diff --git a/.Rbuildignore b/.Rbuildignore index 83c2311..4e05a51 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,5 +1,11 @@ ^.*\.Rproj$ ^\.Rproj\.user$ -.travis.yml codecov.yml -vignettes/Roahd.html +^\.github$ +^revdep$ +^cran-comments\.md$ +^CRAN-RELEASE$ +^README\.Rmd$ +^_pkgdown\.yml$ +^docs$ +^pkgdown$ diff --git a/.github/.gitignore b/.github/.gitignore new file mode 100644 index 0000000..2d19fc7 --- /dev/null +++ b/.github/.gitignore @@ -0,0 +1 @@ +*.html diff --git a/.github/workflows/check-standard.yaml b/.github/workflows/check-standard.yaml new file mode 100644 index 0000000..0528262 --- /dev/null +++ b/.github/workflows/check-standard.yaml @@ -0,0 +1,58 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/master/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: R-CMD-check + +jobs: + R-CMD-check: + runs-on: ${{ matrix.config.os }} + + name: ${{ matrix.config.os }} (${{ matrix.config.r }}) + + strategy: + fail-fast: false + matrix: + config: + - {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: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + R_KEEP_PKG_SOURCE: yes + + steps: + - uses: actions/checkout@v2 + + - uses: r-lib/actions/setup-pandoc@v1 + + - uses: r-lib/actions/setup-r@v1 + with: + r-version: ${{ matrix.config.r }} + http-user-agent: ${{ matrix.config.http-user-agent }} + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v1 + with: + extra-packages: rcmdcheck + + - uses: r-lib/actions/check-r-package@v1 + + - name: Show testthat output + if: always() + run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true + shell: bash + + - name: Upload check results + if: failure() + uses: actions/upload-artifact@main + with: + name: ${{ runner.os }}-r${{ matrix.config.r }}-results + path: check diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml new file mode 100644 index 0000000..59ae308 --- /dev/null +++ b/.github/workflows/pkgdown.yaml @@ -0,0 +1,33 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/master/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + tags: ['*'] + +name: pkgdown + +jobs: + pkgdown: + runs-on: ubuntu-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: + - uses: actions/checkout@v2 + + - uses: r-lib/actions/setup-pandoc@v1 + + - uses: r-lib/actions/setup-r@v1 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v1 + with: + extra-packages: pkgdown + needs: website + + - name: Deploy package + run: | + git config --local user.name "$GITHUB_ACTOR" + git config --local user.email "$GITHUB_ACTOR@users.noreply.github.com" + Rscript -e 'pkgdown::deploy_to_branch(new_process = FALSE)' diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml new file mode 100644 index 0000000..3c0da1c --- /dev/null +++ b/.github/workflows/test-coverage.yaml @@ -0,0 +1,30 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/master/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@v1 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v1 + with: + extra-packages: covr + + - name: Test coverage + run: covr::codecov() + shell: Rscript {0} diff --git a/.gitignore b/.gitignore index 5926e8d..9a0ac43 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,5 @@ .Rhistory .RData Rplots.pdf - -vignettes/Roahd.R -vignettes/Roahd.html +docs +*.DS_Store diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index f4ba7dd..0000000 --- a/.travis.yml +++ /dev/null @@ -1,8 +0,0 @@ -language: r -cache: scales, robustbase, devtools - -r_packages: - - covr - -after_success: - - Rscript -e 'library(covr); codecov()' diff --git a/DESCRIPTION b/DESCRIPTION index f7acfbd..cd2fc99 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,41 +1,67 @@ Package: roahd Type: Package Title: Robust Analysis of High Dimensional Data -Version: 1.4.1 -Date: 2018-08-18 -Authors@R: c( person("Nicholas", "Tarabelloni", role = c("aut", "cre"), - email = "nicholas.tarabelloni@polimi.it"), - person("Ana", "Arribas-Gil", role = "aut", - email = "aarribas@est-econ.uc3m.es" ), - person("Francesca", "Ieva", role = "aut", - email = "francesca.ieva@polimi.it" ), - person("Anna Maria", "Paganoni", role = "aut", - email = "anna.paganoni@polimi.it" ), - person("Juan", "Romo", role = "aut", - email = "romo@est-econ.uc3m.es"), - person("Francesco", "Palma", role = "ctb", - email = "frapalma7892@gmail.com") ) -Author: Nicholas Tarabelloni [aut, cre], - Ana Arribas-Gil [aut], - Francesca Ieva [aut], - Anna Maria Paganoni [aut], - Juan Romo [aut], - Francesco Palma [ctb] -Maintainer: Nicholas Tarabelloni +Version: 1.4.3.9000 +Authors@R: c( + person(given = "Nicholas", + family = "Tarabelloni", + role = "aut", + email = "nicholas.tarabelloni@polimi.it"), + person(given = "Ana", + family = "Arribas-Gil", + role = "aut", + email = "aarribas@est-econ.uc3m.es"), + person(given = "Francesca", + family = "Ieva", + role = "aut", + email = "francesca.ieva@polimi.it"), + person(given = "Anna Maria", + family = "Paganoni", + role = "aut", + email = "anna.paganoni@polimi.it"), + person(given = "Juan", + family = "Romo", + role = "aut", + email = "romo@est-econ.uc3m.es"), + person(given = "Francesco", + family = "Palma", + role = "ctb", + email = "frapalma7892@gmail.com"), + person(given = "Aymeric", + family = "Stamm", + role = c("ctb", "cre"), + email = "aymeric.stamm@math.cnrs.fr", + comment = c(ORCID = "0000-0002-8725-3654")), + person(given = "Antonio", + family = "Elias-Fernandez", + role = "ctb")) Description: A collection of methods for the robust analysis of univariate and multivariate functional data, possibly in high-dimensional cases, and hence - with attention to computational efficiency and simplicity of use. + with attention to computational efficiency and simplicity of use. See the R + Journal publication of Ieva et al. (2019) for an + in-depth presentation of the 'roahd' package. See Aleman-Gomez et al. (2021) + for details about the concept of depthgram. Depends: R (>= 2.10) License: GPL-3 +Encoding: UTF-8 LazyData: yes Suggests: - testthat, + testthat (>= 3.0.0), knitr, - rmarkdown + rmarkdown, + withr, + vdiffr Imports: scales, robustbase, magrittr, - dplyr + dplyr, + ggplot2, + plotly VignetteBuilder: knitr -RoxygenNote: 6.0.1 +RoxygenNote: 7.1.2 +Roxygen: list(markdown = TRUE) +URL: https://astamm.github.io/roahd/, https://github.com/astamm/roahd +BugReports: https://github.com/astamm/roahd/issues +Language: en-US +Config/testthat/edition: 3 diff --git a/NAMESPACE b/NAMESPACE index dc10020..95afa55 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -29,6 +29,9 @@ S3method(MHRD,fData) S3method(as.mfData,list) S3method(cov_fun,fData) S3method(cov_fun,mfData) +S3method(depthgram,default) +S3method(depthgram,fData) +S3method(depthgram,mfData) S3method(fbplot,fData) S3method(fbplot,mfData) S3method(mean,fData) @@ -42,8 +45,10 @@ S3method(multiMEI,mfData) S3method(multiMHI,default) S3method(multiMHI,mfData) S3method(plot,Cov) +S3method(plot,depthgram) S3method(plot,fData) S3method(plot,mfData) +export("%>%") export(BCIntervalSpearman) export(BCIntervalSpearmanMultivariate) export(BD) @@ -66,6 +71,7 @@ export(cor_kendall) export(cor_spearman) export(cor_spearman_accuracy) export(cov_fun) +export(depthgram) export(exp_cov_function) export(fDColorPalette) export(fData) @@ -89,27 +95,5 @@ export(toListOfValues) export(toRowMatrixForm) export(unfold) export(warp) -importFrom(dplyr,filter) -importFrom(dplyr,group_by) -importFrom(dplyr,summarize) -importFrom(grDevices,col2rgb) -importFrom(grDevices,dev.cur) -importFrom(grDevices,dev.set) -importFrom(grDevices,rgb) -importFrom(graphics,image) -importFrom(graphics,lines) -importFrom(graphics,matplot) -importFrom(graphics,par) -importFrom(graphics,plot) -importFrom(graphics,points) -importFrom(graphics,polygon) -importFrom(graphics,text) +import(ggplot2) importFrom(magrittr,"%>%") -importFrom(stats,approx) -importFrom(stats,cor) -importFrom(stats,cov) -importFrom(stats,pnorm) -importFrom(stats,qnorm) -importFrom(stats,quantile) -importFrom(stats,rnorm) -importFrom(stats,uniroot) diff --git a/NEWS.md b/NEWS.md index 60c280b..9a699b8 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,20 +1,146 @@ -# Changelog +# roahd 1.4.3.9000 -Here's a list of what is changed in this update of __roahd__: +# roahd 1.4.3 -### Upgrades +## New feature -#### Major upgrades +* Added tools for manipulating and visualizing depthgrams (#1, @aefdz). This mathematical constructs aim at facilitating the visualization of outliers in high dimensional functional data sets. The [`depthgram()`](https://astamm.github.io/roahd/reference/depthGram.html) function computes a number of depthgrams from the functional data set. An S3 specialized method for [`plot()`](https://astamm.github.io/roahd/reference/plot.depthgram.html) makes it possible to visualize the depthgrams and proceed with a visual inspection at outliers. -#### Minor updates +## Minor updates -### Fixes +* Improved unit testing: Refactoring of unit tests using [**testthat**](https://testthat.r-lib.org) 3e edition and in particular snapshot tests for plots and complex objects. +* Added an hexsticker. -Fixed dependency error on a new version of `scales` package that breaks the use of multivariate fbplot in the corner-case of zero -outliers. +# roahd 1.4.2 -#### Major fixes +## Minor updates -#### Minor fixes +* Switch from Travis to Github Actions for continuous integration. +* Setup automatic `R CMD check` on Windows, macOS and Linux for both the latest +release and the development version of R. +* Setup automatic deployment of a [website](https://astamm.github.io/roahd/) for +the package that references a package introduction, its help and all vignettes. +* Setup automatic computation of [test coverage](https://about.codecov.io/) and +report to both the [Github page](https://github.com/astamm/roahd) and +[website](https://astamm.github.io/roahd/) of the package. +* Added CRAN status badge to `README`. +* New package maintainer. +## Fixes +### Major fixes + +* Updated all `matrix` class checks for compliance with R-4.0 in which the +`matrix` class inherits from the `array` class. + +### Minor fixes + +* Fixed typos in doc, vignette and README. +* Fixed bug in `fbplot()` display. + +# roahd 1.4.1 + +## Fixes + +### Minor fixes + +* Fixed dependency error on a new version of **scales** package that breaks the +use of multivariate `fbplot` in the corner-case of zero outliers. + +# roahd 1.4 + +## Upgrades + +### Major upgrades + +* Extended Spearman's correlation coefficient computation for multivariate +datasets with more than two components. +* Added bootstrap-based computation of Spearman's correlation coefficient bias +and standard deviation. +* Added methods to provide bootstrap-based confidence intervals on Spearman's +coefficients for two univariate functional datasets or a multivariate functional +dataset. +* Added a bootstrap-based test on Spearman's correlation coefficient for two +multivariate functional datasets. +* Added an outliergram version (without graphical display of original data) of +multivariate functional datasets. +* Added example multivariate functional datasets of ECG signals. + +### Minor updates + +* Added two convenience functions to append compatible functional datasets +(univariate or multivariate). +* Added a `[` operator overload for multivariate functional dataset +representation `mfData`. + +## Fixes + +### Major fixes + +* Fixed bug in `cor_spearman()` function. Now the standard Spearman correlation +is not computed on ranks of MHI/MEI, but on MHI/MEI itself. The difference is +very small, but allows for full reproducibility of the results in the original +paper. + +### Minor fixes + +* Fixed typos in doc. +* Standardized formulas for the application of F inflations in outliergram and +boxplot. + +# roahd 1.2 + +## Fixes + +### Major fixes + +* Removed check for uniformity in the grid of `fData()` and `mfData()` +constructor. +* Added the possibility to subset `fData` in time with logical vectors. +* Fixes in methods `BD`, `BD_relative`, `HI` and `EI`: the previous +computational technique was based on arguments from the popular reference "Exact +fast computation of band depth for large functional datasets: How quickly can +one million curves be ranked?" by Sun, Genton and Nychka, which in the case of +BD, and HI/EI are wrong. Now the implementation exploited sticks to the +definition, at the cost of a higher computational burden (and thus, time to +complete the computation). + +# roahd 1.1 + +## Fixes + +### Major fixes + +* Modified the check of the grid provided to build fData objects. Since support is provided only for evenly spaced grids, a check is needed before building an `fData` object. Before it was: + +```r +all(abs(diff(unique(diff(grid)))) < 1e-14) +``` + +Now it is: + +```r +max(diff(unique(diff(grid)))) / diff(range(grid)) < 1e-13 +``` + +which is much more robust in practical cases. + +* Extended `README.md`. +* Added `cov_fun` method to compute covariance and cross-covariance functions +for either univariate or multivariate functional data. Implemented the `S3` class +`Cov` and plotting specialization `plot.Cov`, wrapping `graphics::image`. + +### Minor fixes + +* Fixed typos in documentation. +* Fixed typos in vignette. +* Added [Travis](https://travis-ci.org/ntarabelloni/roahd) and +[Codecov](https://app.codecov.io/gh/ntarabelloni/roahd) support. +* Modified the default parameter value for `trial_size` in `fbplot` from +`Data$N` to `8 * Data$N`. +* Added check to `fbplot` and `outliergram` that raises warnings when parameters +different than those supported are provided through `adjust` argument. + +# roahd 1.0 + +* Initial release of the package. diff --git a/R/BandDepths.R b/R/band_depths.R similarity index 97% rename from R/BandDepths.R rename to R/band_depths.R index 20a61be..c61e46b 100644 --- a/R/BandDepths.R +++ b/R/band_depths.R @@ -185,9 +185,9 @@ BD.default = function( Data ) #' BD_relative = function( Data_target, Data_reference ) { - if( class( Data_target ) != class( Data_reference ) ) + if( any(class( Data_target ) != class( Data_reference )) ) { - if( ! ( class( Data_target ) %in% c( 'numeric', 'array', 'matrix' ) & + if( ! all( class( Data_target ) %in% c( 'numeric', 'array', 'matrix' ) & class( Data_reference ) %in% c( 'numeric', 'array', 'matrix' ) ) ) { stop( 'Error in BD_relative: you have to provide target and reference data @@ -262,7 +262,7 @@ not compliant dimensions to BD_relative') #' \tilde{\lambda}\big( {t : \min( X_{i_1}(t), X_{i_2}(t) ) \leq X(t) \leq #' \max( X_{i_1}(t), X_{i_2}(t) ) } \big), } #' -#' where \eqn{\tilde{\lambda}(\cdot)} is the normalised Lebesgue measure over +#' where \eqn{\tilde{\lambda}(\cdot)} is the normalized Lebesgue measure over #' \eqn{I=[a,b]}, that is \eqn{\tilde{\lambda(A)} = \lambda( A ) / ( b - a )}. #' #' See the References section for more details. @@ -396,7 +396,7 @@ MBD.default = function( Data, manage_ties = FALSE ) #' \max( Y_{i_1}(t), Y_{i_2}(t) ) } \big),} #' #' \eqn{\forall i = 1, \ldots, N}, where \eqn{\tilde{\lambda}(\cdot)} is the -#' normalised Lebesgue measure over \eqn{I=[a,b]}, that is +#' normalized Lebesgue measure over \eqn{I=[a,b]}, that is #' \eqn{\tilde{\lambda(A)} = \lambda( A ) / ( b - a )}. #' #' @param Data_target is the univariate functional dataset, provided either as @@ -480,9 +480,9 @@ MBD.default = function( Data, manage_ties = FALSE ) #' MBD_relative = function( Data_target, Data_reference ) { - if( class( Data_target ) != class( Data_reference ) ) + if( any(class( Data_target ) != class( Data_reference )) ) { - if( ! ( class( Data_target ) %in% c( 'numeric', 'array', 'matrix' ) & + if( ! all( class( Data_target ) %in% c( 'numeric', 'array', 'matrix' ) & class( Data_reference ) %in% c( 'numeric', 'array', 'matrix' ) ) ) { stop( 