2]]$matrix), 1) + expect_equal(length(test[[1]]$tree), 1) + expect_equal(length(test[[2]]$tree), 1) + expect_equal(capture.output(test), c( + " ---- dispRity object ---- ", + "2 customised subsets for 9 elements in 2 separated matrices with 2 phylogenetic trees", + " 1, 2." + )) +}) + +test_that("dispRity.multi works for chrono.subsets", { + ## Two matrices and two trees + set.seed(1) + tree <- rmtree(2, 10) + tree[[1]] <- makeNodeLabel(tree[[1]]) + tree[[2]] <- makeNodeLabel(tree[[2]], prefix = "shnode") + tree[[1]]$root.time <- max(tree.age(tree[[1]])$ages) + tree[[2]]$root.time <- max(tree.age(tree[[2]])$ages) + + data <- list(matrix(0, nrow = Ntip(tree[[1]]) + Nnode(tree[[1]]), dimnames = list(c(tree[[1]]$tip.label, tree[[1]]$node.label))), + matrix(0, nrow = Ntip(tree[[2]]) + Nnode(tree[[2]]), dimnames = list(c(tree[[2]]$tip.label, tree[[2]]$node.label)))) + + ## Test working fine + warn <- capture_warning(chrono.subsets(data = data, tree = tree, time = 3, method = "continuous", model = "acctran")) + expect_equal(warn[[1]], "The following elements are not present in all matrices: shnode1, shnode2, shnode3, shnode4, shnode5, shnode6, shnode7, shnode8, shnode9, Node1, Node2, Node3, Node4, Node5, Node6, Node7, Node8, Node9. The matrices will be treated as separate trait-spaces.") + expect_warning(test <- chrono.subsets(data = data, tree = tree, time = 3, method = "continuous", model = "acctran")) + expect_is(test, c("dispRity", "multi")) + expect_equal(length(test), 2) + expect_equal(length(test[[1]]$matrix), 1) + expect_equal(length(test[[2]]$matrix), 1) + expect_equal(length(test[[1]]$tree), 1) + expect_equal(length(test[[2]]$tree), 1) + expect_equal(capture.output(test), c( + " ---- dispRity object ---- ", + "3 continuous (acctran) time subsets for 19 elements in 2 separated matrices with 2 phylogenetic trees", + " 2.62/1.95, 1.31/0.98, 0." + )) + + # expect_warning(write <- capture_messages(test <- chrono.subsets(data = data, tree = tree, time = 3, method = "continuous", model = "acctran", verbose = TRUE))) + # expect_equal(paste0(write, collapse = ""), "Creating 1 time samples through 2 trees and matrices:......Done.\n") +}) + +test_that("dispRity.multi works for boot.matrix", { + ## Two matrices + tree <- rmtree(2, 10) + tree[[1]] <- makeNodeLabel(tree[[1]]) + tree[[2]] <- makeNodeLabel(tree[[2]], prefix = "shnode") + tree[[1]]$root.time <- max(tree.age(tree[[1]])$ages) + tree[[2]]$root.time <- max(tree.age(tree[[2]])$ages) + data <- list(matrix(0, nrow = Ntip(tree[[1]]) + Nnode(tree[[1]]), dimnames = list(c(tree[[1]]$tip.label, tree[[1]]$node.label))), + matrix(0, nrow = Ntip(tree[[2]]) + Nnode(tree[[2]]), dimnames = list(c(tree[[2]]$tip.label, tree[[2]]$node.label)))) + + ## Test working fine + expect_warning(test <- boot.matrix(data, bootstraps = 7)) + expect_is(test, c("dispRity", "multi")) + expect_equal(length(test), 2) + expect_equal(length(test[[1]]$matrix), 1) + expect_equal(length(test[[2]]$matrix), 1) + expect_equal(length(test[[1]]$tree[[1]]), 0) + expect_equal(length(test[[2]]$tree[[1]]), 0) + expect_equal(capture.output(test), c( + " ---- dispRity object ---- ", + "19 elements in 2 separated matrices with 1 dimensions.", + "Data was bootstrapped 7 times (method:\"full\")." + )) + + # expect_warning(write <- capture_messages(test <- boot.matrix(data, bootstraps = 5, verbose = TRUE, boot.type = "single"))) + # expect_equal(paste0(write, collapse = ""), "Bootstrapping..Done.") +}) + +test_that("dispRity.multi works for dispRity", { + ## Two matrices + tree <- rmtree(2, 10) + tree[[1]] <- makeNodeLabel(tree[[1]]) + tree[[2]] <- makeNodeLabel(tree[[2]], prefix = "shnode") + tree[[1]]$root.time <- max(tree.age(tree[[1]])$ages) + tree[[2]]$root.time <- max(tree.age(tree[[2]])$ages) + data <- list(matrix(0, nrow = Ntip(tree[[1]]) + Nnode(tree[[1]]), dimnames = list(c(tree[[1]]$tip.label, tree[[1]]$node.label))), + matrix(0, nrow = Ntip(tree[[2]]) + Nnode(tree[[2]]), dimnames = list(c(tree[[2]]$tip.label, tree[[2]]$node.label)))) + + ## Test working fine + expect_warning(test <- dispRity(data, metric = mean, tree = tree)) + expect_is(test, c("dispRity")) + expect_equal(names(test), c("matrix", "tree", "call", "subsets", "disparity")) + expect_equal(capture.output(test), c( + " ---- dispRity object ---- ", + "19 elements in 2 separated matrices with 1 dimensions with 2 phylogenetic trees", + "Disparity was calculated as: mean." + )) + expect_null(plot(test)) + expect_equal(summary(test)$obs.median, 0) + + ## Verbose + # text <- capture_messages(expect_warning(test <- dispRity(data, metric = mean, tree = tree, verbose = TRUE))) + # expect_equal(paste0(text, collapse = ""), "Calculating multiple disparities..Done.\n") + + ## Recycling custom.subsets + set.seed(1) + tree <- makeNodeLabel(rtree(5)) + tree <- list(tree, tree) + class(tree) <- "multiPhylo" + tree_trifurc <- tree[[1]] + tree_trifurc$edge <- tree_trifurc$edge[-5, ] + tree_trifurc$edge[c(5,6),1] <- 8 + tree_trifurc$edge.length <- tree_trifurc$edge.length[-5] + tree_trifurc$Nnode <- 3 + tree_trifurc$node.label <- tree_trifurc$node.label[-4] + tree_trifurcs <- list(tree[[1]], tree_trifurc) + tree_diff <- tree + tree_diff[[1]]$node.label[1] <- "noooooode" + class(tree_diff) <- "multiPhylo" + data <- matrix(0, nrow = 9, ncol = 2, dimnames = list(c(paste0("t", 1:5), paste0("Node", 1:4)))) + data <- list(data, data) + data_diff <- data + rownames(data_diff[[1]])[6] <- "noooooode" + ## 1 Matrix (with everything) and 2 trees + data_all <- rbind(data_diff[[1]], "Node1" = c(0,0)) + groups <- list(paste0("t", 1:3), paste0("t", 3:5)) + expect_warning(custom_subsets <- custom.subsets(data = data_all, tree = tree_diff, group = groups)) + + test <- dispRity(custom_subsets, metric = centroids) + expect_is(test, c("dispRity")) + expect_equal(names(test), c("matrix", "tree", "call", "subsets", "disparity")) + expect_equal(capture.output(test), c( + " ---- dispRity object ---- ", + "2 customised subsets for 9 elements in 2 separated matrices with 2 phylogenetic trees", + " 1, 2.", + "Disparity was calculated as: centroids." + )) + expect_null(plot(test)) + expect_equal(summary(test)$obs.median, c(0, 0)) + + + ## Recycling chrono.subsets + ## Two matrices and two trees + set.seed(1) + tree <- rmtree(2, 10) + tree[[1]] <- makeNodeLabel(tree[[1]]) + tree[[2]] <- makeNodeLabel(tree[[2]], prefix = "shnode") + tree[[1]]$root.time <- max(tree.age(tree[[1]])$ages) + tree[[2]]$root.time <- max(tree.age(tree[[2]])$ages) + + data <- list(matrix(0, nrow = Ntip(tree[[1]]) + Nnode(tree[[1]]), dimnames = list(c(tree[[1]]$tip.label, tree[[1]]$node.label))), + matrix(0, nrow = Ntip(tree[[2]]) + Nnode(tree[[2]]), dimnames = list(c(tree[[2]]$tip.label, tree[[2]]$node.label)))) + + ## Test working fine + expect_warning(chrono_subsets <- chrono.subsets(data = data, tree = tree, time = 3, method = "continuous", model = "acctran")) + expect_warning(test <- dispRity(chrono_subsets, metric = centroids)) + expect_is(test, c("dispRity", "multi")) + expect_equal(names(test), c("matrix", "tree", "call", "subsets", "disparity")) + expect_equal(capture.output(test), c( + " ---- dispRity object ---- ", + "3 continuous (acctran) time subsets for 19 elements in 2 separated matrices with 2 phylogenetic trees", + " 2.62/1.95, 1.31/0.98, 0.", + "Disparity was calculated as: centroids." + )) + expect_null(plot(test)) + expect_equal(summary(test)$obs.median, c(0, 0, NA)) + + ## Recycling boot.matrix + ## Two matrices + tree <- rmtree(2, 10) + tree[[1]] <- makeNodeLabel(tree[[1]]) + tree[[2]] <- makeNodeLabel(tree[[2]], prefix = "shnode") + tree[[1]]$root.time <- max(tree.age(tree[[1]])$ages) + tree[[2]]$root.time <- max(tree.age(tree[[2]])$ages) + data <- list(matrix(0, nrow = Ntip(tree[[1]]) + Nnode(tree[[1]]), dimnames = list(c(tree[[1]]$tip.label, tree[[1]]$node.label))), + matrix(0, nrow = Ntip(tree[[2]]) + Nnode(tree[[2]]), dimnames = list(c(tree[[2]]$tip.label, tree[[2]]$node.label)))) + + ## Test working fine + expect_warning(boot_matrix <- boot.matrix(data, bootstraps = 7)) + test <- dispRity(boot_matrix, metric = centroids) + expect_is(test, c("dispRity")) + expect_equal(names(test), c("matrix", "tree", "call", "subsets", "disparity")) + expect_equal(capture.output(test), c( + " ---- dispRity object ---- ", + "19 elements in 2 separated matrices with 1 dimensions.", + "Data was bootstrapped 7 times (method:\"full\").", + "Disparity was calculated as: centroids." + )) + expect_null(plot(test)) + expect_equal(summary(test)$obs.median, 0) +}) diff --git a/tests/testthat/test-dispRity.utilities.R b/tests/testthat/test-dispRity.utilities.R index e4a5af37..c07f4de3 100755 --- a/tests/testthat/test-dispRity.utilities.R +++ b/tests/testthat/test-dispRity.utilities.R @@ -416,7 +416,6 @@ test_that("get.disparity", { get.disparity(data, 1, observed = TRUE) ) - test <- get.disparity(data) expect_is( test @@ -469,7 +468,6 @@ test_that("get.disparity", { names(test) ,names(data$subsets)[c(1,5)]) - ## Test whithout disparity but with distribution data <- dispRity(BeckLee_mat99, metric = centroids) expect_error(get.disparity(data, observed = FALSE)) @@ -478,8 +476,8 @@ test_that("get.disparity", { expect_equal(length(test[[1]]), nrow(BeckLee_mat99)) }) -## rescale.dispRity -test_that("rescale.dispRity", { +## scale.dispRity +test_that("scale.dispRity", { data(BeckLee_mat50) groups <- as.data.frame(matrix(data = c(rep(1, nrow(BeckLee_mat50)/2), rep(2, nrow(BeckLee_mat50)/2)), nrow = nrow(BeckLee_mat50), ncol = 1, dimnames = list(rownames(BeckLee_mat50)))) customised_subsets <- custom.subsets(BeckLee_mat50, groups) @@ -487,31 +485,31 @@ test_that("rescale.dispRity", { data <- dispRity(bootstrapped_data, metric = c(sum, centroids)) expect_error( - rescale.dispRity(bootstrapped_data) + scale.dispRity(bootstrapped_data) ) expect_error( - rescale.dispRity(data, scale = "yes") + scale.dispRity(data, scale = "yes") ) expect_error( - rescale.dispRity(data, center = "yes") + scale.dispRity(data, center = "yes") ) expect_error( - rescale.dispRity(data, center = c(1,2)) + scale.dispRity(data, center = c(1,2)) ) expect_is( - rescale.dispRity(data, scale = TRUE) + scale.dispRity(data, scale = TRUE) ,"dispRity") expect_is( - rescale.dispRity(data, scale = FALSE) + scale.dispRity(data, scale = FALSE) ,"dispRity") expect_is( - rescale.dispRity(data, scale = TRUE, center = TRUE) + scale.dispRity(data, scale = TRUE, center = TRUE) ,"dispRity") base <- summary(data) - scaled_down <- summary(rescale.dispRity(data, scale = TRUE)) - scaled_up <- summary(rescale.dispRity(data, scale = 0.1)) + scaled_down <- summary(scale.dispRity(data, scale = TRUE)) + scaled_up <- summary(scale.dispRity(data, scale = 0.1)) expect_lt( scaled_down[1,3] ,base[1,3]) @@ -705,4 +703,246 @@ test_that("tree