'Error in MBD_relative: you have to provide target and reference data diff --git a/R/bootstrap_spearman_inference.R b/R/bootstrap_spearman_inference.R index 23edbd8..5895554 100644 --- a/R/bootstrap_spearman_inference.R +++ b/R/bootstrap_spearman_inference.R @@ -23,33 +23,35 @@ #' \code{\link{mfData}}, \code{\link{BCIntervalSpearmanMultivariate}} #' #' @examples -#' #' set.seed(1) #' -#' N = 2e2 -#' P = 1e2 -#' grid = seq( 0, 1, length.out = P ) +#' N <- 200 +#' P <- 100 +#' +#' grid <- seq(0, 1, length.out = P) #' -#' # Creating an exponential covariance function to simulate guassian data -#' Cov = exp_cov_function( grid, alpha = 0.3, beta = 0.4 ) +#' # Creating an exponential covariance function to simulate Gaussian data +#' Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) #' -#' # Simulating (independent) gaussian functional data with given center and -#' # covariance function -#' Data_1 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) -#' Data_2 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) +#' # Simulating (independent) Gaussian functional data with given center and covariance function +#' Data_1 <- generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) +#' Data_2 <- generate_gauss_fdata( +#' N = N, +#' centerline = sin(2 * pi * grid), +#' Cov = Cov +#' ) #' -#' # Using the simulated data as (independent) components of a bivariate functional -#' # dataset -#' mfD = mfData( grid, list( Data_1, Data_2 ) ) -#'\dontrun{ -#' BCIntervalSpearman(mfD$fDList[[1]], mfD$fDList[[2]], ordering = 'MEI') +#' # Using the simulated data as (independent) components of a bivariate functional dataset +#' mfD <- mfData(grid, list(Data_1, Data_2)) +#' +#' \donttest{ +#' BCIntervalSpearman(mfD$fDList[[1]], mfD$fDList[[2]], ordering = "MEI") +#' BCIntervalSpearman(mfD$fDList[[1]], mfD$fDList[[2]], ordering = "MHI") +#' } #' -#' BCIntervalSpearman(mfD$fDList[[1]], mfD$fDList[[2]], ordering = 'MHI') -#'} #' # BC intervals contain zero since the functional samples are uncorrelated. #' #' @export -#' BCIntervalSpearman = function( fD1, fD2, ordering='MEI', bootstrap_iterations=1000, alpha=0.05, verbose=FALSE ){ @@ -76,7 +78,7 @@ BCIntervalSpearman = function( fD1, fD2, ordering='MEI', bootstrap_iterations=10 # bias-correction pz0 = sum( v < cor_spearman( as.mfData(list(fD1, fD2)), ordering=ordering ) ) / bootstrap_iterations - z0 = qnorm( pz0, mean=0, sd=1 ) + z0 = stats::qnorm( pz0, mean=0, sd=1 ) # vector of jackknife cor_spearman values theta_i = matrix(0, nrow=1, ncol=N) @@ -91,11 +93,11 @@ BCIntervalSpearman = function( fD1, fD2, ordering='MEI', bootstrap_iterations=10 a = sum( ( theta_hat - theta_i )^3 )/( 6 * sum( ( theta_hat - theta_i )^2 )^( 1.5 ) ) # first percentile - alpha1 = pnorm( z0 + (z0 + qnorm( alpha/2, mean=0, sd = 1 ) )/ - ( 1 - a*( z0 + qnorm( alpha/2, mean = 0, sd = 1 ) ) ), mean = 0, sd = 1) + alpha1 = stats::pnorm( z0 + (z0 + stats::qnorm( alpha/2, mean=0, sd = 1 ) )/ + ( 1 - a*( z0 + stats::qnorm( alpha/2, mean = 0, sd = 1 ) ) ), mean = 0, sd = 1) # second percentile - alpha2 = pnorm( z0 + (z0 + qnorm( 1-alpha/2, mean = 0,sd = 1 ) )/ - ( 1 - a*( z0 + qnorm( 1 - alpha/2, mean = 0, sd = 1 ) ) ), mean = 0, sd = 1) + alpha2 = stats::pnorm( z0 + (z0 + stats::qnorm( 1-alpha/2, mean = 0,sd = 1 ) )/ + ( 1 - a*( z0 + stats::qnorm( 1 - alpha/2, mean = 0, sd = 1 ) ) ), mean = 0, sd = 1) v = sort( v ) # it's the case in which alpha1 * bootstrap_iterations is an integer @@ -145,38 +147,47 @@ BCIntervalSpearman = function( fD1, fD2, ordering='MEI', bootstrap_iterations=10 #' \code{\link{mfData}}, \code{\link{BCIntervalSpearman}} #' #' @examples -#' #' set.seed(1) #' -#' N = 2e2 -#' P = 1e2 -#' grid = seq( 0, 1, length.out = P ) -#' -#' # Creating an exponential covariance function to simulate guassian data -#' Cov = exp_cov_function( grid, alpha = 0.3, beta = 0.4 ) -#' -#' # Simulating (independent) gaussian functional data with given center and -#' # covariance function -#' Data_1 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) -#' Data_2 = generate_gauss_fdata( N, centerline = sin( 4 * pi * grid ), Cov = Cov ) -#' Data_3 = generate_gauss_fdata( N, centerline = sin( 6 * pi * grid ), Cov = Cov ) -#' -#' # Using the simulated data as (independent) components of a multivariate functional -#' # dataset -#' mfD = mfData( grid, list( Data_1, Data_2, Data_3 ) ) +#' N <- 200 +#' P <- 100 +#' grid <- seq(0, 1, length.out = P) +#' +#' # Creating an exponential covariance function to simulate Gaussian data +#' Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) +#' +#' # Simulating (independent) Gaussian functional data with given center and covariance function +#' Data_1 <- generate_gauss_fdata( +#' N = N, +#' centerline = sin(2 * pi * grid), +#' Cov = Cov +#' ) +#' Data_2 <- generate_gauss_fdata( +#' N = N, +#' centerline = sin(4 * pi * grid), +#' Cov = Cov +#' ) +#' Data_3 <- generate_gauss_fdata( +#' N = N, +#' centerline = sin(6 * pi * grid), +#' Cov = Cov +#' ) +#' +#' # Using the simulated data as (independent) components of a multivariate functional dataset +#' mfD <- mfData(grid, list(Data_1, Data_2, Data_3)) +#' +#' \donttest{ +#' BCIntervalSpearmanMultivariate(mfD, ordering = "MEI") +#' } #' -#'\dontrun{ -#' BCIntervalSpearmanMultivariate(mfD, ordering = 'MEI') -#'} #' # BC intervals contain zero since the functional samples are uncorrelated. #' #' @export -#' BCIntervalSpearmanMultivariate = function(mfD, - ordering='MEI', - bootstrap_iterations=1000, - alpha=0.05, - verbose=FALSE) + ordering = 'MEI', + bootstrap_iterations = 1000, + alpha = 0.05, + verbose = FALSE) { lower = matrix(0, nrow=mfD$L, ncol=mfD$L) @@ -241,7 +252,7 @@ BCIntervalSpearmanMultivariate = function(mfD, #' be compatible with \code{mfD1}. #' @param bootstrap_iterations is the number of bootstrap iterations to be performed. #' @param ordering is the kind of ordering to be used in the computation of Spearman's correlation -#' coefficeint (default is \code{MEI}). +#' coefficient (default is \code{MEI}). #' @param normtype is the norm to be used when comparing the Spearman correlation matrices of the two #' functional datasets (default is Frobenius, allowed values are the same as for parameter \code{type} in #' the base function \code{norm}). @@ -251,46 +262,69 @@ BCIntervalSpearmanMultivariate = function(mfD, #' The function returns the estimates of the test's p-value and statistics. #' #' @seealso \code{\link{BCIntervalSpearman}}, \code{\link{BCIntervalSpearmanMultivariate}}, \code{\link{mfData}} -#' @examples #' +#' @examples #' set.seed(1) -#' N = 2e2 -#' P = 1e2 -#' L = 2 -#' grid = seq( 0, 1, length.out = P ) -#' # Creating an exponential covariance function to simulate guassian data -#' Cov = exp_cov_function( grid, alpha = 0.3, beta = 0.4 ) #' +#' N <- 200 +#' P <- 100 +#' L <- 2 +#' +#' grid <- seq(0, 1, length.out = P) #' +#' # Creating an exponential covariance function to simulate Gaussian data +#' Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) #' #' # Simulating two populations of bivariate functional data #' # #' # The first population has very high correlation between first and second component -#' centerline_1 = matrix(rep(sin(2 * pi * grid)), nrow = 2, ncol=P, byrow=TRUE) -#' values1 = generate_gauss_mfdata( N, L, correlations = 0.9, -#' centerline = centerline_1, listCov = list(Cov, Cov) ) -#' mfD1 = mfData(grid, values1) +#' centerline_1 <- matrix( +#' data = rep(sin(2 * pi * grid)), +#' nrow = L, +#' ncol = P, +#' byrow = TRUE +#' ) +#' values1 <- generate_gauss_mfdata( +#' N = N, +#' L = L, +#' correlations = 0.9, +#' centerline = centerline_1, +#' listCov = list(Cov, Cov) +#' ) +#' mfD1 <- mfData(grid, values1) #' #' # Pointwise estimate #' cor_spearman(mfD1) #' #' # The second population has zero correlation between first and second component -#' centerline_2 = matrix(rep(cos(2 * pi * grid)), nrow = 2, ncol=P, byrow=TRUE) -#' values2 = generate_gauss_mfdata( N, L, correlations = 0, -#' centerline = centerline_1, listCov = list(Cov, Cov) ) -#' mfD2 = mfData(grid, values2) +#' centerline_2 <- matrix( +#' data = rep(cos(2 * pi * grid)), +#' nrow = L, +#' ncol = P, +#' byrow = TRUE +#' ) +#' values2 <- generate_gauss_mfdata( +#' N = N, +#' L = L, +#' correlations = 0, +#' centerline = centerline_2, +#' listCov = list(Cov, Cov) +#' ) +#' mfD2 <- mfData(grid, values2) #' #' # Pointwise estimate #' cor_spearman(mfD2) #' #' # Applying the test -#' \dontrun{ +#' \donttest{ #' BTestSpearman(mfD1, mfD2) #' } #' @export -#' -BTestSpearman = function( mfD1, mfD2, bootstrap_iterations=1000, ordering='MEI', normtype='f', - verbose=FALSE) +BTestSpearman = function(mfD1, mfD2, + bootstrap_iterations = 1000, + ordering = "MEI", + normtype = "f", + verbose = FALSE) { stopifnot((mfD1$P == mfD2$P) & (mfD1$t0 == mfD2$t0) & (mfD1$tP == mfD2$tP) & (mfD1$fDList[[1]]$h == mfD2$fDList[[1]]$h) & (mfD1$L == mfD2$L)) diff --git a/R/correlation.R b/R/correlation.R index bf9cc2c..596e4c5 100644 --- a/R/correlation.R +++ b/R/correlation.R @@ -1,23 +1,22 @@ #' Maxima of a univariate functional dataset #' -#' This function computes the maximum value of each element of a -#' univariate functional dataset, optionally returing also the value of the -#' grid where they are fulfilled. +#' This function computes the maximum value of each element of a univariate +#' functional dataset, optionally returning also the value of the grid where +#' they are fulfilled. #' #' @param fData the functional dataset containing elements whose maxima have to -#' be computed, in form of \code{fData} object. +#' be computed, in form of \code{fData} object. #' @param ... additional parameters. #' @param which logical flag specifying whether the grid values where maxima are -#' fulfilled have to be returned too. +#' fulfilled have to be returned too. #' #' @return If \code{which = FALSE}, the function returns a vector containing the -#' maxima for each element of the functional dataset; if \code{which = TRUE}, -#' the function returns a \code{data.frame} whose field \code{value} contains -#' the values of maxima, and \code{grid} contains the grid points where maxima -#' are reached. +#' maxima for each element of the functional dataset; if \code{which = TRUE}, +#' the function returns a \code{data.frame} whose field \code{value} contains +#' the values of maxima, and \code{grid} contains the grid points where maxima +#' are reached. #' #' @examples -#' #' P = 1e3 #' #' grid = seq( 0, 1, length.out = P ) @@ -34,7 +33,6 @@ #' @seealso \code{\link{minima}} #' #' @export -#' maxima = function( fData, ..., which = FALSE ) { if( which ) @@ -48,24 +46,23 @@ maxima = function( fData, ..., which = FALSE ) } - #' Minima of a univariate functional dataset #' #' This function computes computes the minimum value of each element of a -#' univariate functional dataset, optionally returing also the value of the -#' grid where they are fulfilled. +#' univariate functional dataset, optionally returning also the value of the grid +#' where they are fulfilled. #' #' @param fData the functional dataset containing elements whose minima have to -#' be computed, in form of \code{fData} object. +#' be computed, in form of \code{fData} object. #' @param ... additional parameters. #' @param which logical flag specifying whether the grid values where minima are -#' fulfilled have to be returned too. +#' fulfilled have to be returned too. #' #' @return If \code{which = FALSE}, the function returns a vector containing the -#' minima for each element of the functional dataset; if \code{which = TRUE}, -#' the function returns a \code{data.frame} whose field \code{value} contains -#' the values of minima, and \code{grid} contains the grid points where minima -#' are reached. +#' minima for each element of the functional dataset; if \code{which = TRUE}, +#' the function returns a \code{data.frame} whose field \code{value} contains +#' the values of minima, and \code{grid} contains the grid points where minima +#' are reached. #' #' @examples #' @@ -85,7 +82,6 @@ maxima = function( fData, ..., which = FALSE ) #' @seealso \code{\link{maxima}} #' #' @export -#' minima = function( fData, ..., which = FALSE ) { if( which ) @@ -98,45 +94,43 @@ minima = function( fData, ..., which = FALSE ) } - -#' Maximum order relation between univariate functional data +#' Maximum order relation between univariate functional data #' -#' This function implements an order relation between univariate functional -#' data based on the maximum relation, that is to say a pre-order relation -#' obtained by comparing the maxima of two different functional data. +#' This function implements an order relation between univariate functional data +#' based on the maximum relation, that is to say a pre-order relation obtained +#' by comparing the maxima of two different functional data. #' -#' Given a univariate functional dataset, \eqn{X_1(t), X_2(t), \ldots, X_N(t)} -#' and another functional dataset \eqn{Y_1(t),} \eqn{Y_2(t), \ldots, Y_M(t)} -#' defined over the same compact interval \eqn{I=[a,b]}, the function computes -#' the maxima in both the datasets, and checks whether the first ones are lower -#' or equal than the second ones. +#' Given a univariate functional dataset, \eqn{X_1(t), X_2(t), \ldots, X_N(t)} +#' and another functional dataset \eqn{Y_1(t),} \eqn{Y_2(t), \ldots, Y_M(t)} +#' defined over the same compact interval \eqn{I=[a,b]}, the function computes +#' the maxima in both the datasets, and checks whether the first ones are lower +#' or equal than the second ones. #' -#' By default the function tries to compare each \eqn{X_i(t)} with the -#' corresponding \eqn{Y_i(t)}, thus assuming \eqn{N=M}, but when either \eqn{N=1} -#' or \eqn{M=1}, the comparison is carried out cycling over the dataset with -#' fewer elements. In all the other cases (\eqn{N\neq M,} and either -#' \eqn{N \neq 1} or \eqn{M \neq 1}) the function stops. +#' By default the function tries to compare each \eqn{X_i(t)} with the +#' corresponding \eqn{Y_i(t)}, thus assuming \eqn{N=M}, but when either +#' \eqn{N=1} or \eqn{M=1}, the comparison is carried out cycling over the +#' dataset with fewer elements. In all the other cases (\eqn{N\neq M,} and +#' either \eqn{N \neq 1} or \eqn{M \neq 1}) the function stops. #' #' @param fData the first univariate functional dataset containing elements to -#' be compared, in form of \code{fData} object. +#' be compared, in form of \code{fData} object. #' @param gData the second univariate functional dataset containing elements to -#' be compared, in form of \code{fData} object. +#' be compared, in form of \code{fData} object. #' -#' @return -#' The function returns a logical vector of length \eqn{\max(N,M)} containing the -#' value of the predicate for all the corresponding elements. +#' @return The function returns a logical vector of length \eqn{\max(N,M)} +#' containing the value of the predicate for all the corresponding elements. #' #' @references #' #' Valencia, D., Romo, J. and Lillo, R. (2015). A Kendall correlation -#' coefficient for functional dependence, -#' \emph{Universidad Carlos III de Madrid technical report}, +#' coefficient for functional dependence, \emph{Universidad Carlos III de Madrid +#' technical report}, #' \code{http://EconPapers.repec.org/RePEc:cte:wsrepe:ws133228}. #' #' #' #' @seealso \code{\link{maxima}}, \code{\link{minima}}, \code{\link{fData}}, -#' \code{\link{area_ordered}} +#' \code{\link{area_ordered}} #' #' @examples #' @@ -183,27 +177,27 @@ max_ordered = function( fData, gData ) return( maxima( fData ) - maxima( gData ) <= 0 ) } -#' #' Area under curve of elements of univariate functional data #' #' This method computes the (signed) area under the curve of elements of a #' univariate functional dataset, namely, their integral. #' #' Given a univariate functional dataset, \eqn{X_1(t), X_2(t), \ldots, X_N(t)}, -#' defined over a compact interval \eqn{I=[a,b]} and observed on an evenly sapced -#' 1D grid \eqn{[a = t_0, t_1, \ldots, t_{P-1} = b \subset I}, the function -#' computes:} +#' defined over a compact interval \eqn{I=[a,b]} and observed on an evenly +#' spaced 1D grid \eqn{[a = t_0, t_1, \ldots, t_{P-1} = b] \subset I}, the +#' function computes: #' -#' \deqn{ \sum_{i=1}^{P-2} \frac{X(t_{i+1}) - X(t_{i-1})}{2} h \approx -#' \int_a^b X(t) dt,} +#' \deqn{ \sum_{i=1}^{P-2} \frac{X(t_{i+1}) - X(t_{i-1})}{2} h \approx \int_a^b +#' X(t) dt,} #' #' where \eqn{h = t_1 - t_0}. #' #' @param fData the functional dataset containing elements whose areas under the -#' curve have to be computed, in form of \code{fData} object. +#' curve have to be computed, in form of \code{fData} object. #' #' @return The function returns a numeric vector containing the values of areas -#' under the curve for all the elements of the functional dataset \code{fData}. +#' under the curve for all the elements of the functional dataset +#' \code{fData}. #' #' @seealso \code{\link{area_ordered}}, \code{\link{fData}} #' @@ -222,7 +216,6 @@ max_ordered = function( fData, gData ) #' area_under_curve( fD ) #' #' @export -#' area_under_curve = function( fData) { if( fData$N > 1 ) @@ -235,39 +228,37 @@ area_under_curve = function( fData) } } -#' Area-under-curve order relation between univariate functional data +#' Area-under-curve order relation between univariate functional data #' -#' This function implements an order relation between univariate functional -#' data based on the area-under-curve relation, that is to say a pre-order -#' relation obtained by comparing the area-under-curve of two -#' different functional data. +#' This function implements an order relation between univariate functional data +#' based on the area-under-curve relation, that is to say a pre-order relation +#' obtained by comparing the area-under-curve of two different functional data. #' -#' Given a univariate functional dataset, \eqn{X_1(t), X_2(t), \ldots, X_N(t)} -#' and another functional dataset \eqn{Y_1(t),} \eqn{Y_2(t), \ldots, Y_M(t)} -#' defined over the same compact interval \eqn{I=[a,b]}, the function computes -#' the area-under-curve (namely, the integral) in both the datasets, and checks -#' whether the first ones are lower or equal than the second ones. +#' Given a univariate functional dataset, \eqn{X_1(t), X_2(t), \ldots, X_N(t)} +#' and another functional dataset \eqn{Y_1(t),} \eqn{Y_2(t), \ldots, Y_M(t)} +#' defined over the same compact interval \eqn{I=[a,b]}, the function computes +#' the area-under-curve (namely, the integral) in both the datasets, and checks +#' whether the first ones are lower or equal than the second ones. #' -#' By default the function tries to compare each \eqn{X_i(t)} with the -#' corresponding \eqn{Y_i(t)}, thus assuming \eqn{N=M}, but when either \eqn{N=1} -#' or \eqn{M=1}, the comparison is carried out cycling over the dataset with -#' fewer elements. In all the other cases (\eqn{N\neq M,} and either -#' \eqn{N \neq 1} or \eqn{M \neq 1}) the function stops. +#' By default the function tries to compare each \eqn{X_i(t)} with the +#' corresponding \eqn{Y_i(t)}, thus assuming \eqn{N=M}, but when either +#' \eqn{N=1} or \eqn{M=1}, the comparison is carried out cycling over the +#' dataset with fewer elements. In all the other cases (\eqn{N\neq M,} and +#' either \eqn{N \neq 1} or \eqn{M \neq 1}) the function stops. #' #' @param fData the first univariate functional dataset containing elements to -#' be compared, in form of \code{fData} object. +#' be compared, in form of \code{fData} object. #' @param gData the second univariate functional dataset containing elements to -#' be compared , in form of \code{fData} object. +#' be compared , in form of \code{fData} object. #' -#' @return -#' The function returns a logical vector of length \eqn{\max(N,M)} containing the -#' value of the predicate for all the corresponding elements. +#' @return The function returns a logical vector of length \eqn{\max(N,M)} +#' containing the value of the predicate for all the corresponding elements. #' #' @references #' #' Valencia, D., Romo, J. and Lillo, R. (2015). A Kendall correlation -#' coefficient for functional dependence, -#' \emph{Universidad Carlos III de Madrid technical report}, +#' coefficient for functional dependence, \emph{Universidad Carlos III de Madrid +#' technical report}, #' \code{http://EconPapers.repec.org/RePEc:cte:wsrepe:ws133228}. #' #' @@ -325,37 +316,7 @@ area_ordered = function( fData, gData ) } } -# cor_kendall = function( mfD, ordering = 'max' ) -# { -# if( mfD$L != 2 ) -# { -# stop( ' Error in cor_kendall: only bivariate data are supported for now.') -# } -# -# N = mfD$N -# -# if( ordering == 'area' ) -# { -# count_concordances = function( iObs )( sum( area_ordered( mfD$fDList[[ 1 ]][ iObs, ], -# mfD$fDList[[ 1 ]][ ( iObs + 1 ) : N, ] ) == -# area_ordered( mfD$fDList[[ 2 ]][ iObs, ], -# mfD$fDList[[ 2 ]][ ( iObs + 1 ) : N, ] ) ) ) -# } else if ( ordering == 'max' ) -# { -# count_concordances = function( iObs )( sum( max_ordered( mfD$fDList[[ 1 ]][ iObs, ], -# mfD$fDList[[ 1 ]][ ( iObs + 1 ) : N, ] ) == -# max_ordered( mfD$fDList[[ 2 ]][ iObs, ], -# mfD$fDList[[ 2 ]][ ( iObs + 1 ) : N, ] ) ) ) -# }else -# { -# stop( ' Error in cor_kendall: unsupported ordering relation') -# } -# -# return( ( 2 * sum( sapply( 1 : ( N - 1 ), count_concordances ) ) - ( N * ( N - 1 ) / 2 ) ) / ( N * ( N - 1 ) / 2 ) ) -# } - - -#' Kendall's tau correlation coefficient for bivariate functional data +#' Kendall's tau correlation coefficient for bivariate functional data #' #' This function computes the Kendall's tau correlation coefficient for a #' bivariate functional dataset, with either a max or area-under-curve order @@ -369,21 +330,21 @@ area_ordered = function( fData, gData ) #' #' See the references for more details. #' -#' @param mfD a bivariate functional dataset whose Kendall's tau -#' coefficient must be computed, in form of bivariate \code{mfData} object -#' (\code{mfD$L=2}). +#' @param mfD a bivariate functional dataset whose Kendall's tau coefficient +#' must be computed, in form of bivariate \code{mfData} object +#' (\code{mfD$L=2}). #' @param ordering the ordering relation to use on functional observations, -#' either \code{"max"} for the maximum relation or \code{"area"} for the -#' area under the curve relation (default is \code{"max"}). +#' either \code{"max"} for the maximum relation or \code{"area"} for the area +#' under the curve relation (default is \code{"max"}). #' #' @return The function returns the Kendall's tau correlation coefficient for -#' the bivariate dataset provided with \code{mfData}. +#' the bivariate dataset provided with \code{mfData}. #' #' @references #' #' Valencia, D., Romo, J. and Lillo, R. (2015). A Kendall correlation -#' coefficient for functional dependence, -#' \emph{Universidad Carlos III de Madrid technical report}, +#' coefficient for functional dependence, \emph{Universidad Carlos III de Madrid +#' technical report}, #' \code{http://EconPapers.repec.org/RePEc:cte:wsrepe:ws133228}. #' #' @@ -428,7 +389,7 @@ area_ordered = function( fData, gData ) #' cor_kendall( mfD, ordering = 'area' ) #' #' @seealso \code{\link{mfData}}, \code{\link{area_ordered}}, -#' \code{\link{max_ordered}} +#' \code{\link{max_ordered}} #' #' @export cor_kendall = function( mfD, ordering = 'max' ) @@ -465,28 +426,29 @@ cor_kendall = function( mfD, ordering = 'max' ) #' Spearman's correlation coefficient for multivariate functional data #' #' This function computes the Spearman's correlation coefficient for a -#' multivariate functional dataset, with either a Modified Epigraph Index (MEI) or -#' Modified Hypograph Index (MHI) ranking of univariate elments of data +#' multivariate functional dataset, with either a Modified Epigraph Index (MEI) +#' or Modified Hypograph Index (MHI) ranking of univariate elements of data #' components. #' #' Given a multivariate functional dataset, with first components \eqn{X^1_1(t), -#' X^1_2(t), \ldots, X^1_N(t)}, second components \eqn{X^2_1(t), X^2_2(t), \ldots, -#' X^2_N(t)}, etc., the function exploits either the MEI or MHI to compute the matrix of -#' Spearman's correlation coefficients. Such matrix is symmetrical and has ones on the -#' diagonal. The entry (i, j) represents the Spearman correlation coefficient between -#' curves of component i and j. +#' X^1_2(t), \ldots, X^1_N(t)}, second components \eqn{X^2_1(t), X^2_2(t), +#' \ldots, X^2_N(t)}, etc., the function exploits either the MEI or MHI to +#' compute the matrix of Spearman's correlation coefficients. Such matrix is +#' symmetrical and has ones on the diagonal. The entry (i, j) represents the +#' Spearman correlation coefficient between curves of component i and j. #' #' See the references for more details. #' #' @param mfD a multivariate functional dataset whose Spearman's correlation -#' coefficient must be computed, in form of multivariate \code{mfData} object. +#' coefficient must be computed, in form of multivariate \code{mfData} object. #' @param ordering the ordering relation to use on functional observations, -#' either \code{"MEI"} for MEI or \code{"MHI"} for MHI (default is \code{"MEI"}). +#' either \code{"MEI"} for MEI or \code{"MHI"} for MHI (default is +#' \code{"MEI"}). #' #' @return If the original dataset is bivariate, the function returns only the -#' scalar value of the correlation coefficient for the two components. -#' When the number of components is L >2, it returns the whole matrix of -#' Spearman's correlation coefficients for all the components. +#' scalar value of the correlation coefficient for the two components. When +#' the number of components is L >2, it returns the whole matrix of Spearman's +#' correlation coefficients for all the components. #' #' @references #' @@ -559,7 +521,7 @@ cor_spearman = function( mfD, ordering = 'MEI' ) # rk_2 = MHI( mfD$fDList[[ 2 ]]$values ) } - cor_output = cor(rks, method='pearson') + cor_output = stats::cor(rks, method='pearson') if( mfD$L == 2 ) { @@ -572,54 +534,69 @@ cor_spearman = function( mfD, ordering = 'MEI' ) #' Bootstrap Spearman's correlation coefficient for multivariate functional data #' -#' This function computes the bootstrap estimates of standard error and bias of the Spearman's -#' correlation coefficient for a multivariate functional dataset. +#' This function computes the bootstrap estimates of standard error and bias of +#' the Spearman's correlation coefficient for a multivariate functional dataset. #' -#' Given a multivariate functional dataset \eqn{X_1^(i), \ldots, X_n^(i)}, \eqn{i=0, \ldots, L} -#' defined over the grid \eqn{I = t_0, \ldots, t_P}, having components \eqn{i=1, \ldots, L}, and a -#' chosen ordering strategy (MEI or MHI), the function computes the matrix of Speraman's correlation -#' indexes of the dataset's components, as well as their bias and standard deviation estimates -#' through a specified number of bootstrap iterations (bias and standard error are updated with -#' on-line formulas). +#' Given a multivariate functional dataset \eqn{X_1^(i), \ldots, X_n^(i)}, +#' \eqn{i=0, \ldots, L} defined over the grid \eqn{I = t_0, \ldots, t_P}, having +#' components \eqn{i=1, \ldots, L}, and a chosen ordering strategy (MEI or MHI), +#' the function computes the matrix of Spearman's correlation indices of the +#' dataset components, as well as their bias and standard deviation estimates +#' through a specified number of bootstrap iterations (bias and standard error +#' are updated with on-line formulas). #' #' @param mfD a multivariate functional dataset whose Spearman's correlation -#' coefficient must be computed, in form of multivariate \code{mfData} object. +#' coefficient must be computed, in form of multivariate \code{mfData} object. #' @param ordering the ordering relation to use on functional observations, -#' either \code{"MEI"} for MEI or \code{"MHI"} for MHI (default is \code{"MEI"}). -#' @param bootstrap_iterations the number of bootstrap iterations to be used for estimation of bias and -#' standard error. -#' @param verbose a logical flag specifying whether to log information on the estimation progress. -#' -#' @return a list of three elements: \code{mean}, the mean of the matrix of correlation coefficients; -#' \code{bias}, a matrix containing the estimated bias (mean - point estimate of correlation coefficients); -#' \code{sd}, a matrix containing the estiated standard deviation of the coefficients' matrix. In case -#' the multivariate functional dataset has only two components, the return type is scalar and not matrix. +#' either \code{"MEI"} for MEI or \code{"MHI"} for MHI (default is +#' \code{"MEI"}). +#' @param bootstrap_iterations the number of bootstrap iterations to be used for +#' estimation of bias and standard error. +#' @param verbose a logical flag specifying whether to log information on the +#' estimation progress. +#' +#' @return a list of three elements: \code{mean}, the mean of the matrix of +#' correlation coefficients; \code{bias}, a matrix containing the estimated +#' bias (mean - point estimate of correlation coefficients); \code{sd}, a +#' matrix containing the estimated standard deviation of the coefficients' +#' matrix. In case the multivariate functional dataset has only two +#' components, the return type is scalar and not matrix. #' #' @seealso \code{\link{cor_spearman}}, \code{\link{mfData}} #' #' @examples +#' N <- 200 +#' P <- 100 #' -#' N = 2e2 -#' P = 1e2 -#' grid = seq( 0, 1, length.out = P ) +#' grid <- seq(0, 1, length.out = P) #' -#' # Creating an exponential covariance function to simulate guassian data -#' Cov = exp_cov_function( grid, alpha = 0.3, beta = 0.4 ) +#' # Creating an exponential covariance function to simulate Gaussian data +#' Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) #' -#' # Simulating (independent) gaussian functional data with given center and covariance function +#' # Simulating (independent) Gaussian functional data with given center and covariance function #' -#' Data_1 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) -#' Data_2 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) +#' Data_1 <- generate_gauss_fdata( +#' N = N, +#' centerline = sin(2 * pi * grid), +#' Cov = Cov +#' ) +#' +#' Data_2 <- generate_gauss_fdata( +#' N = N, +#' centerline = sin(2 * pi * grid), +#' Cov = Cov +#' ) #' #' # Using the simulated data as (independent) components of a bivariate functional dataset -#' mfD = mfData( grid, list( Data_1, Data_2 ) ) -#'\dontrun{ -#' cor_spearman_accuracy(mfD, ordering='MEI') +#' mfD <- mfData(grid, list(Data_1, Data_2)) #' -#' cor_spearman_accuracy(mfD, ordering='MHI') -#'} -#' @export +#' \donttest{ +#' # Computes bootstrap estimate of Spearman correlation +#' cor_spearman_accuracy(mfD, ordering = "MEI") +#' cor_spearman_accuracy(mfD, ordering = "MHI") +#' } #' +#' @export cor_spearman_accuracy = function(mfD, ordering='MEI', bootstrap_iterations=1000, verbose=FALSE) { @@ -659,4 +636,3 @@ cor_spearman_accuracy = function(mfD, ordering='MEI', bootstrap_iterations=1000, bias = cor_mean - cor_spearman(mfD, ordering = ordering), sd = sqrt(cor_sqd / ( bootstrap_iterations - 1 )))) } - diff --git a/R/mfD_LBBB.R b/R/data.R similarity index 53% rename from R/mfD_LBBB.R rename to R/data.R index 5126599..f7531d6 100644 --- a/R/mfD_LBBB.R +++ b/R/data.R @@ -1,3 +1,16 @@ +#' ECG trace of healthy subjects +#' +#' A dataset containing the 8-Lead ECG traces of 50 healthy subjects. They can be used to compare the +#' signals of pathological