utilities works", { expect_is(tree, "multiPhylo") expect_null(remove.tree(disparitree)$tree[[1]]) expect_null(remove.tree(disparitree2)$tree[[1]]) + + ## Remove and replace trees + disparity <- dispRity(BeckLee_mat99, metric = mean) + expect_null(disparity$tree[[1]]) + disparitree <- add.tree(tree = BeckLee_tree, data = disparity) + expect_equal(length(disparitree$tree), 1) + disparitree <- add.tree(tree = BeckLee_tree, data = disparitree) + expect_equal(length(disparitree$tree), 2) + disparitree <- add.tree(tree = BeckLee_tree, data = disparitree, replace = TRUE) + expect_equal(length(disparitree$tree), 1) +}) + +test_that("name.subsets(dispRity)", { + data(disparity) + expect_equal(name.subsets(disparity), names(disparity$subsets)) + expect_warning(test <- dispRity(matrix(rnorm(25), 5, 5), metric = mean)) + expect_null(name.subsets(test)) +}) + +test_that("get.tree with subsets", { + + ## Testing detect edges and get new tree + set.seed(1) + simple_tree <- rtree(5) + simple_tree$edge.length <- rep(1, Nedge(simple_tree)) + simple_tree$root.time <- 4 + simple_tree$node.label <- letters[1:4] + plot(simple_tree, show.tip.label = FALSE); axisPhylo() + nodelabels() + nodelabels(simple_tree$node.label, adj = -1, col = "blue") + edgelabels() + tiplabels() + tiplabels(simple_tree$tip.label, adj = -1, col = "blue") + abline(v = c(0, 1, 2, 3, 3.8), col = "grey", lty = 2) + # dev.new() + tree <- simple_tree + + ## Detect edges + expect_equal(sort(detect.edges(tree, c(5, 3, 2, 8), to.root = FALSE)), sort(c(6, 5, 4, 3, 8))) + expect_equal(sort(detect.edges(tree, c(5, 3, 2, 8), to.root = TRUE)), sort(c(6, 5, 4, 3, 8, 2))) + expect_equal(sort(detect.edges(tree, c(9, 6, 1), to.root = FALSE)), sort(c(5,3,2,1))) + expect_equal(sort(detect.edges(tree, c(9, 6, 1), to.root = TRUE)), sort(c(5,3,2,1))) + + ## Get new tree + test <- get.new.tree(tree = tree, elements = c(5, 3, 2, 8), to.root = TRUE) + expect_is(test, "phylo") + expect_equal(Ntip(test), 3) + expect_equal(Nnode(test), 4) + expect_equal(test$tip.label, c("t1", "t3", "t5")) + expect_equal(test$node.label, c("a", "b", "c", "d")) + # plot(test) ; nodelabels(test$node.label) + + test <- get.new.tree(tree = tree, elements = c(5, 3, 2, 8), to.root = FALSE) + expect_is(test, "phylo") + expect_equal(Ntip(test), 3) + expect_equal(Nnode(test), 3) + expect_equal(test$tip.label, c("t1", "t3", "t5")) + expect_equal(test$node.label, c("b", "c", "d")) + # plot(test) ; nodelabels(test$node.label) + + test <- get.new.tree(tree = tree, elements = c(5, 9, 2, 8), to.root = FALSE) + expect_is(test, "phylo") + expect_equal(Ntip(test), 3) + expect_equal(Nnode(test), 2) + expect_equal(test$tip.label, c("t1", "d", "t5")) + expect_equal(test$node.label, c("b", "c")) + # plot(test) ; nodelabels(test$node.label) + + test <- get.new.tree(tree = tree, elements = c(9, 6, 1), to.root = FALSE) + expect_is(test, "phylo") + expect_equal(Ntip(test), 2) + expect_equal(Nnode(test), 3) + expect_equal(test$tip.label, c("t2", "d")) + expect_equal(test$node.label, c("a", "b", "c")) + + test <- get.new.tree(tree = tree, elements = c(9, 6, 1), to.root = TRUE) + expect_is(test, "phylo") + expect_equal(Ntip(test), 2) + expect_equal(Nnode(test), 3) + expect_equal(test$tip.label, c("t2", "d")) + expect_equal(test$node.label, c("a", "b", "c")) + + test <- get.new.tree(tree = tree, elements = c(4,3), to.root = FALSE) + expect_is(test, "phylo") + expect_equal(Ntip(test), 2) + expect_equal(Nnode(test), 1) + expect_equal(test$tip.label, c("t3", "t4")) + expect_equal(test$node.label, c("d")) + + test <- get.new.tree(tree = tree, elements = c(4,3), to.root = TRUE) + expect_is(test, "phylo") + expect_equal(Ntip(test), 2) + expect_equal(Nnode(test), 4) + expect_equal(test$tip.label, c("t3", "t4")) + expect_equal(test$node.label, c("a", "b", "c", "d")) + + ## Big test + set.seed(2) + big_tree <- rtree(20) + big_tree$node.label <- letters[1:19] + big_tree$edge.length <- rep(1, Nedge(big_tree)) + big_tree$root.time <- max(tree.age(big_tree)$ages) + plot(big_tree, show.tip.label = FALSE); axisPhylo() + nodelabels(cex = 0.8) + nodelabels(big_tree$node.label, adj = -3, col = "blue", cex = 0.8) + edgelabels(cex = 0.8) + tiplabels(cex = 0.8) + tiplabels(big_tree$tip.label, adj = -2, col = "blue", cex = 0.8) + + test <- get.new.tree(tree = big_tree, elements = c(6, 32, 28, 15, 35), to.root = FALSE) + expect_is(test, "phylo") + expect_equal(Ntip(test), 4) + expect_equal(Nnode(test), 10) + expect_equal(test$tip.label, c("t6", "l", "o", "t12")) + expect_equal(test$node.label, c("b", "c", "d", "e", "f", "h", "i", "j", "m", "n")) + + set.seed(1) ; elements <- sample(1:39, 10) + test <- get.new.tree(tree = big_tree, elements = elements, to.root = FALSE) + expect_is(test, "phylo") + expect_equal(Ntip(test), 5) + expect_equal(Nnode(test), 16) + expect_equal(test$tip.label, c("t16", "t7", "t15", "t5", "t17")) + expect_equal(test$node.label, c("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "l", "m", "n", "o", "p", "q")) + + ## Basic subsets, return the tree per subsets + set.seed(1) + simple_tree <- rtree(10) + simple_tree$edge.length <- rep(1, Nedge(simple_tree)) + simple_tree$node.label <- letters[1:9] + matrix_dumb <- matrix(1, ncol = 1, nrow = 19, dimnames = list(c(simple_tree$tip.label, simple_tree$node.label))) + plot(simple_tree) + nodelabels(simple_tree$node.label) + + ## Groups + groups <- list("clade1" = c("t3", "t2", "t7"), + "clade2" = c("c", "f", "t4"), + "clade3" = c("t10", "a", "t7"), + "clade4" = simple_tree$tip.label) + + data <- custom.subsets(data = matrix_dumb, tree = simple_tree, group = groups) + + ## Just get trees + test_norm <- get.tree(data) + expect_is(test_norm, "phylo") + expect_equal(test_norm$tip.label, simple_tree$tip.label) + + ## Get subsets + test <- get.tree(data, subsets = TRUE) + expect_is(test, "list") + expect_equal(length(test), 4) + expect_equal(test$clade1$elements$tip.label, c("t7", "t2", "t3")) + expect_equal(test$clade2$elements$tip.label, c("c", "f", "t4")) + expect_equal(test$clade3$elements$tip.label, c("t7", "t10")) + expect_equal(test$clade4$elements$tip.label, simple_tree$tip.label) + + ## Get subsets + test <- get.tree(data, subsets = c(1,2)) + expect_is(test, "list") + expect_equal(length(test), 2) + expect_equal(names(test), c("clade1", "clade2")) + + ## Groups and bootstraps + data <- boot.matrix(data, 3) + test <- get.tree(data, subsets = TRUE) + expect_is(test, "list") + expect_equal(length(test), 4) + expect_equal(test$clade1$elements$tip.label, c("t7", "t2", "t3")) + expect_equal(test$clade2$elements$tip.label, c("c", "f", "t4")) + expect_equal(test$clade3$elements$tip.label, c("t7", "t10")) + expect_equal(test$clade4$elements$tip.label, simple_tree$tip.label) + + + + + + + ## Testing slide.node.root + set.seed(1) + simple_tree <- rtree(5) + simple_tree$edge.length <- rep(1, Nedge(simple_tree)) + simple_tree$root.time <- 4 + simple_tree$node.label <- letters[1:4] + tree <- simple_tree + bin_age <- c(2, 1) + test <- slide.node.root(bin_age, tree) + expect_is(test, "phylo") + expect_equal(Ntip(test), 4) + expect_equal(test$edge.length[c(1,6)], c(0,0)) + + ## Testing the pipeline for discrete bins + tree <- simple_tree + tree$edge.length[8] <- 1.5 + matrix_dumb <- matrix(1, ncol = 1, nrow = 9, dimnames = list(c(simple_tree$tip.label, simple_tree$node.label))) + time = c(0.5, 1, 2, 3, 3.5) + data_bins <- chrono.subsets(matrix_dumb, tree = simple_tree, method = "discrete", time = time, inc.nodes = TRUE) + + ## Getting the trees from data bin + test <- get.tree(data_bins, subsets = FALSE) + expect_is(test, "phylo") + test <- get.tree(data_bins, subsets = TRUE, to.root = FALSE) + expect_is(test, "list") + expect_equal(length(test), 4) + expect_is(test[[2]], "phylo") + expect_equal(test[[2]]$edge.length, c(0,0,1,1)) + test <- get.tree(data_bins, subsets = TRUE, to.root = TRUE) + expect_is(test, "list") + expect_equal(length(test), 4) + expect_is(test[[2]], "phylo") + expect_equal(test[[2]]$edge.length, c(1,1,1,1)) + + ## Working on a multiPhylo + multi_tree <- list(simple_tree, simple_tree) + class(multi_tree) <- "multiPhylo" + data_bins <- chrono.subsets(matrix_dumb, tree = multi_tree, method = "discrete", time = time, inc.nodes = TRUE) + test <- get.tree(data_bins, subsets = FALSE) + expect_is(test, "multiPhylo") + test <- get.tree(data_bins, subsets = TRUE, to.root = FALSE) + expect_is(test, "list") + expect_equal(length(test), 4) + expect_is(test[[2]], "multiPhylo") + + ## Working on slices + data_slices <- chrono.subsets(matrix_dumb, tree = simple_tree, method = "continuous", model = "acctran", time = time, inc.nodes = TRUE) + test <- get.tree(data_slices, subsets = TRUE, to.root = TRUE) + expect_is(test, "list") + expect_equal(length(test), 5) + expect_is(test[[2]], "phylo") + expect_equal(test[[2]]$edge.length, c(1,1)) + test <- get.tree(data_slices, subsets = TRUE, to.root = FALSE) + expect_is(test, "list") + expect_equal(length(test), 5) + expect_is(test[[2]], "phylo") + expect_equal(test[[2]]$edge.length, c(1,1)) + + ## Working on multiPhylo + data_slices <- chrono.subsets(matrix_dumb, tree = multi_tree, method = "continuous", model = "equal.split", time = time, inc.nodes = TRUE) + test <- get.tree(data_slices, subsets = FALSE) + expect_is(test, "multiPhylo") + test <- get.tree(data_slices, subsets = TRUE, to.root = FALSE) + expect_is(test, "list") + expect_equal(length(test), 5) + expect_is(test[[2]], "multiPhylo") }) \ No newline at end of file diff --git a/tests/testthat/test-match.tip.edge.R b/tests/testthat/test-match.tip.edge.R index 2736dea0..dbc76669 100755 --- a/tests/testthat/test-match.tip.edge.R +++ b/tests/testthat/test-match.tip.edge.R @@ -1,10 +1,38 @@ ## Test test_that("match.tip.edge works", { + set.seed(3) tree <- rtree(20) + trees <- list(tree, tree, tree) + class(trees) <- "multiPhylo" tip_values <- sample(c("blue", "red"), 20, replace = TRUE) + node_values <- sample(c("blue", "red"), 19, replace = TRUE) + + ## Sanitizing + wrong_multiphylo <- list(rtree(3), rtree(4)) + class(wrong_multiphylo) <- "multiPhylo" + error <- capture_error(match.tip.edge(tip_values, wrong_multiphylo)) + expect_equal(error[[1]], "The trees from wrong_multiphylo must have the same number of tips.") + error <- capture_error(match.tip.edge(tip_values, rtree(5))) + expect_equal(error[[1]], "The input vector must of the same length as the number of tips (5) or tips and nodes (9) in phylo.") + + ## NA replaces edge_colors <- match.tip.edge(tip_values, tree) expect_equal(edge_colors, c(NA, "red", "red", "red", NA, NA, "red", "blue", "blue", NA, NA, NA, "red", "red", "red", NA, "red", "blue", "red", "red", "red", "red", "red", NA, NA, "blue", NA, "blue", "red", NA, NA, NA, "blue", "red", NA, "red", "blue", "blue")) + ## Specific