subjects stored in \code{mfD_LBBB} and \code{mfD_RBBB} objects. +#' +#' The 8 leads are, in order, V1, V2, V3, V4, V5, D1 and D2. The signals have been registered and +#' smoothed over an evenly spaced grid of 1024 time points at 1kHz. +#' +#' @format A \code{\link{mfData}} object. +#' +#' +"mfD_healthy" + #' ECG trace of subjects suffering from Left-Bundle-Branch-Block (LBBB) #' #' A dataset containing the 8-Lead ECG traces of 50 subjects suffering from diff --git a/R/depthgram.R b/R/depthgram.R new file mode 100644 index 0000000..737a722 --- /dev/null +++ b/R/depthgram.R @@ -0,0 +1,476 @@ +#' Depthgram for univariate and multivariate functional data sets +#' +#' This function computes the three 'DepthGram' representations from a p-variate +#' functional data set. +#' +#' @param Data A \code{\link[base]{list}} of length `L` (number of components) +#' in which each element is an `N x P` matrix with `N` individuals and `P` +#' time points. Alternatively, it can also be an object of class +#' \code{\link{fData}} or of class \code{\link{mfData}}. +#' @param marginal_outliers A boolean specifying whether the function should +#' return shape and amplitude outliers over each dimension. Defaults to +#' `FALSE`. +#' @param boxplot_factor A numeric value specifying the inflation factor for +#' marginal functional boxplots. This is ignored if `marginal_outliers == +#' FALSE`. Defaults to `1.5`. +#' @param outliergram_factor A numeric value specifying the inflation factor for +#' marginal outliergrams. This is ignored if `marginal_outliers == FALSE`. +#' Defaults to `1.5`. +#' @param ids A character vector specifying labels for individual observations. +#' Defaults to `NULL`, in which case observations will remain unlabelled. +#' +#' @return An object of class `depthgram` which is a list with the following +#' items: +#' +#' - `mbd.mei.d`: vector MBD of the MEI dimension-wise. +#' - `mei.mbd.d`: vector MEI of the MBD dimension-wise. +#' - `mbd.mei.t`: vector MBD of the MEI time-wise. +#' - `mei.mbd.t`: vector MEI of the MEI time-wise. +#' - `mbd.mei.t2`: vector MBD of the MEI time/correlation-wise. +#' - `mei.mbd.t2`: vector MEI of the MBD time/correlation-wise. +#' - `shp.out.det`: detected shape outliers by dimension. +#' - `mag.out.det`: detected magnitude outliers by dimension. +#' - `mbd.d`: matrix `n x p` of MBD dimension-wise. +#' - `mei.d`: matrix `n x p` of MEI dimension-wise. +#' - `mbd.t`: matrix `n x p` of MBD time-wise. +#' - `mei.t`: matrix `n x p` of MEI time-wise. +#' - `mbd.t2`: matrix `n x p` of MBD time/correlation-wise +#' - `mei.t2`: matrix `n x p` of MBD time/correlation-wise. +#' +#' @references +#' Aleman-Gomez, Y., Arribas-Gil, A., Desco, M. Elias-Fernandez, A., and Romo, +#' J. (2021). "Depthgram: Visualizing Outliers in High Dimensional Functional +#' Data with application to Task fMRI data exploration". +#' +#' @export +#' @name depthgram +#' +#' @examples +#' N <- 2e2 +#' P <- 1e3 +#' grid <- seq(0, 1, length.out = P) +#' Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) +#' +#' Data <- list() +#' Data[[1]] <- generate_gauss_fdata( +#' N, +#' centerline = sin(2 * pi * grid), +#' Cov = Cov +#' ) +#' Data[[2]] <- generate_gauss_fdata( +#' N, +#' centerline = sin(2 * pi * grid), +#' Cov = Cov +#' ) +#' names <- paste0("id_", 1:nrow(Data[[1]])) +#' +#' DG1 <- depthgram(Data, marginal_outliers = TRUE, ids = names) +#' +#' fD <- fData(grid, Data[[1]]) +#' DG2 <- depthgram(fD, marginal_outliers = TRUE, ids = names) +#' +#' mfD <- mfData(grid, Data) +#' DG3 <- depthgram(mfD, marginal_outliers = TRUE, ids = names) +depthgram <- function(Data, + marginal_outliers = FALSE, + boxplot_factor = 1.5, + outliergram_factor = 1.5, + ids = NULL) { + UseMethod("depthgram", Data) +} + +#' @rdname depthgram +#' @export +depthgram.default <- function(Data, + marginal_outliers = FALSE, + boxplot_factor = 1.5, + outliergram_factor = 1.5, + ids = NULL) { + p <- length(Data) + n <- nrow(Data[[1]]) + N <- ncol(Data[[1]]) + + if (marginal_outliers) { + a2 <- a0 <- -2 / (n * (n - 1)) + a1 <- 2 * (n + 1) / (n - 1) + } + + ######################### + # Dimension-wise + ######################### + + # n*p matrix with mbd's on each dimension + mbd.d <- array(0, dim = c(n, p)) + # n*p matrix with mbd's on each dimension + mei.d <- array(0, dim = c(n, p)) + # list for marginal magnitude outliers detection + mag.out.det <- list(length = p) + # list for marginal shape outliers detection + shp.out.det <- list(length = p) + n2 <- ceiling(n * 0.5) + + # Vector containing the sign of correlation between mei in each dimension and + # the next one + corr.mei <- vector("numeric", length = p) + corr.mei[1] <- 1 + + #Array with all observation ranks on each time-point/dimension + rmat.mat <- array(0, dim = c(n, N, p)) + # Storing components in which -1 transformation will be applied (since negative + # mei correlation exists) + wp <- c() + + for (i in 1:p) { ### Over dimensions + x <- Data[[i]] + # MBD and MEI computation on dimension i + rmat <- apply(t(x), 1, rank) + rmat.mat[, , i] <- rmat + down <- rmat - 1 + up <- n - rmat + mbd.d[, i] <- (rowSums(up * down) / N + n - 1) / (n * (n - 1) / 2) + mei.d[, i] <- rowSums(up + 1) / (n * N) + # MEI correlation between i and i-1 dimensions + if (i > 1) + corr.mei[i] <- corr.mei[i - 1] * sign(stats::cor(mei.d[, i], mei.d[, i - 1])) + + if (corr.mei[i] == -1) + wp <- c(wp, i) + + # Marginal outlier detection on dimension i: functional boxplot and + # outliergram with factors boxplot_factor and outliergram_factor + # respectively + if (marginal_outliers) { + index <- order(mbd.d[, i], decreasing = TRUE) + center <- x[index[1:n2], ] + inf <- apply(center, 2, min) + sup <- apply(center, 2, max) + dist <- boxplot_factor * (sup - inf) + upper <- sup + dist + lower <- inf - dist + mag.out.det[[i]] <- which(colSums((t(x) <= lower) + (t(x) >= upper)) > 0) + dist <- (a0 + a1 * mei.d[, i] + a2 * n^2 * mei.d[, i]^2) - mbd.d[, i] + q <- stats::quantile(dist, probs = c(0.25, 0.75)) + lim <- outliergram_factor * (q[2] - q[1]) + q[2] + shp.out.det[[i]] <- which(dist > lim) + rm(index, center, inf, sup, dist, upper, lower, q, lim) + } + + rm(x, rmat, down, up) + } + + rm(Data) + + ##### MEI of MBD and MBD of MEI across dimensions + + mei.mbd.d <- MEI(mbd.d) # managing ties + mbd.mei.d <- MBD(mei.d) + + ######################## + # Time-wise + ######################## + + # n*N matrix with mbd's on each time point + mbd.t <- array(0, dim = c(n, N)) + # n*N matrix with mei's on each time point + mei.t <- array(0, dim = c(n, N)) + # n*N matrix with mbd's on each time point for the "corrected" data set + mbd.t2 <- array(0, dim = c(n, N)) + # n*N matrix with mbd's on each time point for the "corrected" data set + mei.t2 <- array(0, dim = c(n, N)) + + for (i in 1:N) { ### Over time points + # Getting observation ranks at time-point i + rmat <- rmat.mat[, i, ] + if (is.null(dim(rmat))) + rmat <- matrix(rmat, ncol = 1) + # MBD and MEI computation on time-point i + down <- rmat - 1 + up <- n - rmat + mbd.t[, i] <- (rowSums(up * down) / p + n - 1) / (n * (n - 1) / 2) + mei.t[, i] <- rowSums(up + 1) / (n * p) + + # MBD and MEI computation on dimension i for the "corrected" data set + if (length(wp) > 0) { + down[, wp] <- n - rmat[, wp] + up[, wp] <- rmat[, wp] - 1 + mbd.t2[, i] <- (rowSums(up * down) / p + n - 1) / (n * (n - 1) / 2) + mei.t2[, i] <- rowSums(up + 1) / (n * p) + } else { + mbd.t2[, i] <- mbd.t[, i] + mei.t2[, i] <- mei.t[, i] + } + + rm(down, rmat, up) + } + + ##### MEI of MBD and MBD of MEI across time-points (original and corrected + ##### data sets) + mei.mbd.t <- MEI(mbd.t) + mei.mbd.t2 <- MEI(mbd.t2) + mbd.mei.t <- MBD(mei.t) + mbd.mei.t2 <- MBD(mei.t2) + + ##### RETURN + res <- list( + mbd.mei.d = mbd.mei.d, mei.mbd.d = mei.mbd.d, + mbd.mei.t = mbd.mei.t, mei.mbd.t = mei.mbd.t, + mbd.mei.t2 = mbd.mei.t2, mei.mbd.t2 = mei.mbd.t2, + shp.out.det = shp.out.det, mag.out.det = mag.out.det, + mbd.d = mbd.d, mei.d = mei.d, + mbd.t = mbd.t, mei.t = mei.t, + mbd.t2 = mbd.t2, mei.t2 = mei.t2 + ) + + if (!is.null(ids)) { + res <- lapply(res, function(.x) { + if (is.list(.x)) { + .x <- lapply(.x, function(.y) { + names(.y) <- ids[.y] + .y + }) + } else if (!is.null(dim(.x))) { + row.names(.x) <- ids + } else if (length(.x) < n) { + names(.x) <- ids[.x] + } else { + names(.x) <- ids + } + + .x + }) + } + + res$corr.mei <- corr.mei + class(res) <- c("depthgram", class(res)) + res +} + +#' @rdname depthgram +#' @export +depthgram.fData <- function(Data, + marginal_outliers = FALSE, + boxplot_factor = 1.5, + outliergram_factor = 1.5, + ids = NULL) { + depthgram( + Data = list(Data$values), + marginal_outliers = marginal_outliers, + boxplot_factor = boxplot_factor, + outliergram_factor = outliergram_factor, + ids = ids + ) +} + +#' @rdname depthgram +#' @export +depthgram.mfData <- function(Data, + marginal_outliers = FALSE, + boxplot_factor = 1.5, + outliergram_factor = 1.5, + ids = NULL) { + depthgram( + Data = toListOfValues(Data), + marginal_outliers = marginal_outliers, + boxplot_factor = boxplot_factor, + outliergram_factor = outliergram_factor, + ids = ids + ) +} + +#' Specialized method to plot 'depthgram' objects +#' +#' This function plots the three 'DepthGram' representations from the output of +#' the \code{\link{depthgram}} function. +#' +#' @param x An object of class `depthgram` as output by the +#' \code{\link{depthgram}} function. +#' @param limits A boolean specifying whether the empirical limits for outlier +#' detection should be drawn. Defaults to `FALSE`. +#' @param ids A character vector specifying labels for individual observations. +#' Defaults to `NULL`, in which case observations will named by their id +#' number in order of appearance. +#' @param print A boolean specifying whether the graphical output should be +#' optimized for printed version. Defaults to `FALSE`. +#' @param plot_title A character string specifying the main title for the plot. +#' Defaults to `""`, which means no title. +#' @param shorten A boolean specifying whether labels must be shorten to 15 +#' characters. Defaults to `TRUE`. +#' @param col Color palette used for the plot. Defaults to `NULL`, in which case +#' a default palette produced by the \code{\link[grDevices]{hcl}} function is +#' used. +#' @param pch Point shape. See \code{\link[plotly]{plotly}} for more details. +#' Defaults to `19`. +#' @param sp Point size. See \code{\link[plotly]{plotly}} for more details. +#' Defaults to `2`. +#' @param st Label size. See \code{\link[plotly]{plotly}} for more details. +#' Defaults to `4`. +#' @param sa Axis title sizes. See \code{\link[plotly]{plotly}} for more +#' details. Defaults to `10`. +#' @param text_labels A character vector specifying the labels for the +#' individuals. It is overridden if `limits = TRUE`, for which only outliers +#' labels are shown. See \code{\link[plotly]{plotly}} for more details. +#' Defaults to `""`. +#' @param ... Other arguments to be passed to the base \code{\link[base]{plot}} +#' function. Unused. +#' +#' @return A list with the following items: + +#' - `p`: list with all the interactive (plotly) depthGram plots; +#' - `out`: outliers detected; +#' - `colors`: used colors for plotting. +#' +#' @references +#' Aleman-Gomez, Y., Arribas-Gil, A., Desco, M. Elias-Fernandez, A., and Romo, +#' J. (2021). "Depthgram: Visualizing Outliers in High Dimensional Functional +#' Data with application to Task fMRI data exploration". +#' +#' @export +#' +#' @examples +#' N <- 50 +#' P <- 50 +#' grid <- seq(0, 1, length.out = P) +#' Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) +#' +#' Data <- list() +#' Data[[1]] <- generate_gauss_fdata( +#' N, +#' centerline = sin(2 * pi * grid), +#' Cov = Cov +#' ) +#' Data[[2]] <- generate_gauss_fdata( +#' N, +#' centerline = sin(2 * pi * grid), +#' Cov = Cov +#' ) +#' names <- paste0("id_", 1:nrow(Data[[1]])) +#' DG <- depthgram(Data, marginal_outliers = TRUE, ids = names) +#' plot(DG) +plot.depthgram <- function(x, + limits = FALSE, + ids = NULL, + print = FALSE, + plot_title = "", + shorten = TRUE, + col = NULL, + pch = 19, sp = 2, st = 4, sa = 10, + text_labels = "", + ...) { + n <- length(x$mei.mbd.d) + + type <- c( + "Dimensions DepthGram", + "Time DepthGram", + "Time/Correlation DepthGram" + ) + + if (is.null(ids)) ids <- as.character(1:n) + + x <- data.frame( + ID = rep(ids, 3), + mei.mbd = c(x$mei.mbd.d, x$mei.mbd.t, x$mei.mbd.t2), + mbd.mei = c(x$mbd.mei.d, x$mbd.mei.t, x$mbd.mei.t2), + type = rep(type, each = n) + ) + + if (is.null(col)) { + hues <- seq(15, 375, length = n + 1) + color <- grDevices::hcl(h = hues, l = 65, c = 100)[1:n] + } else + color <- col + + out <- NULL + + if (limits) { + P2 <- function(x, n) { + a0 <- 2 / n + a2 <- -n / (2 * (n - 1)) + a0 + x + a2*x^2 + } + meis <- seq(0, 1, n) + x$meis <- rep(meis, 3) + x$par <- P2(x$meis, n) + distp <- x$mbd.mei - P2(1 - x$mei.mbd, n) + q3 <- stats::quantile(distp, 0.75) + q1 <- stats::quantile(distp, 0.25) + x$par2 <- x$par + q3 + 1.5 * (q3 - q1) + out <- unique(which(distp > q3 + 1.5 * (q3 - q1)) %% n) + pch <- rep(1, n) # empty circle + pch[out] <- 19 # solid circle + text_labels <- rep("", n) + text_labels[out] <- ids[out] + + if (is.null(col)) { + hues <- seq(15, 375, length = length(out) + 1) + color.out <- grDevices::hcl(h = hues, l = 65, c = 100)[1:length(out)] + color <- rep(8, n) + color[out] <- color.out + } + } + + if (print) { + sp <- 3 + st <- 5 + sa <- 14 + } + + plots <- list() + + for (i in 1:3) { + dat <- x[which(x$type == type[i]), ] + + plots[[i]] <- ggplot(dat, aes(x = 1 - .data$mei.mbd, y = .data$mbd.mei)) + + geom_point(aes(group = .data$ID), color = color, size = sp, shape = pch) + + geom_text( + label = text_labels, + color = color, + hjust = -0.15, + vjust = -0.15, + size = st + ) + + xlim(c(0, 1.005)) + + ylim(c(0, 0.525)) + + theme_minimal() + + theme( + axis.title = element_text(size = sa), + axis.text = element_text(size = sa - 2), + title = element_text(size = sa) + ) + + if (limits) { + plots[[i]] <- plots[[i]] + + geom_line(aes(x = .data$meis, y = .data$par) , col = 1, na.rm = TRUE) + + geom_line(aes(x = .data$meis, y = .data$par2), col = 1, na.rm = TRUE, lty = 2) + } + + if (i == 1) + pt <- plot_title + else + pt <- "" + + plots[[i]] <- plots[[i]] + + facet_wrap(~ .data$type) + + ggtitle(pt) + } + + p <- plots %>% + plotly::subplot(shareY = TRUE, shareX =TRUE) %>% + plotly::layout( + title = plot_title, + yaxis = list(title = "MBD(MEI)"), + xaxis = list(title = "1-MEI(MBD)") + ) + + print(p) + + list( + p = list( + dimDG = plots[[1]], + timeDG = plots[[2]], + corrDG = plots[[3]], + fullDG = p + ), + out = out, + color = color + ) +} diff --git a/R/fData.R b/R/fData.R index 20d3c60..895c599 100644 --- a/R/fData.R +++ b/R/fData.R @@ -1,4 +1,3 @@ - #' \code{S3} Class for univariate functional datasets. #' #' This function implements a constructor for elements of \code{S3} class @@ -17,7 +16,7 @@ #' @param grid the evenly spaced grid over which the functional observations are #' measured. It must be a numeric vector of length \code{P}. #' @param values the values of the observations in the functional dataset, -#' prodived in form of a 2D data structure (e.g. matrix or array) having as +#' provided in form of a 2D data structure (e.g. matrix or array) having as #' rows the observations and as columns their measurements over the 1D grid of #' length \code{P} specified in \code{grid}. #' @@ -128,11 +127,11 @@ append_fData = function(fD1, fD2) return(fData(grid, values = rbind(fD1$values, fD2$values))) } -#' Specialised method to plot \code{fData} objects +#' Specialized method to plot \code{fData} objects #' #' This function performs the plot of a functional univariate dataset stored in #' an object of class \code{fData}. It is able to accept all the usual -#' customisable graphical parameters, otherwise it will use the default ones. +#' customizable graphical parameters, otherwise it will use the default ones. #' #' @param x the univariate functional dataset in form of \code{fData} object. #' @param ... additional graphical parameters to be used in plotting functions @@ -176,7 +175,7 @@ plot.fData = function( x, ... ) xlab = '', ylab = '', main = '', ... ) { - matplot( seq( x$t0, x$tP, length.out = x$P ), + graphics::matplot( seq( x$t0, x$tP, length.out = x$P ), t( x$values ), type = type, lty = lty, col = col, xlab = xlab, ylab = ylab, main = main, ... ) } @@ -212,7 +211,7 @@ plot.fData = function( x, ... ) #' \item{"\code{t0}"}{: the starting point of the 1D grid;} #' \item{"\code{tP}"}{: the ending point of the 1D grid;} #' \item{"\code{fDList}"}{: the list of \code{fData} objects representing the -#' \code{L} components as corresponding unviariate functional datasets.} +#' \code{L} components as corresponding univariate functional datasets.} #' } #' #' @seealso \code{\link{fData}}, \code{\link{generate_gauss_fdata}}, @@ -339,11 +338,11 @@ append_mfData = function(mfD1, mfD2) function(id) append_fData(mfD1$fDList[[id]], mfD2$fDList[[id]])))) } -#' Specialised method to plot \code{mfData} objects +#' Specialized method to plot \code{mfData} objects #' #' This function performs the plot of a functional multivariate dataset stored #' in an object of class \code{mfData}. It is able to accept all the usual -#' customisable graphical parameters, otherwise it will use the default ones. +#' customizable graphical parameters, otherwise it will use the default ones. #' #' The current active graphical device is split into a number of sub-figures, #' each one meant to contain the plot of the corresponding dimension of the @@ -406,13 +405,14 @@ plot.mfData = function( x, ... ) xlab = NULL, ylab = NULL, main = NULL, add = FALSE, ... ) { - - if( add == FALSE ) + if (add == FALSE) { - mfrow_rows = floor( sqrt( x$L ) ) - mfrow_cols = ceiling( x$L / floor( sqrt( x$L ) ) ) + mfrow_rows <- floor(sqrt(x$L)) + mfrow_cols <- ceiling(x$L / floor(sqrt(x$L))) - par( mfrow = c( mfrow_rows, mfrow_cols ) ) + oldpar <- graphics::par(mfrow = c(1, 1)) + on.exit(graphics::par(oldpar)) + graphics::par(mfrow = c(mfrow_rows, mfrow_cols)) } if( ! is.null( ylab ) ) @@ -518,7 +518,7 @@ NULL #' @export "+.fData" = function( fD, A ) { - if( class( A ) == 'fData' ) + if( 'fData' %in% class( A ) ) { if( fD$t0 != A$t0 || fD$tP != A$tP || fD$h != A$h || fD$P != A$P ) { @@ -574,7 +574,7 @@ NULL #' "-.fData" = function( fD, A ) { - if( class( A ) == 'fData' ) + if( 'fData' %in% class( A ) ) { if( fD$t0 != A$t0 || fD$tP != A$tP || fD$h != A$h || fD$P != A$P ) { @@ -779,14 +779,23 @@ mean.fData = function( x, ... ) #' ) #' #' # Graphical representation of the mean -#' par( mfrow = c( 1, 3 ) ) +#' oldpar <- par(mfrow = c(1, 1)) +#' par(mfrow = c(1, L)) #' -#' for( iL in 1 : L ) +#' for(iL in 1:L) #' { -#' plot( mfD$fDList[[ 1 ]] ) -#' plot( mean( mfD )$fDList[[ 1 ]], col = 'black', -#' lwd = 2, lty = 2, add = TRUE ) +#' plot(mfD$fDList[[iL]]) +#' plot( +#' mean(mfD)$fDList[[iL]], +#' col = 'black', +#' lwd = 2, +#' lty = 2, +#' add = TRUE +#' ) #' } +#' +#' par(oldpar) +#' #' @export mean.mfData = function( x, ... ) { @@ -848,7 +857,7 @@ mean.mfData = function( x, ... ) #' the cross-covariance function of the two datasets is returned;} #' \item{if \code{X} is of class \code{mfData} and \code{Y} is \code{NULL}, #' the upper-triangular blocks of the covariance function of \code{X} -#' are returned (in form of list and by row, i.e. in the squence 1_1, 1_2, ..., +#' are returned (in form of list and by row, i.e. in the sequence 1_1, 1_2, ..., #' 1_L, 2_2, ... - have a look at the labels of the list with \code{str});} #' \item{if \code{X} is of class \code{mfData} and \code{Y} is of #' class \code{fData}, @@ -858,7 +867,7 @@ mean.mfData = function( x, ... ) #' class \code{mfData}, #' the upper-triangular blocks of the cross-covariance of \code{X}'s and #' \code{Y}'s components are returned (in form of list and by row, i.e. in the -#' squence 1_1, 1_2, ..., 1_L, 2_2, ... - have a look at the labels +#' sequence 1_1, 1_2, ..., 1_L, 2_2, ... - have a look at the labels #' of the list with \code{str}));}} #' #' In any case, the return type is either an instance of the \code{S3} class \code{Cov} @@ -935,8 +944,6 @@ cov_fun = function( X, Y = NULL ) } #' @rdname cov_fun -#' -#' @importFrom stats cov #' @export cov_fun.fData = function( X, Y = NULL ) { @@ -1062,10 +1069,10 @@ cov_fun.mfData = function( X, Y = NULL ) } } -#' Specialised method to plot \code{Cov} objects +#' Specialized method to plot \code{Cov} objects #' #' This function performs the plot of an object of class \code{Cov}, i.e. a -#' covariance or cross-covaraince function. +#' covariance or cross-covariance function. #' #' @details #' It builds above the function \code{graphics::image}, therefore any additional @@ -1098,7 +1105,6 @@ cov_fun.mfData = function( X, Y = NULL ) #' #' plot( cov_fun( fD1 ), main = 'Covariance function', xlab = 'time', ylab = 'time' ) #' -#' @importFrom graphics image #' @export plot.Cov = function( x, ... ) { @@ -1213,14 +1219,23 @@ median_fData = function( fData, type = 'MBD', ... ) #' med_mfD = median_mfData( mfD, type = 'multiMBD', weights = 'uniform' ) #' #' # Graphical representation of the mean -#' par( mfrow = c( 1, 3 ) ) +#' oldpar <- par(mfrow = c(1, 1)) +#' par(mfrow = c(1, L)) #' -#' for( iL in 1 : L ) +#' for(iL in 1:L) #' { -#' plot( mfD$fDList[[ 1 ]] ) -#' plot( med_mfD$fDList[[ 1 ]], col = 'black', -#' lwd = 2, lty = 2, add = TRUE ) +#' plot(mfD$fDList[[iL]]) +#' plot( +#' med_mfD$fDList[[iL]], +#' col = 'black', +#' lwd = 2, +#' lty = 2, +#' add = TRUE +#' ) #' } +#' +#' par(oldpar) +#' #' @export median_mfData = function( mfData, type = 'multiMBD', ... ) { @@ -1232,7 +1247,7 @@ median_mfData = function( mfData, type = 'multiMBD', ... ) which.max( Depths ), ) ) ) } -#' Operator \code{sub-.fData} to subset \code{fData} obejcts +#' Operator \code{sub-.fData} to subset \code{fData} objects #' #' This method provides an easy and natural way to subset a functional dataset #' stored in a \code{fData} object, without having to deal with the inner @@ -1276,22 +1291,25 @@ median_mfData = function( mfData, type = 'multiMBD', ... ) #' Cov = C ) ) #' #' dev.new() -#' par( mfrow = c( 2, 2 ) ) +#' oldpar <- par(mfrow = c(1, 1)) +#' par(mfrow = c(2, 2)) #' #' # Original data -#' plot( fD ) +#' plot(fD) #' #' # Subsetting observations -#' plot( fD[ c(1,2,3), , as_fData = TRUE ] ) +#' plot(fD[c(1, 2, 3), , as_fData = TRUE]) #' #' # Subsetting measurements -#' plot( fD[ , 1 : 30 ] ) +#' plot(fD[, 1:30]) #' #' # Subsetting both observations and measurements -#' plot( fD[ 1 : 10, 50 : P ] ) +#' plot(fD[1:10, 50:P]) +#' +#' par(oldpar) #' #' # Subsetting both observations and measurements but returning a matrix -#' fD[ 1 : 10, 50 : P, as_fData = FALSE ] +#' fD[1:10, 50:P, as_fData = FALSE] #' #' @export "[.fData" = function( fD, i, j, as_fData = TRUE ) @@ -1338,7 +1356,7 @@ median_mfData = function( mfData, type = 'multiMBD', ... ) } } -#' Operator \code{sub-.mfData} to subset \code{mfData} obejcts +#' Operator \code{sub-.mfData} to subset \code{mfData} objects #' #' This method provides an easy and natural way to subset a multivariate #' functional dataset stored in a \code{mfData} object, without having to @@ -1450,11 +1468,11 @@ toListOfValues = function( mfData ) #' This function operates on a univariate functional dataset and transforms its #' observations unfolding their values and turning them into monotone functions. #' -#' Each function of the \code{fData} object is transformed into a nonmonotone +#' Each function of the \code{fData} object is transformed into a non-monotone #' function into a monotone function by ``unfolding'' it at any of its maxima. #' For more details about the definition of the transform, see the reference. #' -#' @param fData the unvariate functional dataset in form of \code{fData} object. +#' @param fData the univariate functional dataset in form of \code{fData} object. #' #' @return The function returns an \code{fData} object whose observations are #' the unfolded version of the corresponding observations in the argument @@ -1482,9 +1500,13 @@ toListOfValues = function( mfData ) #' fD_unfold = unfold( fD ) #' #' dev.new() -#' par( mfrow = c( 1, 2 ) ) -#' plot( fD, main = 'Original data' ) -#' plot( fD_unfold, main = 'Unfolded data' ) +#' oldpar <- par(mfrow = c(1, 1)) +#' par(mfrow = c(1, 2)) +#' +#' plot(fD, main = 'Original data') +#' plot(fD_unfold, main = 'Unfolded data') +#' +#' par(oldpar) #' #' @export unfold = function( fData ) @@ -1558,7 +1580,9 @@ unfold = function( fData ) #' fD_warped = warp( fD, wfD ) #' #' dev.new() -#' par( mfrow = c( 1, 3 ) ) +#' oldpar <- par(mfrow = c(1, 1)) +#' par(mfrow = c(1, 3)) +#' #' plot( fD, #' main = 'Unregistered functions', xlab = 'actual grid', ylab = 'values' ) #' plot( wfD, @@ -1568,7 +1592,7 @@ unfold = function( fData ) #' main = 'Warped functions', xlab = 'registered grid', #' ylab = 'values' ) #' -#' @importFrom stats approx +#' par(oldpar) #' #' @export warp = function( fData, warpings ) @@ -1598,11 +1622,13 @@ observations' ) length.out = warpings$P ), t( sapply( 1 : fData$N, function( i )( - approx( time_grid, - fData$values[ i, ], - xout = warpings$values[ i, ], - yright = fData$values[ i, fData$P ], - yleft = fData$values[ i, 1 ] )$y ) ) ) ) ) + stats::approx( + time_grid, + fData$values[i,], + xout = warpings$values[i,], + yright = fData$values[i, fData$P], + yleft = fData$values[i, 1] + )$y ) ) ) ) ) } diff --git a/R/fbplot.R b/R/fbplot.R index bdea069..fad8542 100644 --- a/R/fbplot.R +++ b/R/fbplot.R @@ -5,97 +5,97 @@ #' #' @section Adjustment: #' -#' In the \bold{univariate functional case}, when the adjustment option is selected, -#' the value of \eqn{F} is optimised for the univariate functional dataset -#' provided with \code{Data}. +#' In the \bold{univariate functional case}, when the adjustment option is +#' selected, the value of \eqn{F} is optimized for the univariate functional +#' dataset provided with \code{Data}. #' #' In practice, a number \code{adjust$N_trials} of times a synthetic population #' (of size \code{adjust$tiral_size} with the same covariance (robustly #' estimated from data) and centerline as \code{fData} is simulated without -#' outliers and each time an optimised value \eqn{F_i} is computed so that a +#' outliers and each time an optimized value \eqn{F_i} is computed so that a #' given proportion (\code{adjust$TPR}) of observations is flagged as outliers. #' The final value of \code{F} for the functional boxplot is determined as an -#' average of \eqn{F_1, F_2, \ldots, F_{N_{trials}}}. -#' At each time step the optimisation problem is solved using -#' \code{stats::uniroot} (Brent's method. +#' average of \eqn{F_1, F_2, \dots, F_{N_{trials}}}. At each time step the +#' optimization problem is solved using \code{stats::uniroot} (Brent's method). #' -#' -#' @param Data the univariate or multivariate functional dataset whose functional -#' boxplot must be determined, in form of \code{fData} or \code{mfData} object. +#' @param Data the univariate or multivariate functional dataset whose +#' functional boxplot must be determined, in form of \code{fData} or +#' \code{mfData} object. #' @param Depths either a vector containing the depths for each element of the -#' dataset, or: -#' \itemize{ -#' \item{"\emph{univariate case}"}{: a string containing the name of the method -#' you want to use to compute it. The default is \code{'MBD'}}; -#' \item{"\emph{multivariate case}"}{: a list with elements \code{def}, -#' containing the name of the depth notion to be used to compute depths -#' (\code{BD} or \code{MBD}), and \code{weights}, containing the value -#' of parameter \code{weights} to be passed to the depth function. Default is -#' \code{list( def = 'MBD', weights = 'uniform' ) }. } -#' } +#' dataset, or: +#' +#' * \emph{univariate case}: a string containing the name of the method you +#' want to use to compute it. The default is \code{'MBD'}. +#' * \emph{multivariate case}: a list with elements \code{def}, containing the +#' name of the depth notion to be used to compute depths (\code{BD} or +#' \code{MBD}), and \code{weights}, containing the value of parameter +#' \code{weights} to be passed to the depth function. Default is +#' \code{list(def = 'MBD', weights = 'uniform')}. +#' #' In both cases the name of the functions to compute depths must be available #' in the caller's environment. -#' @param Fvalue the value of the inflation factor \eqn{F}, default is -#' \code{F = 1.5}. +#' @param Fvalue the value of the inflation factor \eqn{F}, default is \code{F = +#' 1.5}. #' @param adjust either \code{FALSE} if you would like the default value for the -#' inflation factor, \eqn{F = 1.5}, to be used, or (for now \bold{only in the -#' univariate functional case}) a list specifying the parameters required by -#' the adjustment: -#' \itemize{ -#' \item{"\code{N_trials}"}{: the number of repetitions of the adujustment -#' procedure based on the simulation of a gaussisan population of functional -#' data, each one producing an adjusted value of \eqn{F}, which will lead -#' to the averaged adjusted value \eqn{\bar{F}}. Default is 20;} -#' \item{"\code{trial_size}"}{: the number of elements in the gaussian -#' population of functional data that will be simulated at each repetition of -#' the adjustment procedure. Default is 8 * \code{Data$N};} -#' \item{"\code{TPR}"}{: the True Positive Rate of outliers, i.e. the proportion -#' of observations in a dataset without amplitude outliers that have to be -#' considered outliers. Default is \code{2 * pnorm( 4 * qnorm( 0.25 ) )};} -#' \item{"\code{F_min}"}{: the minimum value of \eqn{F}, defining the left -#' boundary for the optimisation problem aimed at finding, for a given dataset -#' of simulated gaussian data associated to \code{Data}, the optimal value of -#' \eqn{F}. Default is 0.5;} -#' \item{"\code{F_max}"}{: the maximum value of \eqn{F}, defining the right -#' boundary for the optimisation problem aimed at finding, for a given dataset -#' of simulated gaussian data associated to \code{Data}, the optimal value of -#' \eqn{F}. Default is 5;} -#' \item{"\code{tol}"}{: the tolerance to be used in the optimisation problem -#' aimed at finding, for a given dataset of simulated gaussian data associated -#' to \code{Data}, the optimal value of \eqn{F}. Default is \code{1e-3};} -#' \item{"\code{maxiter}"}{: the maximum number of iterations to solve the -#' optimisation problem aimed at finding, for a given dataset of simulated -#' gaussian data associated to \code{Data}, the optimal value of \eqn{F}. -#' Default is \code{100};} -#' \item{"\code{VERBOSE}"}{: a parameter controlling the verbosity of the -#' adjustment process;} -#' } -#' @param display either a logical value indicating wether you want the -#' outliergram to be displayed, or the number of the graphical device -#' where you want the outliergram to be displayed. +#' inflation factor, \eqn{F = 1.5}, to be used, or (for now \bold{only in the +#' univariate functional case}) a list specifying the parameters required by +#' the adjustment: +#' +#' * \code{N_trials}: the number of repetitions of the adjustment procedure +#' based on the simulation of a gaussian population of functional data, each +#' one producing an adjusted value of \eqn{F}, which will lead to the averaged +#' adjusted value \eqn{\bar{F}}. Default is 20. +#' * \code{trial_size}: the number of elements in the gaussian