replaces edge_colors <- match.tip.edge(tip_values, tree, replace.na = "grey") expect_equal(edge_colors, c("grey", "red", "red", "red", "grey", "grey", "red", "blue", "blue", "grey", "grey", "grey", "red", "red", "red", "grey", "red", "blue", "red", "red", "red", "red", "red", "grey", "grey", "blue", "grey", "blue", "red", "grey", "grey", "grey", "blue", "red", "grey", "red", "blue", "blue")) + ## Parsimony = FALSE + edge_colors <- match.tip.edge(tip_values, tree, use.parsimony = FALSE) + expect_equal(edge_colors, c(NA, NA, "red", "red", NA, NA, "red", "blue", "blue", NA, NA, NA, NA, "red", "red", NA, "red", "blue", NA, NA, "red", "red", "red", NA, NA, "blue", NA, "blue", "red", NA, NA, NA, "blue", "red", NA, "red", "blue", "blue")) + + ## with nodes + edge_colors <- match.tip.edge(c(tip_values, node_values), tree) + expect_equal(edge_colors, c("red", "red", "red", "red", "blue", "blue", "red", "blue", "blue", "blue", "red", "red", "blue", "red", "red", "blue", "red", "blue", "blue", "red", "red", "red", "red", "red", "red", "blue", "blue", "blue", "red", "red", "blue", "red", "blue", "red", "blue", "red", "blue", "blue")) + + ## With multiple trees + edge_colors <- match.tip.edge(tip_values, trees) + expect_is(edge_colors, "list") + expect_equal(length(edge_colors), 3) + expect_equal(edge_colors[[1]], c(NA, "red", "red", "red", NA, NA, "red", "blue", "blue", NA, NA, NA, "red", "red", "red", NA, "red", "blue", "red", "red", "red", "red", "red", NA, NA, "blue", NA, "blue", "red", NA, NA, NA, "blue", "red", NA, "red", "blue", "blue")) }) \ No newline at end of file diff --git a/tests/testthat/test-model.test.R b/tests/testthat/test-model.test.R index 6ba8375c..08b7420b 100755 --- a/tests/testthat/test-model.test.R +++ b/tests/testthat/test-model.test.R @@ -137,7 +137,6 @@ test_that("multiple.models work", { expect_equal(dim(test$aic.models), c(13, 3)) }) - test_that("model.test example works", { set.seed(42) ## Mammal disparity through time @@ -172,7 +171,6 @@ test_that("model.test.sim example works", { error <- capture_error(model.test.sim(model.test.sim(sim = 10, model = rnorm(10)))) expect_equal(error[[1]], "model must be either a model name (character) or a dispRity object from model.test().") - set.seed(42) models <- list("Trend", "BM", "Stasis", "EB") model_test_output <- model.test(data, models, time.split = 66, verbose = FALSE) @@ -198,7 +196,7 @@ test_that("model.test.sim example works", { expect_equal(length(model_test_sim_output), 6) expect_equal(lapply(model_test_sim_output, length), list("simulation.data" = 2, - "p.value" = 12, + "p.value" = 5, "call" = 5, "nsim" = 1, "subsets" = 25, @@ -216,7 +214,7 @@ test_that("model.test.sim example works", { expect_equal(length(model_test_sim_output), 6) expect_equal(lapply(model_test_sim_output, length), list("simulation.data" = 2, - "p.value" = 12, + "p.value" = 5, "call" = 4, "nsim" = 1, "subsets" = 25, @@ -240,20 +238,19 @@ test_that("model.test.sim example works", { expect_null(plot(model_simulation, main = "A simple Brownian motion")) }) - test_that("model.test.wrapper example works", { set.seed(42) models <- list("BM", "OU", "multi.OU", "Trend") ## Some errors - expect_error(model.test.wrapper(data = data, model = "BIM", fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = FALSE, sim = 10)) + expect_error(model.test.wrapper(data = data, model = "BIM", fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = FALSE, sim = 5)) expect_error(model.test.wrapper(data = data, model = models, fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = FALSE, sim = "a")) - expect_error(model.test.wrapper(data = "a", model = models, fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = FALSE, sim = 10)) - expect_error(model.test.wrapper(data = data, model = models, fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = "yes", sim = 10)) - expect_error(model.test.wrapper(data = data, model = models, fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = FALSE, sim = 10, col.sim = 1)) - expect_error(model.test.wrapper(data = data, model = models, fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = FALSE, sim = 10, cex.p = "a")) + expect_error(model.test.wrapper(data = "a", model = models, fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = FALSE, sim = 5)) + expect_error(model.test.wrapper(data = data, model = models, fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = "yes", sim = 5)) + expect_error(model.test.wrapper(data = data, model = models, fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = FALSE, sim = 5, col.sim = 1)) + expect_error(model.test.wrapper(data = data, model = models, fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = FALSE, sim = 5, cex.p = "a")) - test <- model.test.wrapper(data = data, model = models, fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = FALSE, sim = -10, legend = TRUE, cex.p = 0.6) + test <- model.test.wrapper(data = data, model = models, fixed.optima = TRUE, time.split = 66, show.p = TRUE, verbose = FALSE, sim = -5, legend = TRUE, cex.p = 0.6) ## Check test expect_is(test, "matrix") @@ -262,10 +259,9 @@ test_that("model.test.wrapper example works", { expect_equal(colnames(test), c("aicc", "delta_aicc", "weight_aicc", "log.lik", "param", "ancestral state", "sigma squared", "alpha", "optima.2", "trend", "median p value", "lower p value", "upper p value")) ## Testing with a single model - test2 <- model.test.wrapper(data = data, model = "BM", fixed.optima = TRUE, time.split = 66, show.p = FALSE, verbose = FALSE, sim = 10) + test2 <- model.test.wrapper(data = data, model = "BM", fixed.optima = TRUE, time.split = 66, show.p = FALSE, verbose = FALSE, sim = 5) expect_is(test2, "matrix") expect_equal(dim(test2), c(1, 10)) expect_equal(rownames(test2), "") expect_equal(colnames(test2), c("aicc", "delta_aicc", "weight_aicc", "log.lik", "param", "ancestral state", "sigma squared", "median p value", "lower p value", "upper p value")) - }) \ No newline at end of file diff --git a/tests/testthat/test-multi.ace.R b/tests/testthat/test-multi.ace.R index 25e45e6b..366b5bba 100755 --- a/tests/testthat/test-multi.ace.R +++ b/tests/testthat/test-multi.ace.R @@ -390,25 +390,25 @@ test_that("multi.ace works", { "Running ancestral states estimations:" , ".......... Done.")) - set.seed(3) - test <- capture.output(results <- multi.ace(data = matrix_complex, - tree = tree_test, - models = "ER", - threshold = TRUE, - special.tokens = c("weird" = "%"), - special.behaviours = list(weirdtoken = function(x,y) return(c(1,2))), - brlen.multiplier = rnorm(10), - verbose = TRUE, - parallel = TRUE, - output = "matrix", - estimation.details = c("loglikelihood", "transition_matrix"))) - expect_equal(length(test),2) - expect_is(results, "list") - expect_equal(names(results), c("estimations", "details")) - expect_is(results$estimations, "list") - expect_is(results$estimations[[1]], "matrix") - expect_is(results$details[[1]]$transition_matrix[[9]], "matrix") - expect_equal(rownames(results$details[[1]]$transition_matrix[[9]]), c("0","1","2")) - expect_is(results$details[[2]]$loglikelihood[[1]], "numeric") + # set.seed(3) + # test <- capture.output(results <- multi.ace(data = matrix_complex, + # tree = tree_test, + # models = "ER", + # threshold = TRUE, + # special.tokens = c("weird" = "%"), + # special.behaviours = list(weirdtoken = function(x,y) return(c(1,2))), + # brlen.multiplier = rnorm(10), + # verbose = TRUE, + # parallel = 2, + # output = "matrix", + # estimation.details = c("loglikelihood", "transition_matrix"))) + # expect_equal(length(test),2) + # expect_is(results, "list") + # expect_equal(names(results), c("estimations", "details")) + # expect_is(results$estimations, "list") + # expect_is(results$estimations[[1]], "matrix") + # expect_is(results$details[[1]]$transition_matrix[[9]], "matrix") + # expect_equal(rownames(results$details[[1]]$transition_matrix[[9]]), c("0","1","2")) + # expect_is(results$details[[2]]$loglikelihood[[1]], "numeric") }) diff --git a/tests/testthat/test-null.test.R b/tests/testthat/test-null.test.R index 84b868fc..72f3aa51 100755 --- a/tests/testthat/test-null.test.R +++ b/tests/testthat/test-null.test.R @@ -4,7 +4,7 @@ # Testing data data(BeckLee_mat50) -single_disp <- dispRity(BeckLee_mat50, metric = ellipse.volume) +single_disp <- dispRity(BeckLee_mat50, metric = ellipsoid.volume) groups <- as.data.frame(matrix(data = c(rep(1, nrow(BeckLee_mat50)/2), rep(2, nrow(BeckLee_mat50)/2)), nrow = nrow(BeckLee_mat50), ncol = 1, dimnames = list(rownames(BeckLee_mat50)))) diff --git a/tests/testthat/test-pgls.dispRity.R b/tests/testthat/test-pgls.dispRity.R new file mode 100644 index 00000000..576b9f40 --- /dev/null +++ b/tests/testthat/test-pgls.dispRity.R @@ -0,0 +1,315 @@ +## Simple examples +set.seed(1) +data(BeckLee_tree) +data(BeckLee_mat99) +data(BeckLee_mat50) +data(BeckLee_ages) +nonode_tree <- BeckLee_tree +nonode_tree$node.labels <- NULL + +## Base examples +disparity_base <- dispRity(BeckLee_mat50, metric = centroids) +disparity_group <- dispRity(custom.subsets(BeckLee_mat50, group = crown.stem(BeckLee_tree, inc.nodes = FALSE)), tree = nonode_tree, metric = centroids) + +## Create a list of trees and matrices +trees_list <- replicate(4, rcoal(30), simplify = FALSE) +class(trees_list) <- "multiPhylo" +matrices_list <- space.maker(elements = 30, dimensions = 5, distribution = rnorm, elements.names = trees_list[[1]]$tip.label, replicates = 3) +matrices_groups <- custom.subsets(data = matrices_list, group = list("group1" = trees_list[[1]]$tip.label[ 1:20], + "group2" = trees_list[[1]]$tip.label[21:30])) +matrix_groups <- custom.subsets(data = matrices_list[[1]], group = list("group1" = trees_list[[1]]$tip.label[ 1:20], + "group2" = trees_list[[1]]$tip.label[21:30])) + +test_that("sanitizing works", { + expect_warning(disparity_time <- dispRity(chrono.subsets(BeckLee_mat50, FADLAD = BeckLee_ages, tree = BeckLee_tree, time = 5, method = "discrete"), metric = centroids, tree = nonode_tree)) + + ## Sanitizing + ## data + wrong_dimensions <- dispRity(BeckLee_mat50, metric = c(sum, variances)) + error <- capture_error(pgls.dispRity(data = "wrong_dimensions", tree = BeckLee_tree)) + expect_equal(error[[1]], "data must be of class dispRity.") + error <- capture_error(pgls.dispRity(data = wrong_dimensions, tree = BeckLee_tree)) + expect_equal(error[[1]], "Impossible to run a univariate pgls on wrong_dimensions because doesn't contain a dimension level-2 metric. See ?dispRity.metric for more info.") + + ## tree + wrong_tree <- rtree(50) + error <- capture_error(pgls.dispRity(data = disparity_base, tree = "wrong_tree")) + expect_equal(error[[1]], "tree must be of class phylo or multiPhylo.") + expect_warning(error <- capture_error(pgls.dispRity(data = disparity_base, tree = wrong_tree))) + expect_equal(error[[1]], "The data is not matching the tree labels (you can use ?clean.data to match both data and tree).") + error <- capture_error(pgls.dispRity(data = disparity_base)) + expect_equal(error[[1]], "No tree was found in the provided data and none was provided through the tree argument.") + + + ## formula + wrong_formula <- dispoority ~ 1 + error <- capture_error(pgls.dispRity(data = disparity_base, tree = nonode_tree, formula = wrong_formula)) + expect_equal(error[[1]], "The response term of the formula must be 'disparity'.") + expect_equal(get.formula(disparity_base), disparity ~ 1) + expect_equal(get.formula(disparity_group), disparity ~ group) + error <- capture_error(get.formula(disparity_time)) + expect_equal(error[[1]], "Some groups have overlapping elements.") + disparity_time2 <- dispRity(chrono.subsets(BeckLee_mat50, tree = BeckLee_tree, time = c(140, 66, 0), method = "discrete"), metric = centroids, tree = nonode_tree) + error <- capture_error(get.formula(disparity_time2)) + expect_equal(error[[1]], "It is currently not possible to apply an phylogenetic linear model on dispRity data with time series.") + + ## model + error <- capture_error(pgls.dispRity(data = disparity_base, tree = nonode_tree, model = "SM")) + expect_equal(error[[1]], "model must be one of the following: BM, OUrandomRoot, OUfixedRoot, lambda, kappa, delta, EB, trend.") +}) + +test_that("get.data works", { + + tree <- nonode_tree + + ## Base test (no group) + data <- add.tree(disparity_base, tree, replace = TRUE) + test <- get.pgls.data(data) + expect_equal(length(test), 1) + expect_is(test[[1]]$data, "data.frame") + expect_is(test[[1]]$phy, "phylo") + expect_equal(dim(test[[1]][[1]]), c(50, 1)) + expect_equal(colnames(test[[1]][[1]]), c("disparity")) + expect_equal(rownames(test[[1]][[1]]), rownames(data$matrix[[1]])) + + ## Test with group test + data <- add.tree(disparity_group, tree, replace = TRUE) + test <- get.pgls.data(data) + expect_equal(length(test), 1) + expect_is(test[[1]]$data, "data.frame") + expect_is(test[[1]]$phy, "phylo") + expect_equal(dim(test[[1]][[1]]), c(50, 2)) + expect_equal(colnames(test[[1]][[1]]), c("disparity", "group")) + expect_equal(rownames(test[[1]][[1]]), rownames(data$matrix[[1]])[unlist(lapply(data$subsets, `[[`, "elements"))]) + + ## Test with multiple matrices + data <- dispRity(data = matrices_list, tree = trees_list[[1]], metric = centroids) + test <- get.pgls.data(data) + expect_equal(length(test), 3) + expect_is(test[[1]]$data, "data.frame") + expect_is(test[[1]]$phy, "phylo") + expect_equal(dim(test[[1]][[1]]), c(30, 1)) + expect_equal(colnames(test[[1]][[1]]), c("disparity")) + expect_equal(rownames(test[[1]][[1]]), rownames(data$matrix[[1]])[unlist(lapply(data$subsets, `[[`, "elements"))]) + + ## Test with multiple matrices (grouped) + data <- dispRity(data = matrices_groups, tree = trees_list[[1]], metric = centroids) + test <- get.pgls.data(data) + expect_equal(length(test), 3) + expect_is(test[[1]]$data, "data.frame") + expect_is(test[[1]]$phy, "phylo") + expect_equal(dim(test[[1]][[1]]), c(30, 2)) + expect_equal(colnames(test[[1]][[1]]), c("disparity", "group")) + expect_equal(rownames(test[[1]][[1]]), rownames(data$matrix[[1]])[unlist(lapply(data$subsets, `[[`, "elements"))]) + + ## Test with multiple trees + data <- dispRity(data = matrices_list[[1]], tree = trees_list, metric = centroids) + test <- get.pgls.data(data) + expect_equal(length(test) , 4) + expect_is(test[[1]]$data, "data.frame") + expect_is(test[[1]]$phy, "phylo") + expect_equal(dim(test[[1]][[1]]), c(30, 1)) + expect_equal(colnames(test[[1]][[1]]), c("disparity")) + expect_equal(rownames(test[[1]][[1]]), rownames(data$matrix[[1]])[unlist(lapply(data$subsets, `[[`, "elements"))]) + + ## Test with multiple trees (grouped) + data <- dispRity(data = matrix_groups, tree = trees_list, metric = centroids) + test <- get.pgls.data(data) + expect_equal(length(test) , 4) + expect_is(test[[1]]$data, "data.frame") + expect_is(test[[1]]$phy, "phylo") + expect_equal(dim(test[[1]][[1]]), c(30, 2)) + expect_equal(colnames(test[[1]][[1]]), c("disparity", "group")) + expect_equal(rownames(test[[1]][[1]]), rownames(data$matrix[[1]])[unlist(lapply(data$subsets, `[[`, "elements"))]) + + ## Test with the same number of trees and matrices + data <- dispRity(data = matrices_list, tree = trees_list[1:3], metric = centroids) + test <- get.pgls.data(data) + expect_equal(length(test), 3) + expect_is(test[[1]]$data, "data.frame") + expect_is(test[[1]]$phy, "phylo") + expect_equal(dim(test[[1]][[1]]), c(30, 1)) + expect_equal(colnames(test[[1]][[1]]), c("disparity")) + expect_equal(rownames(test[[1]][[1]]), rownames(data$matrix[[1]])[unlist(lapply(data$subsets, `[[`, "elements"))]) + + ## Test with the same number of trees and matrices (grouped) + data <- dispRity(data = matrix_groups, tree = trees_list[1:3], metric = centroids) + test <- get.pgls.data(data) + expect_equal(length(test), 3) + expect_is(test[[1]]$data, "data.frame") + expect_is(test[[1]]$phy, "phylo") + expect_equal(dim(test[[1]][[1]]), c(30, 2)) + expect_equal(colnames(test[[1]][[1]]), c("disparity", "group")) + expect_equal(rownames(test[[1]][[1]]), rownames(data$matrix[[1]])[unlist(lapply(data$subsets, `[[`, "elements"))]) + + ## Error (not the same numbers) + data <- dispRity(data = matrices_groups, tree = trees_list, metric = centroids) + error <- capture_error(test <- get.pgls.data(data)) + expect_equal(error[[1]], "Data must either same number of matrices (12) and trees (4) or just one tree or matrix combined with respectively with multiple matrices or trees.") +}) + +test_that("pgls works", { + ## Testing super simple + test <- pgls.dispRity(disparity_base, tree = nonode_tree) + expect_is(test, "phylolm") + expect_equal(test$call, "dispRity interface of phylolm using: formula = disparity ~ 1 and model = BM") + + ## Testing group example + test <- pgls.dispRity(disparity_group, tree = nonode_tree) + expect_is(test, "phylolm") + expect_equal(test$call, "dispRity interface of phylolm using: formula = disparity ~ group and model = BM") + + ## Wrapped up test + disparity <- dispRity(BeckLee_mat50, metric = centroids, tree = BeckLee_tree) + test <- test.dispRity(disparity, test = pgls.dispRity) + expect_is(test, "phylolm") + expect_equal(test$call, "dispRity interface of phylolm using: formula = disparity ~ 1 and model = BM") + + ## Create a list of trees and matrices + trees_list <- replicate(10, rcoal(30), simplify = FALSE) + class(trees_list) <- "multiPhylo" + matrices_list <- space.maker(elements = 30, dimensions = 5, distribution = rnorm, elements.names = trees_list[[1]]$tip.label, replicates = 3) + + ## Disparity for one matrix and one tree + data <- dispRity(data = matrices_list[[1]], tree = trees_list[[1]], metric = edge.length.tree) + test <- pgls.dispRity(data) + expect_is(test, "phylolm") + expect_equal(test$call, "dispRity interface of phylolm using: formula = disparity ~ 1 and model = BM") + + ## Disparity for one matrix but multiple trees + data <- dispRity(data = matrices_list[[1]], tree = trees_list, metric = centroids) + test <- pgls.dispRity(data) + expect_is(test, c("dispRity", "pgls.dispRity")) + expect_equal(length(test), 10) + expect_is(test[[1]], "phylolm") + expect_equal(test[[1]]$call, "dispRity interface of phylolm using: formula = disparity ~ 1 and model = BM") +}) + +test_that("options parsed correctly", { + ## Test with different formulas + test <- pgls.dispRity(disparity_group, tree = nonode_tree, formula = disparity ~ 1) + expect_equal(test$call, "dispRity interface of phylolm using: formula = disparity ~ 1 and model = BM") + + ## Test with different model + test <- pgls.dispRity(disparity_group, tree = nonode_tree, model = "EB") + expect_equal(test$call, "dispRity interface of phylolm using: formula = disparity ~ group and model = EB") + + ## Test with additional argument + test <- pgls.dispRity(disparity_group, tree = nonode_tree, starting.value = 0) + expect_equal(test$call, "dispRity interface of phylolm using: formula = disparity ~ group and model = BM") + + ## Test with optim parameters + test <- pgls.dispRity(disparity_group, tree = nonode_tree, optim = list(method = "Nelder-Mead")) + expect_equal(test$call, "dispRity interface of phylolm using: formula = disparity ~ group and model = BM") +}) + +test_that("associated S3s work", { + + ## print + ## normal test + set.seed(1) + data <- dispRity(data = matrices_list, tree = trees_list[1:3], metric = centroids) + test <- pgls.dispRity(data) + text <- capture.output(test) + expect_equal(text, c( + "phylolm test (pgls) applied to 3 disparity estimates" , + "using the formula: disparity ~ 1 and the model: BM" , + "" , + " median sd" , + "aic 103.6818 30.51387" , + "logLik -49.8409 15.25694" , + "" , + "Parameter estimate(s) using ML:" , + " median sd" , + "sigma2 13.92685 8.516613" , + "" , + "Coefficients:" , + " median sd" , + "(Intercept) 2.109776 0.1719198" , + "" , + "You can access individual models by using their index (e.g. x[[1]])", + "or summarise and plot all models using summary(x) or plot(x)." ) + ) + ## Summary works too + text <- capture.output(summary(test)) + expect_equal(text, c( + "" , + "Call:" , + "[1] \"dispRity interface of phylolm using: formula = disparity ~ 1 and model = BM\"", + "[2] \"The statistics are calculated based on the median estimates of 3 models.\" ", + "" , + " AIC logLik " , + "103.68 -49.84 " , + "" , + "Raw residuals:" , + " Min 1Q Median 3Q Max " , + "-0.97989 -0.41799 -0.06967 0.36557 1.11200 " , + "" , + "Mean tip height: 2.282328" , + "Parameter estimate(s) using ML:" , + "sigma2: 13.92685 " , + "" , + "Coefficients:" , + " Estimate StdErr t.value p.value" , + "(Intercept) 2.1098 2.6163 0.8064 0.4266" , + "" , + "R-squared: 0\tAdjusted R-squared: 0 ") + ) + ## Plotting! + expect_null(plot(test, xlab = "hahah", col = "blue")) + + ## grouped test + set.seed(1) + data <- dispRity(data = matrices_groups, tree = trees_list[1:3], metric = centroids) + test <- pgls.dispRity(data) + text <- capture.output(test) + expect_equal(text, c( + "phylolm test (pgls) applied to 3 disparity estimates" , + "using the formula: disparity ~ group and the model: BM" , + "" , + " median sd" , + "aic 102.3646 29.07677" , + "logLik -48.1823 14.53838" , + "" , + "Parameter estimate(s) using ML:" , + " median sd" , + "sigma2 12.469 7.370084" , + "" , + "Coefficients:" , + " median sd" , + "(Intercept) 2.03348693 0.3301852" , + "groupgroup2 -0.04111237 0.4920793" , + "" , + "You can access individual models by using their index (e.g. x[[1]])", + "or summarise and plot all models using summary(x) or plot(x)." ) + ) + ## Summary works too + text <- capture.output(summary(test)) + expect_equal(text, c( + "" , + "Call:" , + "[1] \"dispRity interface of phylolm using: formula = disparity ~ group and model = BM\"", + "[2] \"The statistics are calculated based on the median estimates of 3 models.\" ", + "" , + " AIC logLik " , + "102.36 -48.18 " , + "" , + "Raw residuals:" , + " Min 1Q Median 3Q Max " , + "-1.09212 -0.26948 -0.05383 0.40231 0.76508 " , + "" , + "Mean tip height: 2.282328" , + "Parameter estimate(s) using ML:" , + "sigma2: 12.469 " , + "" , + "Coefficients:" , + " Estimate StdErr t.value p.value" , + "(Intercept) 2.033487 2.591239 0.7848 0.4392" , + "groupgroup2 -0.041112 0.369139 -0.1114 0.9121" , + "" , + "R-squared: 0.003123\tAdjusted R-squared: -0.03248 ") + ) + expect_null(plot(test)) +}) + + diff --git a/tests/testthat/test-plot.dispRity.R b/tests/testthat/test-plot.dispRity.R index f4c63a28..977d0dbf 100755 --- a/tests/testthat/test-plot.dispRity.R +++ b/tests/testthat/test-plot.dispRity.R @@ -207,7 +207,7 @@ test_that("get.quantile.col works", { expect_equal(get.quantile.col(1, 2, 2), c(3, 4)) }) -test_that("plot.observed works", { +test_that("do.plot.observed works", { ## Set plot params plot_params <- get.plot.params(data = disparity, @@ -219,11 +219,11 @@ test_that("plot.observed works", { type = "continuous", observed_args = list(observed = TRUE, col = c("black", "blue"))) plot(1) - expect_null(plot.observed(plot_params)) + expect_null(do.plot.observed(plot_params)) plot(1) plot_params <- list(observed_args = list(observed = FALSE)) - expect_null(plot.observed(plot_params)) + expect_null(do.plot.observed(plot_params)) }) @@ -273,7 +273,7 @@ test_that("plot.dispRity examples work", { error <- capture_error(plot(test_wrong, rarefaction = TRUE, col = "blue")) expect_equal(error[[1]], "Impossible to plot rarefaction curves with only one level of rarefaction. Try to use plot(..., rarefaction = 5) to just see the rarefied data for that level instead.") - ## Testing additional behaviours for plot.discrete/continuous + ## Testing additional behaviours for do.plot.discrete/continuous expect_null(plot(disparity, rarefaction = 5, type = "l", col = c("blue", "orange"))) expect_null(plot(disparity, rarefaction = 5, type = "p", col = "blue", observed = TRUE)) expect_null(plot(disparity, type = "c", col = c("blue", "orange"))) @@ -313,8 +313,8 @@ test_that("plot.dispRity with preview", { data_cust <- custom.subsets(BeckLee_mat99, crown.stem(BeckLee_tree, inc.nodes = TRUE)) data_slice <- chrono.subsets(BeckLee_mat99, tree = BeckLee_tree, method = "discrete", time = 5) - expect_null(plot.preview(data_cust, specific.args = list(dimensions = c(1,2), matrix = 1))) - expect_null(plot.preview(data_slice, specific.args = list(dimensions = c(1,2), matrix = 1))) + expect_null(do.plot.preview(data_cust, specific.args = list(dimensions = c(1,2), matrix = 1))) + expect_null(do.plot.preview(data_slice, specific.args = list(dimensions = c(1,2), matrix = 1))) expect_null(plot(data_cust)) expect_null(plot(data_slice, type = "preview", specific.args = list(dimensions = c(38, 22)), main = "Ha!")) expect_null(plot(data_slice, type = "preview", legend = FALSE, main = "Ha!")) diff --git a/tests/testthat/test-print.dispRity.R b/tests/testthat/test-print.dispRity.R index f15070b6..16ed8402 100755 --- a/tests/testthat/test-print.dispRity.R +++ b/tests/testthat/test-print.dispRity.R @@ -118,7 +118,7 @@ test_that("normal printing", { test_that("randtest printing", { set.seed(1) - obs_disparity <- dispRity(BeckLee_mat50, metric = ellipse.volume, dimensions = c(1:5)) + obs_disparity <- dispRity(BeckLee_mat50, metric = ellipsoid.volume, dimensions = c(1:5)) expect_warning(test <- null.test(obs_disparity, replicates = 100, null.distrib = rnorm)) expect_equal(capture.output(test), @@ -304,8 +304,18 @@ test_that("print.dispRity with model.test data", { " aicc log.lik param ancestral state sigma squared", "BM -31.3 17.92 2 3.099 0.002", "", - "Rank envelope test", - " p-value of the test: 0.3636364 (ties method: midrank)", - " p-interval : (0.09090909, 0.6363636)" + "Rank envelope test:", + " p-value of the global test: 0.1818182 (ties method: erl)", + " p-interval : (0, 0.6363636)" )) +}) + +test_that("dispRitreats verbose", { + ## Testing the placeholder trigger + data(disparity) + output <- capture_output(print(disparity)) + expect_equal(output, " ---- dispRity object ---- \n7 continuous (acctran) time subsets for 99 elements in one matrix with 97 dimensions with 1 phylogenetic tree\n 90, 80, 70, 60, 50 ...\nData was bootstrapped 100 times (method:\"full\") and rarefied to 20, 15, 10, 5 elements.\nDisparity was calculated as: c(median, centroids).") + disparity$call$dispRitreats <- TRUE + output <- capture_output(print(disparity)) + expect_equal(output, " ---- dispRity object ---- \n7 continuous (acctran) time subsets for 99 elements in one matrix with 97 dimensions with 1 phylogenetic tree\n 90, 80, 70, 60, 50 ...\nData was bootstrapped 100 times (method:\"full\") and rarefied to 20, 15, 10, 5 elements.\nDisparity was calculated as: c(median, centroids).\nDisparity was calculated from treats simulated data.") }) \ No newline at end of file diff --git a/tests/testthat/test-randtest.dispRity.R b/tests/testthat/test-randtest.dispRity.R index 10e44554..f1cf2a0c 100755 --- a/tests/testthat/test-randtest.dispRity.R +++ b/tests/testthat/test-randtest.dispRity.R @@ -6,41 +6,41 @@ test_that("randtest.dispRity works", { test_subset <- sample(1:100, 20) ## Sanitizing - error <- capture_error(randtest.dispRity(data = "dummy_matrix", subsets = test_subset, metric = mean, replicates = 100, resample = TRUE, alter = "lesser")) + error <- capture_error(randtest.dispRity(xtest = "dummy_matrix", subsets = test_subset, metric = mean, replicates = 100, resample = TRUE, alter = "lesser")) expect_equal(error[[1]], "data must be of class matrix or dispRity.") - error <- capture_error(randtest.dispRity(data = dummy_matrix, subsets = "test_subset", metric = mean, replicates = 100, resample = TRUE, alter = "lesser")) + error <- capture_error(randtest.dispRity(xtest = dummy_matrix, subsets = "test_subset", metric = mean, replicates = 100, resample = TRUE, alter = "lesser")) expect_equal(error[[1]], "Subsets must be a vector or a list of vector of integers or numeric values that can not exceed the number of rows in data.") - error <- capture_error(randtest.dispRity(data = dummy_matrix, subsets = test_subset, metric = "mean", replicates = 100, resample = TRUE, alter = "lesser")) + error <- capture_error(randtest.dispRity(xtest = dummy_matrix, subsets = test_subset, metric = "mean", replicates = 100, resample = TRUE, alter = "lesser")) expect_equal(error[[1]], "metric must be of class function.") - error <- capture_error(randtest.dispRity(data = dummy_matrix, subsets = test_subset, metric = mean, replicates = "100", resample = TRUE, alter = "lesser")) + error <- capture_error(randtest.dispRity(xtest = dummy_matrix, subsets = test_subset, metric = mean, replicates = "100", resample = TRUE, alter = "lesser")) expect_equal(error[[1]], "replicates must be of class numeric or integer.") - error <- capture_error(randtest.dispRity(data = dummy_matrix, subsets = test_subset, metric = mean, replicates = c(1,2.2), resample = TRUE, alter = "lesser")) + error <- capture_error(randtest.dispRity(xtest = dummy_matrix, subsets = test_subset, metric = mean, replicates = c(1,2.2), resample = TRUE, alter = "lesser")) expect_equal(error[[1]], "replicates must be a single numeric value.") - error <- capture_error(randtest.dispRity(data = dummy_matrix, subsets = test_subset, metric = mean, replicates = -1, resample = TRUE, alter = "lesser")) + error <- capture_error(randtest.dispRity(xtest = dummy_matrix, subsets = test_subset, metric = mean, replicates = -1, resample = TRUE, alter = "lesser")) expect_equal(error[[1]], "At least one replicate must be run.") - error <- capture_error(randtest.dispRity(data = dummy_matrix, subsets = test_subset, metric = mean, replicates = 100, resample = "TRUE", alter = "lesser")) + error <- capture_error(randtest.dispRity(xtest = dummy_matrix, subsets = test_subset, metric = mean, replicates = 100, resample = "TRUE", alter = "lesser")) expect_equal(error[[1]], "resample must be of class logical.") - error <- capture_error(randtest.dispRity(data = dummy_matrix, subsets = test_subset, metric = mean, replicates = 100, resample = TRUE, alter = "f")) + error <- capture_error(randtest.dispRity(xtest = dummy_matrix, subsets = test_subset, metric = mean, replicates = 100, resample = TRUE, alter = "f")) expect_equal(error[[1]], "alter must be one of the following: two-sided, greater, lesser.") ## Testing whether the mean of a random subset ## is different than the means of 100 subsets dummy_test <- randtest.dispRity( - data = dummy_matrix, + xtest = dummy_matrix, subsets = test_subset, metric = mean, alter = "lesser") expect_is(dummy_test, c("dispRity", "randtest")) expect_equal(names(dummy_test), c("rep", "observed", "random", "call", "sim", "obs", "plot", "alter", "pvalue", "expvar", "n")) ## Normal print # print_results <- capture.output(dummy_test) - # expect_equal(print_results, c("Monte-Carlo test" , "Call: randtest.dispRity(data = dummy_matrix, subsets = test_subset, ", " metric = mean)" , "", "Observation: -0.05355287 ", "", "Based on 100 replicates", "Simulated p-value: 0.5445545 ", "Alternative hypothesis: two-sided ", "", "Mean Normal residuals Random mean ", " -0.64347263 0.02131353 ", " Random variance ", " 0.01353673 ")) + # expect_equal(print_results, c("Monte-Carlo test" , "Call: randtest.dispRity(xtest = dummy_matrix, subsets = test_subset, ", " metric = mean)" , "", "Observation: -0.05355287 ", "", "Based on 100 replicates", "Simulated p-value: 0.5445545 ", "Alternative hypothesis: two-sided ", "", "Mean Normal residuals Random mean ", " -0.64347263 0.02131353 ", " Random variance ", " 0.01353673 ")) ## Normal summary expect_is(summary(dummy_test), c("summaryDefault", "table")) @@ -53,7 +53,7 @@ test_that("randtest.dispRity works", { subsets_list <- replicate(5, sample(1:100, 20), simplify = FALSE) names(subsets_list) <- LETTERS[1:5] test_list <- randtest.dispRity( - data = dummy_matrix, + xtest = dummy_matrix, subsets = subsets_list, metric = c(mean, centroids)) expect_is(test_list, c('dispRity', 'randtest')) @@ -76,4 +76,13 @@ test_that("randtest.dispRity works", { test_sum <- summary(test_list) expect_is(test_sum, "matrix") expect_equal(dim(test_sum), c(7, 7)) + + ## Testing more complex subsets + data(disparity) + test_disparity2 <- randtest.dispRity(disparity, + subsets = list("test1" = c(observed = "90"), "test2" = c(observed = "70", random = c("90", "70", "30")))) + expect_is(test_disparity2, c('dispRity', 'randtest')) + expect_equal(dim(summary(test_disparity2)), c(2, 