population of +#' functional data that will be simulated at each repetition of the adjustment +#' procedure. Default is 8 * \code{Data$N}. +#' * \code{TPR}: the True Positive Rate of outliers, i.e. the proportion of +#' observations in a dataset without amplitude outliers that have to be +#' considered outliers. Default is \code{2 * pnorm(4 * qnorm(0.25))}. +#' * \code{F_min}: the minimum value of \eqn{F}, defining the left boundary +#' for the optimization problem aimed at finding, for a given dataset of +#' simulated gaussian data associated to \code{Data}, the optimal value of +#' \eqn{F}. Default is 0.5. +#' * \code{F_max}: the maximum value of \eqn{F}, defining the right boundary +#' for the optimization problem aimed at finding, for a given dataset of +#' simulated gaussian data associated to \code{Data}, the optimal value of +#' \eqn{F}. Default is 5. +#' * \code{tol}: the tolerance to be used in the optimization problem aimed at +#' finding, for a given dataset of simulated gaussian data associated to +#' \code{Data}, the optimal value of \eqn{F}. Default is \code{1e-3}. +#' * \code{maxiter}: the maximum number of iterations to solve the +#' optimization problem aimed at finding, for a given dataset of simulated +#' gaussian data associated to \code{Data}, the optimal value of \eqn{F}. +#' Default is \code{100}. +#' * \code{VERBOSE}: a parameter controlling the verbosity of the adjustment +#' process. +#' +#' @param display either a logical value indicating whether you want the +#' functional boxplot to be displayed, or the number of the graphical device +#' where you want the functional boxplot to be displayed. #' @param xlab the label to use on the x axis when displaying the functional -#' boxplot. -#' @param ylab the label (or list of labels for the multivariate functional case) -#' to use on the y axis when displaying the functional boxplot. +#' boxplot. +#' @param ylab the label (or list of labels for the multivariate functional +#' case) to use on the y axis when displaying the functional boxplot. #' @param main the main title (or list of titles for the multivariate functional -#' case) to be used when displaying the functional boxplot. +#' case) to be used when displaying the functional boxplot. #' @param ... additional graphical parameters to be used in plotting functions. #' -#' @return -#' Even when used in graphical way to plot the functional boxplot, the function -#' returns a list of three elements: the first, \code{Depths}, contains the depths -#' of each element of the functional dataset; the second, \code{Fvalue}, is the -#' value of F used to obtain the outliers, and the third, \code{ID_out}, contains -#' the vector of indices of dataset's elements flagged as outliers (if any). -#' -#' @references +#' @return Even when used in graphical way to plot the functional boxplot, the +#' function returns a list of three elements: #' +#' * \code{Depths}: contains the depths of each element of the functional +#' dataset. +#' * \code{Fvalue}: is the value of F used to obtain the outliers. +#' * \code{ID_out}: contains the vector of indices of dataset elements flagged +#' as outliers (if any). #' -#' Sun, Y., & Genton, M. G. (2012). Functional boxplots. Journal of -#' Computational and Graphical Statistics. +#' @references #' -#' Sun, Y., & Genton, M. G. (2012). Adjusted functional boxplots for spatio- -#' temporal data visualization and outlier detection. Environmetrics, 23(1), 54-64. +#' 1. Sun, Y., & Genton, M. G. (2012). Functional boxplots. Journal of +#' Computational and Graphical Statistics. +#' 1. Sun, Y., & Genton, M. G. (2012). Adjusted functional boxplots for +#' spatio-temporal data visualization and outlier detection. Environmetrics, +#' 23(1), 54-64. #' #' @examples #' @@ -119,7 +119,9 @@ #' fD = fData( grid, D ) #' #' dev.new() -#' par( mfrow = c(1,3) ) +#' oldpar <- par(mfrow = c(1, 1)) +#' par(mfrow = c(1, 3)) +#' #' plot( fD, lwd = 2, main = 'Functional dataset', #' xlab = 'time', ylab = 'values' ) #' @@ -127,6 +129,8 @@ #' #' boxplot(fD$values[,1], ylim = range(fD$values), main = 'Boxplot of functional dataset at t_0 ' ) #' +#' par(oldpar) +#' #' # UNIVARIATE FUNCTIONAL BOXPLOT - WITH ADJUSTMENT #' #' @@ -145,7 +149,7 @@ #' fD = fData( grid, Data ) #' #' dev.new() -#' \dontrun{ +#' \donttest{ #' fbplot( fD, adjust = list( N_trials = 10, #' trial_size = 5 * N, #' VERBOSE = TRUE ), @@ -188,7 +192,6 @@ #' \code{\link{mfData}}, \code{\link{multiMBD}}, \code{\link{multiBD}} #' #' @export -#' fbplot = function( Data, Depths = 'MBD', Fvalue = 1.5, @@ -203,11 +206,7 @@ fbplot = function( Data, } #' @rdname fbplot -#' -#' @importFrom stats quantile -#' #' @export -#' fbplot.fData = function( Data, Depths = 'MBD', Fvalue = 1.5, @@ -221,7 +220,7 @@ fbplot.fData = function( Data, # Checking if depths have already been provided or must be computed if( is.character( Depths ) ) { - # Nice trick to encapsulate the information on the desired definiton of + # Nice trick to encapsulate the information on the desired definition of # depth inside the vector that supposedly should contain depth values Depths_spec = Depths @@ -264,7 +263,7 @@ fbplot.fData = function( Data, adjust$trial_size ) TPR = ifelse( is.null( adjust$TPR ), - 2 * pnorm( 4 * qnorm( 0.25 ) ), + 2 * stats::pnorm( 4 * stats::qnorm( 0.25 ) ), adjust$TPR ) F_min = ifelse( is.null( adjust$F_min ), @@ -316,16 +315,16 @@ fbplot.fData = function( Data, if( VERBOSE > 0 ) { - cat( ' * * * * beginning optimisation\n' ) + cat( ' * * * * beginning optimization\n' ) } - opt = uniroot( cost_functional, + opt = stats::uniroot( cost_functional, interval = c( F_min, F_max ), tol = tol, maxiter = maxiter ) if( VERBOSE > 0 ) { - cat( ' * * * * optimisation finished.\n') + cat( ' * * * * optimization finished.\n') } Fvalues[ iTrial ] = opt$root @@ -341,7 +340,7 @@ fbplot.fData = function( Data, # Plotting part if( is.numeric( display ) ) { - dev.set( display ) + grDevices::dev.set( display ) } if( ! display == FALSE ) @@ -374,7 +373,7 @@ fbplot.fData = function( Data, if( length( ID_out ) > 0 ) { # Plotting non-outlying data - matplot( time_grid, + graphics::matplot( time_grid, t( Data$values[ - ID_out, ] ), lty = 1, type = 'l', col = col_non_outlying, ylim = range( Data$values ), @@ -385,7 +384,7 @@ fbplot.fData = function( Data, min_envelope_limit = apply( Data$values[ - ID_out, ], 2, min ) } else { # Plotting all data - matplot( time_grid, + graphics::matplot( time_grid, t( Data$values ), lty = 1, type = 'l', col = col_non_outlying, ylim = range( Data$values ), @@ -399,29 +398,29 @@ fbplot.fData = function( Data, # Filling in the central envelope - polygon( c(time_grid, rev( time_grid) ), + graphics::polygon( c(time_grid, rev( time_grid) ), c( out$min_envelope_central, rev( out$max_envelope_central ) ), col = col_envelope, border = NA) - lines( time_grid, out$max_envelope_central, lty = 1, col = col_envelope, lwd = 3 ) - lines( time_grid, out$min_envelope_central, lty = 1, col = col_envelope, lwd = 3 ) + graphics::lines( time_grid, out$max_envelope_central, lty = 1, col = col_envelope, lwd = 3 ) + graphics::lines( time_grid, out$min_envelope_central, lty = 1, col = col_envelope, lwd = 3 ) # Plotting the sample median - lines( time_grid, Data$values[ which.max( Depths ), ], lty = 1, type = 'l', + graphics::lines( time_grid, Data$values[ which.max( Depths ), ], lty = 1, type = 'l', col = col_center, lwd = 3) - lines( time_grid, max_envelope_limit, lty = 1, + graphics::lines( time_grid, max_envelope_limit, lty = 1, col = col_fence_structure, lwd = 3 ) - lines( time_grid, min_envelope_limit, lty = 1, + graphics::lines( time_grid, min_envelope_limit, lty = 1, col = col_fence_structure, lwd = 3 ) # Plotting vertical whiskers half.time_grid = which.min( abs( time_grid - 0.5 ) ) - lines( c( time_grid[ half.time_grid ], time_grid[ half.time_grid ] ), + graphics::lines( c( time_grid[ half.time_grid ], time_grid[ half.time_grid ] ), c( out$max_envelope_central[ half.time_grid ], max_envelope_limit[ half.time_grid ] ), lty = 1, col = col_fence_structure, lwd = 3 ) - lines( c( time_grid[ half.time_grid ], time_grid[ half.time_grid ] ), + graphics::lines( c( time_grid[ half.time_grid ], time_grid[ half.time_grid ] ), c( out$min_envelope_central[ half.time_grid ], min_envelope_limit[ half.time_grid ] ), lty = 1, col = col_fence_structure, lwd = 3 ) @@ -429,7 +428,7 @@ fbplot.fData = function( Data, # Plotting outlying data if( length( ID_out ) > 0 ) { - matplot( time_grid, t( toRowMatrixForm( Data$values[ ID_out, ] ) ), + graphics::matplot( time_grid, t( toRowMatrixForm( Data$values[ ID_out, ] ) ), lty = 1, type = 'l', col = col_outlying, lwd = 3, add = T ) } } @@ -457,7 +456,7 @@ fbplot.fData = function( Data, Data_center = Data[ which.max( Depths ), ] - id_central_region = which( Depths >= quantile( Depths, prob = 0.5 ) ) + id_central_region = which( Depths >= stats::quantile( Depths, prob = 0.5 ) ) max_envelope_central = apply( Data[ id_central_region, ], 2, max ) min_envelope_central = apply( Data[ id_central_region, ], 2, min ) @@ -530,7 +529,7 @@ provided in the multivariate version of the functional boxplot' ) if( is.numeric( display ) ) { - dev.set( display ) + grDevices::dev.set( display ) } if( ! display == FALSE ) @@ -567,7 +566,9 @@ provided in the multivariate version of the functional boxplot' ) mfrow_rows = ceiling( Data$L / 2 ) mfrow_cols = 2 - par( mfrow = c( mfrow_rows, mfrow_cols ) ) + oldpar <- graphics::par(mfrow = c(1, 1)) + on.exit(graphics::par(oldpar)) + graphics::par(mfrow = c(mfrow_rows, mfrow_cols)) # Creating color palettes col_non_outlying = scales::hue_pal( h = c( 180, 270 ), @@ -599,7 +600,7 @@ provided in the multivariate version of the functional boxplot' ) if( length( ID_out ) > 0 ) { # Plotting non-outlying data - matplot( time_grid, + graphics::matplot( time_grid, t( Data_curr[ - ID_out, ] ), lty = 1, type = 'l', col = col_non_outlying, ylim = range( Data_curr ), @@ -612,7 +613,7 @@ provided in the multivariate version of the functional boxplot' ) min_envelope_limit = apply( Data_curr[ - ID_out, ], 2, min ) } else { # Plotting all data - matplot( time_grid, + graphics::matplot( time_grid, t( Data_curr ), lty = 1, type = 'l', col = col_non_outlying, ylim = range( Data_curr ), @@ -625,32 +626,32 @@ provided in the multivariate version of the functional boxplot' ) # Filling in the central envelope - polygon( c(time_grid, rev( time_grid) ), + graphics::polygon( c(time_grid, rev( time_grid) ), c( as.numeric( out$min_envelope_central[ iL, ] ), rev( as.numeric( out$max_envelope_central[ iL, ] ) ) ), col = col_envelope, border = NA) - lines( time_grid, as.numeric( out$max_envelope_central[ iL, ] ), + graphics::lines( time_grid, as.numeric( out$max_envelope_central[ iL, ] ), lty = 1, col = col_envelope, lwd = 3 ) - lines( time_grid, as.numeric( out$min_envelope_central[ iL, ] ), + graphics::lines( time_grid, as.numeric( out$min_envelope_central[ iL, ] ), lty = 1, col = col_envelope, lwd = 3 ) # Plotting the sample median - lines( time_grid, Data_curr[ which.max( Depths ), ], lty = 1, type = 'l', + graphics::lines( time_grid, Data_curr[ which.max( Depths ), ], lty = 1, type = 'l', col = col_center, lwd = 3) - lines( time_grid, max_envelope_limit, lty = 1, + graphics::lines( time_grid, max_envelope_limit, lty = 1, col = col_fence_structure, lwd = 3 ) - lines( time_grid, min_envelope_limit, lty = 1, + graphics::lines( time_grid, min_envelope_limit, lty = 1, col = col_fence_structure, lwd = 3 ) # Plotting vertical whiskers half.time_grid = which.min( abs( time_grid - 0.5 ) ) - lines( c( time_grid[ half.time_grid ], time_grid[ half.time_grid ] ), + graphics::lines( c( time_grid[ half.time_grid ], time_grid[ half.time_grid ] ), c( out$max_envelope_central[ iL, half.time_grid ], max_envelope_limit[ half.time_grid ] ), lty = 1, col = col_fence_structure, lwd = 3 ) - lines( c( time_grid[ half.time_grid ], time_grid[ half.time_grid ] ), + graphics::lines( c( time_grid[ half.time_grid ], time_grid[ half.time_grid ] ), c( out$min_envelope_central[ iL, half.time_grid ], min_envelope_limit[ half.time_grid ] ), lty = 1, col = col_fence_structure, lwd = 3 ) @@ -659,7 +660,7 @@ provided in the multivariate version of the functional boxplot' ) # Plotting outlying data if( length( ID_out ) > 0 ) { - matplot( time_grid, t( toRowMatrixForm( Data_curr[ ID_out, ] ) ), + graphics::matplot( time_grid, t( toRowMatrixForm( Data_curr[ ID_out, ] ) ), lty = 1, type = 'l', col = col_outlying, lwd = 3, add = T ) } } @@ -713,7 +714,7 @@ provided in the multivariate version of the functional boxplot' ) # concatenate the result into a row-major matrix Data_center = t( sapply( listOfValues, `[`, which.max( Depths ), 1 : P ) ) - id_central_region = which( Depths >= quantile( Depths, prob = 0.5 ) ) + id_central_region = which( Depths >= stats::quantile( Depths, prob = 0.5 ) ) max_envelope_central = t( sapply( 1 : L, function( i ) ( apply( listOfValues[[ i ]][ id_central_region, ], 2, max ) ) ) ) @@ -724,7 +725,7 @@ provided in the multivariate version of the functional boxplot' ) fence_upper = ( max_envelope_central - min_envelope_central ) * Fvalue + max_envelope_central fence_lower = ( min_envelope_central - max_envelope_central ) * Fvalue + min_envelope_central - ID_outlying = unique( unlist( sapply( 1 : L, function( iL ) ( which( + ID_outlying = unique( unlist( lapply( 1 : L, function( iL ) ( which( apply( listOfValues[[ iL ]], 1, function( x ) ( any( x > as.numeric( fence_upper[ iL, ] ) ) | any( x < as.numeric( fence_lower[ iL, ] ) ) ) ) ) diff --git a/R/Indexes.R b/R/indices.R similarity index 99% rename from R/Indexes.R rename to R/indices.R index 8b93ddc..92dde23 100644 --- a/R/Indexes.R +++ b/R/indices.R @@ -95,7 +95,7 @@ EI.default = function( Data ) #' \deqn{MEI( X(t) ) = \frac{1}{N} \sum_{i=1}^N \tilde{\lambda}( X(t) \leq #' X_i(t) ), } #' -#' where \eqn{\tilde{\lambda}(\cdot)} is the normalised Lebesgue measure over +#' where \eqn{\tilde{\lambda}(\cdot)} is the normalized Lebesgue measure over #' \eqn{I=[a,b]}, that is \eqn{\tilde{\lambda(A)} = \lambda( A ) / ( b - a )}. #' #' @param Data either an \code{fData} object or a matrix-like dataset of @@ -277,7 +277,7 @@ HI.default = function( Data ) #' \deqn{MHI( X(t) ) = \frac{1}{N} \sum_{i=1}^N \tilde{\lambda}( X(t) \geq #' X_i(t) ), } #' -#' where \eqn{\tilde{\lambda}(\cdot)} is the normalised Lebesgue measure over +#' where \eqn{\tilde{\lambda}(\cdot)} is the normalized Lebesgue measure over #' \eqn{I=[a,b]}, that is \eqn{\tilde{\lambda(A)} = \lambda( A ) / ( b - a )}. #' #' @param Data either an \code{fData} object or a matrix-like dataset of diff --git a/R/mfD_healthy.R b/R/mfD_healthy.R deleted file mode 100644 index a4c18fe..0000000 --- a/R/mfD_healthy.R +++ /dev/null @@ -1,12 +0,0 @@ -#' ECG trace of healthy subjects -#' -#' A dataset containing the 8-Lead ECG traces of 50 healthy subjects. They can be used to compare the -#' signals of pathological subjects stored in \code{mfD_LBBB} and \code{mfD_RBBB} objects. -#' -#' The 8 leads are, in order, V1, V2, V3, V4, V5, D1 and D2. The signals have been registered and -#' smoothed over an evenly spaced grid of 1024 time points at 1kHz. -#' -#' @format A \code{\link{mfData}} object. -#' -#' -"mfD_healthy" diff --git a/R/outliergram.R b/R/outliergram.R index 8fcc8ff..e637e12 100644 --- a/R/outliergram.R +++ b/R/outliergram.R @@ -1,20 +1,20 @@ -#' Outliergram for univariate functional datasets +#' Outliergram for univariate functional data sets #' -#' This function performs the outliergram of a univariate functional dataset, +#' This function performs the outliergram of a univariate functional data set, #' possibly with an adjustment of the true positive rate of outliers discovered #' under