7)) + expect_equal(names(test_disparity2), c("test1", "test2")) + expect_null(plot(test_disparity2)) }) diff --git a/tests/testthat/test-randtest.dist.R b/tests/testthat/test-randtest.dist.R index 0d01356e..d2f18913 100755 --- a/tests/testthat/test-randtest.dist.R +++ b/tests/testthat/test-randtest.dist.R @@ -1,5 +1,5 @@ ## Test -test_that("randtest.dist works", { +test_that("distance.randtest works", { set.seed(1) dummy_matrix <- matrix(rnorm(500), 100, 5) @@ -18,19 +18,19 @@ test_that("randtest.dist works", { quant <- c(0.4, 0.6) ## Sanitizing - error <- capture_error(randtest.dist("test", quantile = quant, abs = TRUE)) + error <- capture_error(distance.randtest("test", quantile = quant, abs = TRUE)) expect_equal(error[[1]], "randtest must be of class randtest.") - error <- capture_error(randtest.dist(test, quantile = "quant", abs = TRUE)) + error <- capture_error(distance.randtest(test, quantile = "quant", abs = TRUE)) expect_equal(error[[1]], "quantile must be of class numeric.") - error <- capture_error(randtest.dist(test, quantile = quant, abs = "Wrong!")) + error <- capture_error(distance.randtest(test, quantile = quant, abs = "Wrong!")) expect_equal(error[[1]], "abs must be of class logical.") - res <- randtest.dist(test) + res <- distance.randtest(test) expect_equal_round(res, c("2.5%" = -0.2861862), digits = 6) - res <- randtest.dist(test, abs = TRUE) + res <- distance.randtest(test, abs = TRUE) expect_equal_round(res, c("2.5%" = 0.2861862), digits = 6) - res <- randtest.dist(test_right, quantile = quant) + res <- distance.randtest(test_right, quantile = quant) expect_equal_round(res, c("60%" = 0.9972712), digits = 6) - res <- randtest.dist(test_left, quantile = quant) + res <- distance.randtest(test_left, quantile = quant) expect_equal_round(res, c("40%" = 0.7982932), digits = 6) }) diff --git a/tests/testthat/test-reduce.matrix.R b/tests/testthat/test-reduce.matrix.R index 70f41321..4412589e 100755 --- a/tests/testthat/test-reduce.matrix.R +++ b/tests/testthat/test-reduce.matrix.R @@ -41,8 +41,8 @@ test_that("reduce.matrix works", { expect_equal(test_col[[2]], c("3")) ## Verbose test - expect_warning(test.verbose <- capture_messages(reduce.matrix(na_matrix, distance = "gower", by.row = TRUE, verbose = TRUE))) - expect_equal(paste0(test.verbose, collapse = ""), "Searching for row(s) to remove:...Done.\n") + # expect_warning(test.verbose <- capture_messages(reduce.matrix(na_matrix, distance = "gower", by.row = TRUE, verbose = TRUE))) + # expect_equal(paste0(test.verbose, collapse = ""), "Searching for row(s) to remove:...Done.\n") ## Flipped! expect_warning(test_row <- reduce.matrix(t(na_matrix), distance = "gower", by.row = TRUE, verbose = FALSE)) diff --git a/tests/testthat/test-remove.zero.brlen.R b/tests/testthat/test-remove.zero.brlen.R index 38c5f79d..47aee367 100755 --- a/tests/testthat/test-remove.zero.brlen.R +++ b/tests/testthat/test-remove.zero.brlen.R @@ -1,5 +1,6 @@ #context("remove.zero.brlen") + ## Test test_that("remove.zero.brlen works", { ## Root connecting to a tip with zero branch length @@ -37,3 +38,33 @@ test_that("remove.zero.brlen works", { message <- capture_output(remove.zero.brlen(tree, verbose = TRUE)) expect_equal(message, "Changing 5 branch lengths:.....Done.") }) + +test_that("remove.zero.brlen works with multiPhylo", { + ## Generating some trees + trees <- rmtree(10, 10) + trees[[1]]$edge.length[2] <- 0 + expect_true(any(unlist(lapply(trees, function(x) return(x$edge.length))) == 0)) + ## Removing all 0s + trees2 <- remove.zero.brlen(trees) + expect_is(trees2, "multiPhylo") + expect_false(any(unlist(lapply(trees2, function(x) return(x$edge.length))) == 0)) +}) + +# test_that("remove.zero.brlen also removes negative brlen", { +# ## Generating a tree with negative branch lengths +# set.seed(3) +# tree <- rtree(10) +# tree_neg <- chronoMPL(tree) +# expect_true(any(tree_neg$edge.length < 0)) +# expect_true(is.ultrametric(tree_neg)) +# ## Removing negative branch length +# tree_pos <- remove.zero.brlen(tree_neg) +# expect_true(all(tree_pos$edge.length > 0)) +# expect_true(is.ultrametric(tree_pos)) + +# ## Complex test +# tree_ultra <- read.tree(file = "tree_negative.tre") +# expect_true(any(tree_ultra$edge.length < 0)) +# tree_pos <- remove.zero.brlen(tree_ultra) +# expect_true(all(tree_pos$edge.length > 0)) +# }) \ No newline at end of file diff --git a/tests/testthat/test-sanitizing.R b/tests/testthat/test-sanitizing.R index 8a4c6189..9bc3a9dc 100755 --- a/tests/testthat/test-sanitizing.R +++ b/tests/testthat/test-sanitizing.R @@ -169,26 +169,21 @@ test_that("test_equal_round works", { expect_equal(expect_equal_round(x, y, digits = 2), 1.11) }) -## Test check.dispRity.data -test_that("check.dispRity.data works", { +## Test check.data +test_that("check.data works", { + match_call <- list("data" = "my_data") ## All errors - error <- capture_error(check.dispRity.data("a")) + error <- capture_error(check.data("a", match_call)$matrix) expect_equal(error[[1]], "data must be of class matrix or data.frame or list.") - error <- capture_error(check.dispRity.data(list(matrix(c(1,2)), "a"))) - expect_equal(error[[1]], "list(matrix(c(1, 2)), \"a\") must be matrix or a list of matrices with the same dimensions and unique row names.") - error2 <- list(matrix(c(1,2)), matrix(c(1,2,3))) - error <- capture_error(check.dispRity.data(error2)) - expect_equal(error[[1]], "error2 must be matrix or a list of matrices with the same dimensions and unique row names.") - error3 <- list(matrix(c(1,2), dimnames = list(c(1:2), 1)), matrix(c(1,2), dimnames = list(c(3:4), 1))) - error <- capture_error(check.dispRity.data(error3)) - expect_equal(error[[1]], "error3 must be matrix or a list of matrices with the same dimensions and unique row names.") - + error <- capture_error(check.data(list(matrix(c(1,2)), "a"), match_call)$matrix) + expect_equal(error[[1]], "my_data must be matrix or a list of matrices with the same dimensions and unique row names.") + ## Matrix input bob <- matrix(c(1,2)) - warn <- capture_warnings(test <- check.dispRity.data(bob)) - expect_equal(warn[[1]], "Row names have been automatically added to bob.") + warn <- capture_warnings(test <- check.data(bob, match_call)$matrix) + expect_equal(warn[[1]], "Row names have been automatically added to my_data.") expect_is(test, "list") expect_is(test[[1]], "matrix") expect_equal(dim(test[[1]]), c(2,1)) @@ -196,7 +191,7 @@ test_that("check.dispRity.data works", { bob <- matrix(c(1,2)) rownames(bob) <- c(1,2) - test <- check.dispRity.data(bob) + test <- check.data(bob, match_call)$matrix expect_is(test, "list") expect_is(test[[1]], "matrix") expect_equal(dim(test[[1]]), c(2,1)) @@ -204,16 +199,16 @@ test_that("check.dispRity.data works", { ## List input bib <- list(matrix(c(1,2))) - warn <- capture_warnings(test <- check.dispRity.data(bib)) - expect_equal(warn[[1]], "Row names have been automatically added to bib.") + warn <- capture_warnings(test <- check.data(bib, match_call)$matrix) + expect_equal(warn[[1]], "Row names have been automatically added to my_data.") expect_is(test, "list") expect_is(test[[1]], "matrix") expect_equal(dim(test[[1]]), c(2,1)) expect_equal(rownames(test[[1]]), c("1","2")) bob <- list(matrix(c(1,2)), matrix(c(1,2))) - warn <- capture_warnings(test <- check.dispRity.data(bob)) - expect_equal(warn[[1]], "Row names have been automatically added to bob.") + warn <- capture_warnings(test <- check.data(bob, match_call)$matrix) + expect_equal(warn[[1]], "Row names have been automatically added to my_data.") expect_is(test, "list") expect_is(test[[1]], "matrix") expect_equal(dim(test[[1]]), c(2,1)) @@ -222,27 +217,53 @@ test_that("check.dispRity.data works", { expect_equal(rownames(test[[2]]), c("1","2")) bub <- list(matrix(c(1,2), dimnames = list(c(1:2), 1)), matrix(c(1,2), dimnames = list(c(2:1), 1))) - test <- check.dispRity.data(bub) + test <- check.data(bub, match_call)$matrix expect_is(test, "list") expect_is(test[[1]], "matrix") expect_equal(dim(test[[1]]), c(2,1)) expect_equal(dim(test[[2]]), c(2,1)) expect_equal(rownames(test[[1]]), c("1","2")) expect_equal(rownames(test[[2]]), c("1","2")) + + ## Different matrices + ## error (not the same number of rows) + data <- list(matrix(1, nrow = 2, ncol = 1, dimnames = list(letters[1:2])), matrix(1, nrow = 2, ncol = 2, dimnames = list(letters[1:2]))) + error <- capture_error(check.data(data, match_call)) + expect_equal(error[[1]], "my_data must be matrix or a list of matrices with the same dimensions and unique row names.") + ## works (outputs multi = FALSE) + data <- list(matrix(1, nrow = 2, ncol = 1, dimnames = list(letters[1:2])), matrix(1, nrow = 2, ncol = 1, dimnames = list(letters[1:2]))) + test <- check.data(data, match_call) + expect_is(test, "list") + expect_equal(names(test), c("matrix", "multi")) + expect_false(test$multi) + ## works (outputs multi = TRUE) # warning: differing stuff + data <- list(matrix(1, nrow = 2, ncol = 1, dimnames = list(letters[1:2])), matrix(1, nrow = 2, ncol = 1, dimnames = list(letters[2:3]))) + warn <- capture_warning(check.data(data, match_call)) + expect_equal(warn[[1]], "The following elements are not present in all matrices: c, a. The matrices will be treated as separate trait-spaces.") + expect_warning(test <- check.data(data, match_call)) + expect_true(test$multi) + ## works (outputs multi = TRUE) # warning: differing stuff + data <- list(matrix(1, nrow = 2, ncol = 1, dimnames = list(letters[1:2])), matrix(1, nrow = 3, ncol = 1, dimnames = list(letters[1:3]))) + warn <- capture_warning(check.data(data, match_call)) + expect_equal(warn[[1]], "The following elements are not present in all matrices: c. The matrices will be treated as separate trait-spaces.") + expect_warning(test <- check.data(data, match_call)) + expect_true(test$multi) }) -## Test check.dispRity.tree -test_that("check.dispRity.tree works", { +## Test check.tree +test_that("check.tree works", { + set.seed(1) + match_call <- list(tree = "my_tree", data = "my_data") ## One tree one data tree <- makeNodeLabel(rtree(5)) data <- matrix(0, nrow = 9, ncol = 2, dimnames = list(c(paste0("t", 1:5), paste0("Node", 1:4)))) - data <- fill.dispRity(make.dispRity(data = check.dispRity.data(data))) + data <- fill.dispRity(make.dispRity(data = check.data(data, match_call)$matrix)) ## Basic error - error <- capture_error(check.dispRity.tree(tree = "tree", data = data)) + error <- capture_error(check.tree(tree = "tree", data = data, bind.trees = FALSE, match_call)) expect_equal(error[[1]], "tree must be of class phylo or multiPhylo.") ## Basic works - test <- check.dispRity.tree(tree = tree, data = data) + test <- check.tree(tree = tree, data = data, bind.trees = FALSE, match_call)$tree expect_is(test, "multiPhylo") expect_is(test[[1]], "phylo") expect_equal(length(test), 1) @@ -252,15 +273,15 @@ test_that("check.dispRity.tree works", { tree <- list(tree, tree) class(tree) <- "multiPhylo" data <- matrix(0, nrow = 9, ncol = 2, dimnames = list(c(paste0("t", 1:5), paste0("Node", 1:4)))) - # data <- fill.dispRity(make.dispRity(data = check.dispRity.data(data))) + data <- fill.dispRity(make.dispRity(data = check.data(data, match_call)$matrix)) ## Not all trees have node labels tree_error <- tree tree_error[[1]]$node.label <- NULL - error <- capture_error(check.dispRity.tree(tree = tree_error, data = data)) + error <- capture_error(check.tree(tree = tree_error, data = data, bind.trees = FALSE, match_call)) expect_equal(error[[1]], "All trees should have node labels or no node labels.") ## multiple trees works - test <- check.dispRity.tree(tree = tree, data = data) + test <- check.tree(tree = tree, data = data, bind.trees = FALSE, match_call)$tree expect_is(test, "multiPhylo") expect_is(test[[1]], "phylo") expect_equal(length(test), 2) @@ -268,10 +289,10 @@ test_that("check.dispRity.tree works", { ## One tree multiple data tree <- makeNodeLabel(rtree(5)) data <- matrix(0, nrow = 9, ncol = 2, dimnames = list(c(paste0("t", 1:5), paste0("Node", 1:4)))) - data <- fill.dispRity(make.dispRity(data = check.dispRity.data(list(data, data)))) + data <- fill.dispRity(make.dispRity(data = check.data(list(data, data), match_call)$matrix)) ## One tree multiple data works - test <- check.dispRity.tree(tree = tree, data = data) + test <- check.tree(tree = tree, data = data, bind.trees = FALSE, match_call)$tree expect_is(test, "multiPhylo") expect_is(test[[1]], "phylo") expect_equal(length(test), 1) @@ -281,20 +302,87 @@ test_that("check.dispRity.tree works", { tree <- list(tree, tree) class(tree) <- "multiPhylo" data <- matrix(0, nrow = 9, ncol = 2, dimnames = list(c(paste0("t", 1:5), paste0("Node", 1:4)))) - data <- fill.dispRity(make.dispRity(data = check.dispRity.data(list(data, data)))) + data <- fill.dispRity(make.dispRity(data = check.data(list(data, data), match_call)$matrix)) ## multiple tree multiple data works - test <- check.dispRity.tree(tree = tree, data = data) + test <- check.tree(tree = tree, data = data, bind.trees = FALSE, match_call)$tree expect_is(test, "multiPhylo") expect_is(test[[1]], "phylo") expect_equal(length(test), 2) ## Binding works - error <- capture_error(check.dispRity.tree(tree = tree[[1]], data = data, bind.trees = TRUE)) + error <- capture_error(check.tree(tree = tree[[1]], data = data, bind.trees = TRUE, match_call)$tree) expect_equal(error[[1]], "The number of matrices and trees must be the same to bind them.") wrong_tree <- tree[[1]] wrong_tree$tip.label[1:2] <- letters[1:2] - error <- capture_error(check.dispRity.tree(tree = wrong_tree, data = data)) + + ## Is set to multi + warn <- capture_warning(check.tree(tree = wrong_tree, data = data)) + expect_equal(warn[[1]], "The following elements are not present in all trees: t1, t4, a, b. Some analyses downstream might not work because of this (you can use ?clean.data to match both data and tree if needed).") + expect_warning(test <- check.tree(tree = wrong_tree, data = data)) + expect_false(test$multi) + + ## Different trees work + set.seed(1) + tree <- makeNodeLabel(rtree(5)) + tree <- list(tree, tree) + class(tree) <- "multiPhylo" + data <- matrix(0, nrow = 9, ncol = 2, dimnames = list(c(paste0("t", 1:5), paste0("Node", 1:4)))) + data2 <- fill.dispRity(make.dispRity(data = check.data(list(data, data), match_call)$matrix)) + data1 <- fill.dispRity(make.dispRity(data = check.data(data, match_call)$matrix)) + + tree_trifurc <- tree[[1]] + tree_trifurc$edge <- tree_trifurc$edge[-5, ] + tree_trifurc$edge[c(5,6),1] <- 8 + tree_trifurc$edge.length <- tree_trifurc$edge.length[-5] + tree_trifurc$Nnode <- 3 + tree_trifurc$node.label <- tree_trifurc$node.label[-4] + tree <- list(tree[[1]], tree_trifurc) + class(tree) <- "multiPhylo" + + ## Outputs the multi part + test <- check.tree(tree[[1]], data1, bind.trees = FALSE, match_call) + expect_is(test, "list") + expect_equal(names(test), c("tree", "multi")) + expect_false(test$multi) + + ## Nodes are different between the trees but all match the one matrix + warn <- capture_warning(check.tree(tree, data1, bind.trees = FALSE, match_call)) + expect_equal(warn[[1]], "The following elements are not present in all trees: Node4. Some analyses downstream might not work because of this (you can use ?clean.data to match both data and tree if needed).") + expect_warning(test <- check.tree(tree, data1, bind.trees = FALSE, match_call)) + expect_false(test$multi) + + ## Nodes are different between the trees and match the individual matrices + error <- capture_error(check.tree(tree, data2, bind.trees = FALSE, match_call)) + expect_equal(error[[1]], "The data is not matching the tree labels (you can use ?clean.data to match both data and tree).") + error <- capture_error(check.tree(tree, data2, bind.trees = TRUE, match_call)) expect_equal(error[[1]], "The data is not matching the tree labels (you can use ?clean.data to match both data and tree).") +}) + +## Test the check.dispRity.data generic +test_that("check.dispRity.data works", { + match_call <- list(tree = "my_tree", data = "my_data") + ## One tree one data + tree <- makeNodeLabel(rtree(5)) + data <- matrix(0, nrow = 9, ncol = 2, dimnames = list(c(paste0("t", 1:5), paste0("Node", 1:4)))) + data_formed <- fill.dispRity(make.dispRity(data = check.data(data, match_call)$matrix)) + + ## Just data + test <- check.dispRity.data(data = data, returns = "matrix") + expect_is(test, "list") + expect_is(test[[1]], "matrix") + + ## Just tree + test <- check.dispRity.data(data = data, tree = tree, returns = "tree") + expect_is(test, "multiPhylo") + expect_is(test[[1]], "phylo") + + ## Correct returns + test <- check.dispRity.data(data = data, tree = tree, returns = c("tree", "multi", "matrix")) + expect_equal(names(test), c("matrix", "tree", "multi")) + test <- check.dispRity.data(data = data, tree = tree, returns = c("tree", "matrix")) + expect_equal(names(test), c("matrix", "tree")) + test <- check.dispRity.data(data = data, tree = tree, returns = c("multi", "matrix")) + expect_equal(names(test), c("matrix", "multi")) +}) -}) \ No newline at end of file diff --git a/tests/testthat/test-slice.tree.R b/tests/testthat/test-slice.tree.R index db573da9..3e0dc798 100755 --- a/tests/testthat/test-slice.tree.R +++ b/tests/testthat/test-slice.tree.R @@ -296,4 +296,26 @@ test_that("slice.tree works on a single edge", { expect_equal(slice.tree(tree, age = 3.9, "proximity"), "n2") expect_equal(slice.tree(tree, age = 1, "equal.split"), c("n2", "B", "0.5")) expect_equal(slice.tree(tree, age = 3, "gradual.split"), c("n2", "B", "0.6")) +}) + + +# Deep slice from example +test_that("example works with deep slice", { + + set.seed(1) + ## Generate a random ultrametric tree + tree <- rtree(20) + ## Add some node labels + tree$node.label <- letters[1:19] + ## Add its root time + tree$root.time <- max(tree.age(tree)$ages) + ## Slice the tree at age 1.5 + tree_slice <- slice.tree(tree, age = 1.5, "deltran") + ## The slice at age 0.5 but keeping all the ancestors + deep_slice <- slice.tree(tree, age = 1.5, "deltran", + keep.all.ancestors = TRUE) + + expect_equal(deep_slice$tip.label, c("t10", "t14", "t20", "t7", "t9", "t15", "i", "l", "l", "o", "o", "t17")) + expect_equal(which(tree.age(deep_slice)$age == 1.5), 7:11) + expect_equal(deep_slice$root.time, tree$root.time) }) \ No newline at end of file diff --git a/tests/testthat/test-slide.nodes.R b/tests/testthat/test-slide.nodes.R index 0dde0fc7..030fc172 100755 --- a/tests/testthat/test-slide.nodes.R +++ b/tests/testthat/test-slide.nodes.R @@ -1,13 +1,12 @@ #context("slide.nodes") ## Test - test_that("slide.nodes.internal works", { set.seed(1) tree <- rtree(5) ## Error output (null) - expect_null(slide.nodes.internal(nodes = c(7,8), tree = tree, slide = 4)) + expect_null(slide.nodes.internal(nodes = c(7,8), tree = tree, slide = 4, allow.negative.root = FALSE)) ## Works with a single node set.seed(42) @@ -30,7 +29,6 @@ test_that("slide.nodes.internal works", { expect_equal(tree$edge.length[-c(3,4,5)], tree_stret_up$edge.length[-c(3,4,5)]) }) - test_that("slide.nodes works", { set.seed(1) @@ -42,7 +40,7 @@ test_that("slide.nodes works", { error <- capture_error(slide.nodes(nodes = c(7,8,10), tree = tree, slide = 0.1)) expect_equal(error[[1]], "node(s) not found in tree.") warning <- capture_warning(slide.nodes(nodes = c(6,8), tree = tree, slide = 0.1)) - expect_equal(warning[[1]], "The parent of the root node (6) cannot be slideed.") + expect_equal(warning[[1]], "The parent of the root node (6) cannot be slid.") no_edge <- tree ; no_edge$edge.length <- NULL error <- capture_error(slide.nodes(nodes = c(7,8), tree = no_edge, slide = 0.1)) expect_equal(error[[1]], "The tree has no edge lengths.") @@ -81,5 +79,4 @@ test_that("slide.nodes works", { changed_branches <- c(which(tree$edge[,1] %in% move_nodes), which(tree$edge[,2] %in% move_nodes)) expect_equal(tree$edge.length[-changed_branches], tree_slideed$edge.length[-changed_branches]) expect_equal(unique(round(abs(tree$edge.length[changed_branches] - tree_slideed$edge.length[changed_branches]), 3)), 0.07) - }) diff --git a/tests/testthat/test-space.maker.R b/tests/testthat/test-space.maker.R index 535abf2a..a272916a 100755 --- a/tests/testthat/test-space.maker.R +++ b/tests/testthat/test-space.maker.R @@ -114,7 +114,6 @@ test_that("correlation works", { expect_warning(space_cor <- space.maker(1000, 20, rnorm, cor.matrix = cor.matrix)) }) - test_that("scree works", { ## One space set.seed(1) @@ -138,7 +137,6 @@ test_that("scree works", { , c(0.7, 0.0, 0.0)) }) - test_that("random.circle works", { set.seed(1) @@ -165,5 +163,17 @@ test_that("random.circle works", { test <- space.maker(elements = 10, dimensions = 4, distribution = c(random.circle, runif, runif), arguments = list(list(distribution = runif, inner = 0.5, outer = 1), list(min = 0, max = 1), list(min = 0, max = 1))) expect_equal(dim(test), c(10, 4)) +}) +## Testing element names +test_that("element.names works", { + test <- space.maker(10, 1, rnorm, elements.names = LETTERS[11:20]) + expect_equal(rownames(test), LETTERS[11:20]) }) + +## Testing replicates +test_that("replicates works", { + test <- space.maker(10, 2, rnorm, replicates = 3) + expect_equal(length(test), 3) + expect_equal(unique(lapply(test, dim)), list(c(10, 2))) +}) \ No newline at end of file diff --git a/tests/testthat/test-summary.dispRity.R b/tests/testthat/test-summary.dispRity.R index beb2ecb1..d00b266e 100755 --- a/tests/testthat/test-summary.dispRity.R +++ b/tests/testthat/test-summary.dispRity.R @@ -73,13 +73,13 @@ test_that("get.digit", { expect_equal(get.digit(1234.123456789), 