assumption of gaussianity. #' #' @section Adjustment: #' -#' When the adjustment option is selected, the value of \eqn{F} is optimised for +#' When the adjustment option is selected, the value of \eqn{F} is optimized for #' the univariate functional dataset provided with \code{fData}. In practice, #' a number \code{adjust$N_trials} of times a synthetic population #' (of size \code{adjust$trial_size} with the same covariance (robustly #' estimated from data) and centerline as \code{fData} is simulated without -#' outliers and each time an optimised value \eqn{F_i} is computed so that a +#' outliers and each time an optimized value \eqn{F_i} is computed so that a #' given proportion (\code{adjust$TPR}) of observations is flagged as outliers. #' The final value of \code{F} for the outliergram is determined as an average -#' of \eqn{F_1, F_2, \ldots, F_{N_{trials}}}. At each time step the optimisation +#' of \eqn{F_1, F_2, \ldots, F_{N_{trials}}}. At each time step the optimization #' problem is solved using \code{stats::uniroot} (Brent's method). #' #' @param fData the univariate functional dataset whose outliergram has to be @@ -35,35 +35,35 @@ #' inflation factor, \eqn{F = 1.5}, to be used, or a list specifying the #' parameters required by the adjustment. #' \itemize{ -#' \item{"\code{N_trials}"}{: the number of repetitions of the adujustment -#' procedure based on the simulation of a gaussisan population of functional +#' \item{"\code{N_trials}"}{: the number of repetitions of the adjustment +#' procedure based on the simulation of a gaussian population of functional #' data, each one producing an adjusted value of \eqn{F}, which will lead #' to the averaged adjusted value \eqn{\bar{F}}. Default is 20;} #' \item{"\code{trial_size}"}{: the number of elements in the gaussian #' population of functional data that will be simulated at each repetition of #' the adjustment procedure. Default is \code{5 * fData$N};} -#' \item{"\code{TPR}"}{: the True Positive Rate of outleirs, i.e. the proportion +#' \item{"\code{TPR}"}{: the True Positive Rate of outliers, i.e. the proportion #' of observations in a dataset without shape outliers that have to be considered #' outliers. Default is \code{2 * pnorm( 4 * qnorm( 0.25 ) )};} #' \item{"\code{F_min}"}{: the minimum value of \eqn{F}, defining the left -#' boundary for the optimisation problem aimed at finding, for a given dataset +#' boundary for the optimization problem aimed at finding, for a given dataset #' of simulated gaussian data associated to \code{fData}, the optimal value of #' \eqn{F}. Default is 0.5;} #' \item{"\code{F_max}"}{: the maximum value of \eqn{F}, defining the right -#' boundary for the optimisation problem aimed at finding, for a given dataset +#' boundary for the optimization problem aimed at finding, for a given dataset #' of simulated gaussian data associated to \code{fData}, the optimal value of #' \eqn{F}. Default is 20;} -#' \item{"\code{tol}"}{: the tolerance to be used in the optimisation problem +#' \item{"\code{tol}"}{: the tolerance to be used in the optimization problem #' aimed at finding, for a given dataset of simulated gaussian data associated #' to \code{fData}, the optimal value of \eqn{F}. Default is \code{1e-3};} #' \item{"\code{maxiter}"}{: the maximum number of iterations to solve the -#' optimisation problem aimed at finding, for a given dataset of simulated +#' optimization problem aimed at finding, for a given dataset of simulated #' gaussian data associated to \code{fData}, the optimal value of \eqn{F}. #' Default is \code{100};} #' \item{"\code{VERBOSE}"}{: a parameter controlling the verbosity of the #' adjustment process;} #' } -#' @param display either a logical value indicating wether you want the +#' @param display either a logical value indicating whether you want the #' outliergram to be displayed, or the number of the graphical device #' where you want the outliergram to be displayed. #' @param xlab a list of two labels to use on the x axis when displaying the @@ -94,61 +94,72 @@ #' \code{\link{fbplot}} #' #' @examples -#' -#' -#' set.seed( 1618 ) -#' -#' N = 200 -#' P = 200 -#' N_extra = 4 -#' -#' grid = seq( 0, 1, length.out = P ) -#' -#' Cov = exp_cov_function( grid, alpha = 0.2, beta = 0.8 ) -#' -#' Data = generate_gauss_fdata( N, -#' centerline = sin( 4 * pi * grid ), -#' Cov = Cov ) -#' -#' Data_extra = array( 0, dim = c( N_extra, P ) ) -#' -#' Data_extra[ 1, ] = generate_gauss_fdata( 1, -#' sin( 4 * pi * grid + pi / 2 ), -#' Cov = Cov ) -#' -#' Data_extra[ 2, ] = generate_gauss_fdata( 1, -#' sin( 4 * pi * grid - pi / 2 ), -#' Cov = Cov ) -#' -#' Data_extra[ 3, ] = generate_gauss_fdata( 1, -#' sin( 4 * pi * grid + pi/ 3 ), -#' Cov = Cov ) -#' -#' Data_extra[ 4, ] = generate_gauss_fdata( 1, -#' sin( 4 * pi * grid - pi / 3), -#' Cov = Cov ) -#' Data = rbind( Data, Data_extra ) -#' -#' fD = fData( grid, Data ) -#' -#' outliergram( fD, display = TRUE ) -#' -#' outliergram( fD, Fvalue = 2.5, display = TRUE ) -#' \dontrun{ -#' outliergram( fD, -#' adjust = list( N_trials = 10, -#' trial_size = 5 * nrow( Data ), -#' TPR = 0.01, -#' VERBOSE = FALSE ), -#' display = TRUE ) +#' set.seed(1618) +#' +#' N <- 200 +#' P <- 200 +#' N_extra <- 4 +#' +#' grid <- seq(0, 1, length.out = P) +#' +#' Cov <- exp_cov_function(grid, alpha = 0.2, beta = 0.8) +#' +#' Data <- generate_gauss_fdata( +#' N = N, +#' centerline = sin(4 * pi * grid), +#' Cov = Cov +#' ) +#' +#' Data_extra <- array(0, dim = c(N_extra, P)) +#' +#' Data_extra[1, ] <- generate_gauss_fdata( +#' N = 1, +#' centerline = sin(4 * pi * grid + pi / 2), +#' Cov = Cov +#' ) +#' +#' Data_extra[2, ] <- generate_gauss_fdata( +#' N = 1, +#' centerline = sin(4 * pi * grid - pi / 2), +#' Cov = Cov +#' ) +#' +#' Data_extra[3, ] <- generate_gauss_fdata( +#' N = 1, +#' centerline = sin(4 * pi * grid + pi / 3), +#' Cov = Cov +#' ) +#' +#' Data_extra[4, ] <- generate_gauss_fdata( +#' N = 1, +#' centerline = sin(4 * pi * grid - pi / 3), +#' Cov = Cov +#' ) +#' +#' Data <- rbind(Data, Data_extra) +#' +#' fD <- fData(grid, Data) +#' +#' # Outliergram with default Fvalue = 1.5 +#' outliergram(fD, display = TRUE) +#' +#' # Outliergram with Fvalue enforced to 2.5 +#' outliergram(fD, Fvalue = 2.5, display = TRUE) +#' +#' \donttest{ +#' # Outliergram with estimated Fvalue to ensure TPR of 1% +#' outliergram( +#' fData = fD, +#' adjust = list( +#' N_trials = 10, +#' trial_size = 5 * nrow(Data), +#' TPR = 0.01, +#' VERBOSE = FALSE +#' ), +#' display = TRUE +#' ) #' } #' -#' @importFrom grDevices dev.set dev.cur -#' @importFrom stats cor pnorm rnorm qnorm uniroot -#' @importFrom graphics text lines polygon plot points matplot par -#' @importFrom dplyr filter group_by summarize -#' @importFrom magrittr %>% -#' #' @export outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, Fvalue = 1.5, @@ -193,7 +204,7 @@ outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, adjust$trial_size ) TPR = ifelse( is.null( adjust$TPR ), - 2 * pnorm( 4 * qnorm( 0.25 ) ), + 2 * stats::pnorm( 4 * stats::qnorm( 0.25 ) ), adjust$TPR ) F_min = ifelse( is.null( adjust$F_min ), @@ -250,13 +261,15 @@ outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, if( VERBOSE > 0 ) { - cat( ' * * * * beginning optimisation\n' ) + cat( ' * * * * beginning optimization\n' ) } - opt = uniroot( obj_function, - interval = c( F_min, F_max ), - tol = tol, - maxiter = maxiter ) + opt = stats::uniroot( + obj_function, + interval = c( F_min, F_max ), + tol = tol, + maxiter = maxiter + ) Fvalues[ iTrial ] = opt$root } @@ -307,24 +320,26 @@ outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, } - dev.cur() - par( mfrow = c( 1, 2 ) ) + grDevices::dev.cur() + oldpar <- graphics::par(mfrow = c(1, 1)) + on.exit(graphics::par(oldpar)) + graphics::par(mfrow = c(1, 2)) # Plotting functional data if( length( out$ID_SO ) > 0 ) { - matplot( grid, t( fData$values[ - out$ID_SO, ] ), type = 'l', lty = 1, + graphics::matplot( grid, t( fData$values[ - out$ID_SO, ] ), type = 'l', lty = 1, ylim = range( fData$values ), col = col_non_outlying, xlab = xlab[[1]], ylab = ylab[[1]], main = main[[1]], ... ) - matplot( grid, t( toRowMatrixForm( fData$values[ out$ID_SO, ] ) ), + graphics::matplot( grid, t( toRowMatrixForm( fData$values[ out$ID_SO, ] ) ), type = 'l', lty = 1, lwd = 3, ylim = range( fData$values ), col = col_outlying, add = TRUE ) } else { - matplot( grid, t( fData$values ), type = 'l', lty = 1, + graphics::matplot( grid, t( fData$values ), type = 'l', lty = 1, ylim = range( fData$values ), col = col_non_outlying, xlab = xlab[[1]], @@ -339,7 +354,7 @@ outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, for( iOut in seq_along( out$ID_SO ) ) { - text( grid[ 1 ] + ( 2 * iOut - 1 ) * w_spacing, + graphics::text( grid[ 1 ] + ( 2 * iOut - 1 ) * w_spacing, fData$values[ out$ID_SO[ iOut ], which.min( abs( grid - grid[ 1 ] - ( 2 * iOut - 1 ) * w_spacing ) ) ] + @@ -353,7 +368,7 @@ outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, # Upper parabolic limit grid_1D = seq( 0, 1, length.out = 100 ) - plot( grid_1D, a_0_2 + a_1 * grid_1D + a_0_2 * N^2 * grid_1D^2, + graphics::plot( grid_1D, a_0_2 + a_1 * grid_1D + a_0_2 * N^2 * grid_1D^2, lty = 2, type = 'l', col = 'darkblue', lwd = 2, ylim = c( 0, a_0_2 + a_1 / 2 + a_0_2 * N^2/4 ), xlab = xlab[[2]], @@ -362,24 +377,24 @@ outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, if( length( out$ID_SO ) > 0 ) { - points( out$MEI_data[ - out$ID_SO ], out$MBD_data[ - out$ID_SO ], + graphics::points( out$MEI_data[ - out$ID_SO ], out$MBD_data[ - out$ID_SO ], pch = 16, col = col_non_outlying ) - points( out$MEI_data[ out$ID_SO ], out$MBD_data[ out$ID_SO ], + graphics::points( out$MEI_data[ out$ID_SO ], out$MBD_data[ out$ID_SO ], pch = 16, cex = 1.5, col = col_outlying ) for( idOut in out$ID_SO ) { - text( out$MEI_data[ idOut ], + graphics::text( out$MEI_data[ idOut ], out$MBD_data[ idOut ] + 0.5 / 30, idOut, col = col_outlying[ match( idOut, out$ID_SO ) ] ) } } else { - points( out$MEI_data, out$MBD_data, + graphics::points( out$MEI_data, out$MBD_data, pch = 16, col = col_non_outlying ) } # lower parabolic limit - lines( grid_1D, a_0_2 + a_1 * grid_1D + a_0_2 * N^2 * grid_1D^2 - + graphics::lines( grid_1D, a_0_2 + a_1 * grid_1D + a_0_2 * N^2 * grid_1D^2 - out$Q_d3 - Fvalue * out$IQR_d, lty = 2, lwd = 2, col = 'lightblue' ) } @@ -413,7 +428,7 @@ outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, d = a_0_2 + a_1 * MEI_data + N^2 * a_0_2 * MEI_data^2 - MBD_data - Q = quantile( d ) + Q = stats::quantile( d ) Q_d3 = Q[ 4 ] @@ -432,13 +447,13 @@ outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, # Low MEI curves will be checked for upward shift ID_non_outlying_Low_MEI = ID_non_outlying[ which( MEI_data[ - ID_shape_outlier ] <= - quantile( MEI_data, + stats::quantile( MEI_data, probs = p_check ) ) ] # High MEI curves will be checked for downward shift ID_non_outlying_High_MEI = ID_non_outlying[ which( MEI_data[ - ID_shape_outlier ] >= - quantile( MEI_data, probs = 1 - p_check ) ) ] + stats::quantile( MEI_data, probs = 1 - p_check ) ) ] # Manage high MEI data @@ -512,7 +527,7 @@ outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, #' Default is \code{1.5}; #' @param shift whether to apply the shifting algorithm to properly manage observations having low #' or high MEI. Default is TRUE. -#' @param display either a logical value indicating wether you want the +#' @param display either a logical value indicating whether you want the #' outliergram to be displayed, or the number of the graphical device #' where you want the outliergram to be displayed; #' @param xlab the label to use on the x axis in the outliergram plot; @@ -573,9 +588,6 @@ outliergram = function( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, #' dev.new() #' plot(mfD, col=colors, lwd=lwd) #' -#' @importFrom dplyr filter group_by summarize -#' @importFrom magrittr %>% -#' #' @export multivariate_outliergram = function( mfData, MBD_data = NULL, @@ -632,12 +644,12 @@ multivariate_outliergram = function( mfData, c = 150 )( 1 ) } - dev.cur() + grDevices::dev.cur() # Plotting outliergram ## Upper parabolic limit grid_1D = seq( 0, 1, length.out = 100 ) - plot( grid_1D, a_0_2 + a_1 * grid_1D + a_0_2 * N^2 * grid_1D^2, + graphics::plot( grid_1D, a_0_2 + a_1 * grid_1D + a_0_2 * N^2 * grid_1D^2, lty = 2, type = 'l', col = 'darkblue', lwd = 2, ylim = c( 0, a_0_2 + a_1 / 2 + a_0_2 * N^2/4 ), xlab = xlab, @@ -646,24 +658,24 @@ multivariate_outliergram = function( mfData, if( length( out$ID_SO ) > 0 ) { - points( out$MEI_data[ - out$ID_SO ], out$MBD_data[ - out$ID_SO ], + graphics::points( out$MEI_data[ - out$ID_SO ], out$MBD_data[ - out$ID_SO ], pch = 16, col = col_non_outlying ) - points( out$MEI_data[ out$ID_SO ], out$MBD_data[ out$ID_SO ], + graphics::points( out$MEI_data[ out$ID_SO ], out$MBD_data[ out$ID_SO ], pch = 16, cex = 1.5, col = col_outlying ) for( idOut in out$ID_SO ) { - text( out$MEI_data[ idOut ], + graphics::text( out$MEI_data[ idOut ], out$MBD_data[ idOut ] + 0.5 / 30, idOut, col = col_outlying[ match( idOut, out$ID_SO ) ] ) } } else { - points( out$MEI_data, out$MBD_data, + graphics::points( out$MEI_data, out$MBD_data, pch = 16, col = col_non_outlying ) } # lower parabolic limit - lines( grid_1D, a_0_2 + a_1 * grid_1D + a_0_2 * N^2 * grid_1D^2 - + graphics::lines( grid_1D, a_0_2 + a_1 * grid_1D + a_0_2 * N^2 * grid_1D^2 - out$Q_d3 - Fvalue * out$IQR_d, lty = 2, lwd = 2, col = 'lightblue' ) } @@ -694,7 +706,7 @@ multivariate_outliergram = function( mfData, d = a_0_2 + a_1 * MEI_data + N^2 * a_0_2 * MEI_data^2 - MBD_data - Q = quantile( d ) + Q = stats::quantile( d ) Q_d3 = Q[ 4 ] @@ -715,13 +727,13 @@ multivariate_outliergram = function( mfData, # Low MEI curves will be checked for upward shift ID_non_outlying_Low_MEI = ID_non_outlying[ which( MEI_data[ - ID_shape_outlier ] <= - quantile( MEI_data, + stats::quantile( MEI_data, probs = p_check ) ) ] # High MEI curves will be checked for downward shift ID_non_outlying_High_MEI = ID_non_outlying[ which( MEI_data[ - ID_shape_outlier ] >= - quantile( MEI_data, probs = 1 - p_check ) ) ] + stats::quantile( MEI_data, probs = 1 - p_check ) ) ] # Manage high MEI data diff --git a/R/roahd.R b/R/roahd-package.R similarity index 66% rename from R/roahd.R rename to R/roahd-package.R index 47bcbc1..eb0ba42 100644 --- a/R/roahd.R +++ b/R/roahd-package.R @@ -1,6 +1,6 @@ #' roahd: RObust Analysis for High dimensional Data. #' -#' A package meant to collect and provide methods for the analysis of unviariate +#' A package meant to collect and provide methods for the analysis of univariate #' and multivariate functional datasets through the use of robust methods, with #' special focus on computation of depths and outlier detection. #' Special care was devoted to the efficient implementation of robust methods, @@ -8,5 +8,12 @@ #' #' @docType package #' @name roahd +#' @keywords internal +"_PACKAGE" + +# The following block is used by usethis to automatically manage +# roxygen namespace tags. Modify with care! +## usethis namespace: start +#' @import ggplot2 +## usethis namespace: end NULL -#> NULL diff --git a/R/simulation.R b/R/simulation.R index 1509071..7f40830 100644 --- a/R/simulation.R +++ b/R/simulation.R @@ -1,6 +1,6 @@ #' Exponential covariance function over a grid #' -#' This function computes the discretisation of an exponential +#' This function computes the discretization of an exponential #' covariance function of the form: #' \deqn{C( s, t ) = \alpha e^{ - \beta | s - t | }} #' over a 1D grid \eqn{[t_0, t_1, \ldots, t_{P-1}]}, thus obtaining the @@ -101,8 +101,6 @@ exp_cov_function = function( grid, alpha, beta ) #' #' invisible(generate_gauss_fdata( N, centerline, CholCov = CholC )) #' -#' @importFrom