0) }) -test_that("round.column", { +test_that("column.round", { column <- c(12.123, 1.1234) - expect_equal(round.column(column, digits = "default"), c(12.12, 1.12)) - expect_equal(round.column(column, digits = 5), c(12.12300, 1.12340)) - expect_equal(round.column(column, digits = 1), c(12.1, 1.1)) - expect_equal(round.column(column, digits = 0), c(12, 1)) - expect_equal(round.column(column, digits = -1), c(10, 0)) + expect_equal(column.round(column, digits = "default"), c(12.12, 1.12)) + expect_equal(column.round(column, digits = 5), c(12.12300, 1.12340)) + expect_equal(column.round(column, digits = 1), c(12.1, 1.1)) + expect_equal(column.round(column, digits = 0), c(12, 1)) + expect_equal(column.round(column, digits = -1), c(10, 0)) }) test_that("digits.fun", { diff --git a/tests/testthat/test-test.dispRity.R b/tests/testthat/test-test.dispRity.R index eebe3927..aac4248b 100755 --- a/tests/testthat/test-test.dispRity.R +++ b/tests/testthat/test-test.dispRity.R @@ -70,7 +70,7 @@ test_that("convert.to.numeric internal fun", { test_that("convert.to.character internal fun", { - expect_equal(names.fun(c(1,2,4), list("a"=1, "b"=1, "c"=1, "d"=1)), c("a", "b", "d")) + expect_equal(name.fun(c(1,2,4), list("a"=1, "b"=1, "c"=1, "d"=1)), c("a", "b", "d")) expect_true( is.null(unlist(convert.to.character(1,1))) @@ -88,7 +88,7 @@ test_that("convert.to.character internal fun", { test_that("list.to.table internal fun", { - expect_equal(rep.names("a", 2), c("a", "a")) + expect_equal(repeat.names("a", 2), c("a", "a")) expect_is( list.to.table(list("a"=rnorm(5),"b"=rnorm(5),"c"=rnorm(5))) diff --git a/tests/testthat/test-test.metric.R b/tests/testthat/test-test.metric.R index 214bfe87..3f732071 100755 --- a/tests/testthat/test-test.metric.R +++ b/tests/testthat/test-test.metric.R @@ -27,7 +27,7 @@ test_that("test.metric works", { test <- test.metric(space, metric = c(prod, ranges), replicates = 1, shifts = c("random", "size"), shift.options = list(tunning = c(max = 1000000))) expect_is(test, c("dispRity", "test.metric")) expect_equal(names(test), c("call", "results", "models", "saved_steps")) - expect_equal(names(test$results), c("random", "size.inner", "size.outer")) + expect_equal(names(test$results), c("random", "size.increase", "size.hollowness")) expect_is(test$results[[1]], "data.frame") expect_null(test$saved_steps) @@ -42,13 +42,14 @@ test_that("test.metric works", { # "Use summary(value) or plot(value) for more details." )) ## Verbose works - output <- capture_messages(test <- test.metric(space, metric = c(prod, ranges), replicates = 1, shifts = c("random", "size"), verbose = TRUE)) - expect_equal(output, c("Running the space reductions:", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", "Done.\n" , "Calculating disparity:", ".", ".", ".", "Done.\n")) + test <- test.metric(space, metric = c(prod, ranges), replicates = 1, shifts = c("random", "size"), verbose = TRUE) + # output <- capture_messages(test <- test.metric(space, metric = c(prod, ranges), replicates = 1, shifts = c("random", "size"), verbose = TRUE)) + # expect_equal(output, c("Running the space reductions:", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", ".", "Done.\n" , "Calculating disparity:", ".", ".", ".", "Done.\n")) ## Summarising basic works expect_equal(dim(summary(test)), c(3, 10)) expect_is(summary(test), "matrix") - expect_equal(rownames(summary(test)), c("random", "size.inner", "size.outer")) + expect_equal(rownames(summary(test)), c("random", "size.increase", "size.hollowness")) expect_equal(colnames(summary(test)), paste0(seq(from = 10, to = 100, by = 10), "%")) ## Plot works @@ -63,7 +64,7 @@ test_that("test.metric works", { shifts = c("random", "size", "density", "position"), verbose = FALSE) expect_is(test, c("dispRity", "test.metric")) expect_equal(names(test), c("call", "results", "models", "saved_steps")) - expect_equal(names(test$results), c("random", "size.inner", "size.outer", "density.higher", "density.lower", "position.top", "position.bottom")) + expect_equal(names(test$results), c("random", "size.increase", "size.hollowness", "density.higher", "density.lower", "position.top", "position.bottom")) expect_is(test$results[[1]], "data.frame") expect_is(test$models[[1]], "lm") diff --git a/tree_hackathon.Rmd b/tree_hackathon.Rmd new file mode 100644 index 00000000..e99165d8 --- /dev/null +++ b/tree_hackathon.Rmd @@ -0,0 +1,175 @@ +# tree hackathon! + +## Implementation idea + +The idea would be that users do `chrono.subsets` with all their desired options to generate the time bins (this is already implemented and solid), and then they could use the function `get.tree` to extract the trees from the resulting `dispRity` objects. +In the case of the time bins, it would return a list of length number of time bins with each list element containing a `multiPhylo` object that itself contains at least one tree (the one in the time bin). + +For example, if we have this tree: + +```{r} +## Testing detect edges and get new tree +set.seed(1) +simple_tree <- rtree(5) +simple_tree$edge.length <- rep(1, Nedge(simple_tree)) +simple_tree$root.time <- 4 +simple_tree$node.label <- letters[1:4] +plot(simple_tree, show.tip.label = FALSE); axisPhylo() +nodelabels() +nodelabels(simple_tree$node.label, adj = -1, col = "blue") +edgelabels() +tiplabels() +tiplabels(simple_tree$tip.label, adj = -1, col = "blue") +abline(v = c(0, 1, 2, 3, 3.8), col = "grey", lty = 2) +``` + +We can have the pipeline looking something like: + +```{r, eval = FALSE} +## Some data (to be ignored) +ignore_data <- matrix(1, ncol = 1, nrow = 9, dimnames = list(c(simple_tree$tip.label, simple_tree$node.label))) + +## Using chrono.subsets to do the time bins +my_bins <- chrono.subsets(data = ignore_data, tree = simple_tree, method = "discrete", time = c(0, 1, 2, 3, 3.8)) + +## Get the trees +my_subtrees <- get.tree(my_bins) +``` + +You can then measure branch length for each tree using something like: + +```{r} +my_branch_lengths_per_bin <- lapply(my_subtrees, lapply, function(x) sum(x$edge.length)) +``` + +Using `chrono.subsets` and `get.tree` allows to generalise the method for any type of time bins (e.g. with tips or nodes spanning across time - FADLAD), and any number of trees (e.g. using a tree distribution rather than a single tree). + +### Output format needed + +For all that to work smoothly we want `my_subtrees` in this example to be something like: + +``` +my_subtrees + | + \--- + | | + | \--<"multiPhylo"> = list of trees in the first bin for the first tree + | (if only one subtree is in that bin, it's a multiPhylo of length 1) + | + \--- + | | + | \--<"multiPhylo"> = list of trees in the first bin for the first tree + | + \--- etc. +``` + +Easy! + +# What does `chrono.subsets` outputs + +(this generalises to `custom.subsets` by the way - but ignore for this part of the implementation). + +``` +object + | + \---$matrix* = class:"list" (a list containing the orginal matrix/matrices) + | | + | \---[[1]]* = class:"matrix" (the matrix (trait-space)) + | | + | \---[[...]] = class:"matrix" (any additional matrices) + | + | + \---$tree* = class:"multiPhylo" (a list containing the attached tree(s) or NULL) + | | + | \---[[1]] = class:"phylo" (the first tree) + | | + | \---[[...]] = class:"phylo" (any additional trees) + | + | + \---$call* = class:"list" (details of the methods used) + | | + | \---$subsets = class:"character": some info about the subset type (discrete, continuous, custom) + | | + | \---more stuff. IGNORE + | + \---$subsets* = class:"list" (subsets as a list) + | | + | \---[[1]]* = class:"list" (first item in subsets list) + | | | + | | \---$elements* = class:"matrix" (one column matrix containing the elements within the first subset) + | | | + | | \---[[2]] = IGNORE (for bootstrap and rarefaction) + | | + | \---[[2]] = class:"list" (second item in subsets list) + | | | + | | \---$elements* = class:"matrix" (one column matrix containing the elements within the second subset) + | | | + | | \---[[2]] = IGNORE (for bootstrap and rarefaction) + | | + | \---[[...]] = class:"list" (the following subsets) + | | + | \---$elements* = class:"matrix" (a one column matrix containing the elements within this subset) + | | + | \---[[...]] = IGNORE (for bootstrap and rarefaction) + | + \--- more stuff. IGNORE +``` + +Basically most info about the subsets generated by `chrono.subsets` is either in `data$call$subsets` for the meta data and in `data$subsets` for the actually data. +The names of the subsets can be extracted using `name.subsets()`. +What is in each subset is in the `$elements` part, it's a matrix of usually one column with the ID of the elements. + +For example: +``` +data$subsets[[1]] + $elements + [,1] + [1,] 1 + [2,] 2 + [3,] 3 +``` +Here the integers 1:3 in the column refer to the row numbers in the data. So you can access the sub-matrix using `data$matrix[[1]][data$subsets[[1]]$elements[, 1]]` or it's rownames using `rownames(data$matrix[[1]][data$subsets[[1]]$elements[, 1]])`. +But no need to worry here. This is handled by some other bits. + +# Using these elements to get the sub trees + +The idea is to use these elements though to access the parts of the subtree and generate it as an output. +So for example, if elements 1 to 3 are the tips `t4` and `t3` and the node `d` in the simple tree above, it should output the sub tree `(d(t4,t3))`. + +I've implementing this in the following files: + + * testing: `dispRity/test/testthat/test-dispRity.utilities.R` l.725 ("get.tree with subsets") + * code: `dispRity/R/dispRity.utilities.R`: l.539 `get.tree.R` + * code: `dispRity/R/dispRity.utilities_fun.R`: l.137 `detect.edges`, `get.new.tree`, `get.one.tree.subset`. + +The best way to load is to go to the root of the repo and use `devtools::load_all()` to get all the functions linked: + +```{r} +setwd("dispRity/") +devtools::load_all() +``` + +So far I have: + +1. find the edges connecting the elements (`detect.edges`) which is used in 2 +2. using `get.new.tree` to get the tree using the elements as inputs which is used in 3 +3. using `get.one.tree.subset` as a function to `lapply` across all subsets in `get.tree` + +The problem though is that: + + * it doesn't cut branch lengths + * it doesn't take age into account + * it doesn't really works + + + + +# Working idea + +1. Feed the list of elements (in our case element 9, 2) +2. Feed the boundary ages (1 - 0.2) + * Edit the age boundaries to that the old age becomes the root of the tree and the young age becomes the cutting point +3. Recreate the subtree spanning from the selected elements (descendant from them) + * Make sure that the elements names are kept +4. Using `slice.tree` to cut the upper boundary (young age) +5. Return the new subtree