stats rnorm -#' #' @export generate_gauss_fdata = function( N, centerline, Cov = NULL, CholCov = NULL ) @@ -118,7 +116,7 @@ generate_gauss_fdata = function( N, centerline, P = ncol( CholCov ) if( length( centerline ) != nrow( CholCov ) | nrow( CholCov ) != P ){ - stop( 'Error: You provided mismatching centerline and covaraince matrix + stop( 'Error: You provided mismatching centerline and covariance matrix Cholesky factor to generate_gauss_fdata\n') } } else if( ! is.null( Cov ) ){ @@ -134,7 +132,7 @@ to generate_gauss_fdata\n') CholCov = chol( Cov ) } - return( t( t( matrix( rnorm( N * P ), + return( t( t( matrix( stats::rnorm( N * P ), nrow = N, ncol = P ) %*% CholCov ) + centerline ) ) } @@ -187,13 +185,13 @@ to generate_gauss_fdata\n') #' without the diagonal. #' @param listCov a list containing the \eqn{L} covariance operators (provided #' in form of a \eqn{P \times P}{P x P} matrix), one for each component of the -#' multivariate functional random vairable, that have to be used in the +#' multivariate functional random variable, that have to be used in the #' generation of the processes \eqn{\epsilon_1(t), \ldots, \epsilon_L(t)}. #' At least one argument between \code{listCov} and \code{listCholCov} must be #' different from \code{NULL}. #' @param listCholCov the Cholesky factor of the \eqn{L} covariance operators #' (in \eqn{P \times P}{P x P} matrix form), one for each component of the -#' multivariate functional random vairable, that have to be used in the +#' multivariate functional random variable, that have to be used in the #' generation of the processes \eqn{\epsilon_1(t), \ldots, \epsilon_L(t)}. #' At least one argument between \code{listCov} and \code{listCholCov} must be #' different from \code{NULL}. @@ -295,7 +293,7 @@ matrices to generate_gauss_mfdata') R_chol = chol( R ) # Generating gaussian data with correlations among dimensions - Data = matrix( rnorm( N * L * P ), ncol = L, nrow = N * P ) + Data = matrix( stats::rnorm( N * L * P ), ncol = L, nrow = N * P ) Data = Data %*% R_chol diff --git a/R/utils-pipe.R b/R/utils-pipe.R new file mode 100644 index 0000000..fd0b1d1 --- /dev/null +++ b/R/utils-pipe.R @@ -0,0 +1,14 @@ +#' Pipe operator +#' +#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. +#' +#' @name %>% +#' @rdname pipe +#' @keywords internal +#' @export +#' @importFrom magrittr %>% +#' @usage lhs \%>\% rhs +#' @param lhs A value or the magrittr placeholder. +#' @param rhs A function call using the magrittr semantics. +#' @return The result of calling `rhs(lhs)`. +NULL diff --git a/R/utils.R b/R/utils.R index 8fd722b..cb13c49 100644 --- a/R/utils.R +++ b/R/utils.R @@ -6,7 +6,7 @@ #' type in order to obtain a matrix representation. #' For 1D data structures and column/row arrays and matrices the output is #' turned in a matrix format with just one row. -#' If the input structure is rectangualar, instead, it is only converted in +#' If the input structure is rectangular, instead, it is only converted in #' matrix format. #' #' @section Warning: @@ -53,12 +53,12 @@ toRowMatrixForm = function( D ) return( D ) } -#' Function to setup alpha value for a set of colours +#' Function to setup alpha value for a set of colors #' -#' \code{set_alpha} manipulates a vector of colour representations in order +#' \code{set_alpha} manipulates a vector of color representations in order #' to setup the alpha value, and get the desired transparency level. #' -#' @param col a vector of colours +#' @param col a vector of colors #' @param alpha the value(s) of alpha for (each of) the colors. #' #' @seealso \code{\link{fDColorPalette}} @@ -72,32 +72,33 @@ toRowMatrixForm = function( D ) #' alpha_col = set_alpha( original_col, c(0.5, 0.5, 0.2, 0.1 ) ) #' #' dev.new() -#' par( mfrow = c( 1, 2 ) ) +#' oldpar <- par(mfrow = c(1, 1)) +#' par(mfrow = c(1, 2)) #' #' plot( seq_along( original_col ), #' seq_along( original_col ), #' col = original_col, #' pch = 16, #' cex = 2, -#' main = 'Original colours' ) +#' main = 'Original colors' ) #' #' plot( seq_along( alpha_col ), #' seq_along( alpha_col ), #' col = alpha_col, #' pch = 16, #' cex = 2, -#' main = 'Alpha colours' ) +#' main = 'Alpha colors' ) #' -#' @importFrom grDevices col2rgb rgb +#' par(oldpar) #' #' @export set_alpha = function( col, alpha ) { alpha = alpha * 255 - rgb_colors = rbind( col2rgb( col ), alpha = alpha ) + rgb_colors = rbind( grDevices::col2rgb( col ), alpha = alpha ) - return( apply( rgb_colors, 2, function( x )( rgb( x[ 1 ], + return( apply( rgb_colors, 2, function( x )( grDevices::rgb( x[ 1 ], x[ 2 ], x[ 3 ], x[ 4 ], diff --git a/README.Rmd b/README.Rmd new file mode 100644 index 0000000..8549ca4 --- /dev/null +++ b/README.Rmd @@ -0,0 +1,138 @@ +--- +output: github_document +--- + + + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "", + fig.path = "man/figures/README-", + out.width = "100%" +) +``` + +# roahd + + +[![check-standard](https://github.com/astamm/roahd/workflows/R-CMD-check/badge.svg)](https://github.com/astamm/roahd/actions) +[![test-coverage](https://github.com/astamm/roahd/workflows/test-coverage/badge.svg)](https://github.com/astamm/roahd/actions) +[![codecov](https://codecov.io/gh/astamm/roahd/branch/master/graph/badge.svg)](https://app.codecov.io/gh/astamm/roahd) +[![pkgdown](https://github.com/astamm/roahd/workflows/pkgdown/badge.svg)](https://github.com/astamm/roahd/actions) +[![CRAN status](https://www.r-pkg.org/badges/version/roahd)](https://CRAN.R-project.org/package=roahd) +[![downloads](https://cranlogs.r-pkg.org/badges/roahd)](https://cran.r-project.org/package=roahd) + + +The __roahd__ (_Robust Analysis of High-dimensional Data_) package allows to use +a set of statistical tools for the _exploration_ and _robustification_ of +univariate and multivariate __functional datasets__ through the use of +depth-based statistical methods. + +In the implementation of functions, special attention was put to their +efficiency, so that they can be profitably used also for the analysis of +high-dimensional datasets. + +For a full-featured description of the package, please take a look at the +[roahd](https://astamm.github.io/roahd/articles/roahd.html) vignette. + +## Installation + +Install the released version of **roahd** from CRAN: + +```{r cran-install, eval=FALSE} +install.packages("roahd") +``` + +Or install the development version from GitHub with: + +```{r github-install, eval=FALSE} +# install.packages("remotes") +remotes::install_github("astamm/roahd") +``` + +## [`fData`](https://astamm.github.io/roahd/reference/fData.html) and [`mfData`](https://astamm.github.io/roahd/reference/mfData.html) objects + +A simple `S3` representation of functional data object, [`fData`](https://astamm.github.io/roahd/reference/fData.html), allows to +encapsulate the important features of univariate functional datasets (like the +grid of the dependent variable, the pointwise observations, etc.): + +```{r fData-constructor} +library(roahd) + +# Grid representing the dependent variable +grid = seq( 0, 1, length.out = 100 ) + +# Pointwise measurements of the functional dataset +Data = matrix( c( sin( 2 * pi * grid ), + cos ( 2 * pi * grid ), + sin( 2 * pi * grid + pi / 4 ) ), ncol = 100, byrow = TRUE ) + +# S3 object encapsulating the univariate functional dataset +fD = fData( grid, Data ) + +# S3 representation of a multivariate functional dataset +mfD = mfData( grid, list( 'comp1' = Data, 'comp2' = Data ) ) +``` + +Also, this allows to exploit simple calls to customized functions which +simplifies the exploratory analysis: + +```{r fData-operations, eval=FALSE} +# Algebra of fData objects +fD + 1 : 100 +fD * 4 + +fD + fD + +# Subsetting fData objects (providing other fData objects) +fD[ 1, ] +fD[ 1, 2 : 4] + +# Sample mean and (depth-based) median(s) +mean( fD ) +mean( fD[ 1, 10 : 20 ] ) +median_fData( fD, type = 'MBD' ) +``` + +```{r fData-plot} +# Plotting functions +plot( fD ) +plot( mean( fD ), lwd = 4, add = TRUE ) + +plot( fD[ 2:3, ] ) +``` + +## Robust methods for functional data analysis + +A part of the package is specifically devoted to the computation of depths and +other statistical indices for functional data: + + - Band depths and modified band depths, + - Modified band depths for multivariate functional data, + - Epigraph and hypograph indexes, + - Spearman and Kendall's correlation indexes for functional data, + - Confidence intervals and tests on Spearman's correlation coefficients for + univariate and multivariate functional data. + +These also are the core of the visualization / robustification tools like +functional boxplot +([`fbplot`](https://astamm.github.io/roahd/reference/fbplot.html)) and +outliergram +([`outliergram`](https://astamm.github.io/roahd/reference/outliergram.html)), +allowing the visualization and identification of amplitude and shape outliers. + +Thanks to the functions for the simulation of synthetic functional datasets, +both [`fbplot`](https://astamm.github.io/roahd/reference/fbplot.html) and +[`outliergram`](https://astamm.github.io/roahd/reference/outliergram.html) +procedures can be auto-tuned to the dataset at hand, in order to control the +true positive outliers rate. + +## Citation + +If you use this package for your own research, please cite the corresponding R +Journal article: + +```{r citation, echo=FALSE} +citation("roahd") +``` diff --git a/README.md b/README.md index 6db4bb4..25a7754 100644 --- a/README.md +++ b/README.md @@ -1,31 +1,62 @@ -# roahd + -[![Build Status](https://travis-ci.org/ntarabelloni/roahd.svg?branch=dev)](https://travis-ci.org/ntarabelloni/roahd) [![codecov](https://codecov.io/gh/ntarabelloni/roahd/branch/master/graph/badge.svg)](https://codecov.io/gh/ntarabelloni/roahd) +# roahd -Package __roahd__ (_Robust Analysis of High-dimensional Data_) allows to use -a set of statistical tools for the _exploration_ and _robustification_ of -univariate and multivariate __functional datasets__ through the use of depth-based -statistical methods. + +[![check-standard](https://github.com/astamm/roahd/workflows/R-CMD-check/badge.svg)](https://github.com/astamm/roahd/actions) +[![test-coverage](https://github.com/astamm/roahd/workflows/test-coverage/badge.svg)](https://github.com/astamm/roahd/actions) +[![codecov](https://codecov.io/gh/astamm/roahd/branch/master/graph/badge.svg)](https://app.codecov.io/gh/astamm/roahd) +[![pkgdown](https://github.com/astamm/roahd/workflows/pkgdown/badge.svg)](https://github.com/astamm/roahd/actions) +[![CRAN +status](https://www.r-pkg.org/badges/version/roahd)](https://CRAN.R-project.org/package=roahd) +[![downloads](https://cranlogs.r-pkg.org/badges/roahd)](https://cran.r-project.org/package=roahd) + -In the implementation of functions special attention was put to their efficiency, -so that they can be profitably used also for the analysis of high-dimensional -datasets. +The **roahd** (*Robust Analysis of High-dimensional Data*) package +allows to use a set of statistical tools for the *exploration* and +*robustification* of univariate and multivariate **functional datasets** +through the use of depth-based statistical methods. -(_For a full-featured description of the package, please turn to the Vignette_) +In the implementation of functions, special attention was put to their +efficiency, so that they can be profitably used also for the analysis of +high-dimensional datasets. -## `fData` and `mfData` objects +For a full-featured description of the package, please take a look at +the [roahd](https://astamm.github.io/roahd/articles/roahd.html) +vignette. -A simple `S3` representation of functional data object, `fData`, -allows to encapsulate the important features of univariate functional datasets (like the -grid of the dependent variable, the pointwise observations etc.): +## Installation + +Install the released version of **roahd** from CRAN: + +``` r +install.packages("roahd") +``` + +Or install the development version from GitHub with: + +``` r +# install.packages("remotes") +remotes::install_github("astamm/roahd") +``` + +## [`fData`](https://astamm.github.io/roahd/reference/fData.html) and [`mfData`](https://astamm.github.io/roahd/reference/mfData.html) objects + +A simple `S3` representation of functional data object, +[`fData`](https://astamm.github.io/roahd/reference/fData.html), allows +to encapsulate the important features of univariate functional datasets +(like the grid of the dependent variable, the pointwise observations, +etc.): + +``` r +library(roahd) -```r # Grid representing the dependent variable grid = seq( 0, 1, length.out = 100 ) -# Pointwise-measurements of the functional dataset +# Pointwise measurements of the functional dataset Data = matrix( c( sin( 2 * pi * grid ), cos ( 2 * pi * grid ), sin( 2 * pi * grid + pi / 4 ) ), ncol = 100, byrow = TRUE ) @@ -36,48 +67,89 @@ fD = fData( grid, Data ) # S3 representation of a multivariate functional dataset mfD = mfData( grid, list( 'comp1' = Data, 'comp2' = Data ) ) ``` -Also, this allows to exploit simple calls to customised functions which -simplify the exploratory analysis: -```r +Also, this allows to exploit simple calls to customized functions which +simplifies the exploratory analysis: + +``` r # Algebra of fData objects fD + 1 : 100 fD * 4 -fD_1 + fD_2 +fD + fD # Subsetting fData objects (providing other fData objects) fD[ 1, ] fD[ 1, 2 : 4] -# Smaple mean and (depth-based) median(s) +# Sample mean and (depth-based) median(s) mean( fD ) mean( fD[ 1, 10 : 20 ] ) median_fData( fD, type = 'MBD' ) +``` +``` r # Plotting functions plot( fD ) -plot( mean( fD ), add = TRUE ) - -plot( fD[ 2:3, :] ) +plot( mean( fD ), lwd = 4, add = TRUE ) ``` + -## Robust methods for functional data analysis - -A part of the package is specifically devoted to the computation of depths and -other statistical indexes for functional data: +``` r +plot( fD[ 2:3, ] ) +``` - - Band Dephts and Modified Band Depths, - - Modified band depths for multivariate functional data, - - Epigraph and Hypograph indexes, - - Spearman and Kendall's correlation indexes for functional data. - - Confidence intervals and tests on Spearman's correlation coefficients for univariate andmultivariate functional data. + -These also are the core of the visualization/robustification tools like -functional boxplot (`fbplot`) and outliergram (`outliergram`), allowing -the visualization and identification of amplitude/shape outliers. +## Robust methods for functional data analysis -Thanks to the functions for the simulation of synthetic functional datasets, -both `fbplot` and `outliergram` procedures can be auto-tuned to the dataset -at hand, in order to control the true positive outliers rate. +A part of the package is specifically devoted to the computation of +depths and other statistical indices for functional data: + +- Band depths and modified band depths, +- Modified band depths for multivariate functional data, +- Epigraph and hypograph indexes, +- Spearman and Kendall’s correlation indexes for functional data, +- Confidence intervals and tests on Spearman’s correlation + coefficients for univariate and multivariate functional data. + +These also are the core of the visualization / robustification tools +like functional boxplot +([`fbplot`](https://astamm.github.io/roahd/reference/fbplot.html)) and +outliergram +([`outliergram`](https://astamm.github.io/roahd/reference/outliergram.html)), +allowing the visualization and identification of amplitude and shape +outliers. + +Thanks to the functions for the simulation of synthetic functional +datasets, both +[`fbplot`](https://astamm.github.io/roahd/reference/fbplot.html) and +[`outliergram`](https://astamm.github.io/roahd/reference/outliergram.html) +procedures can be auto-tuned to the dataset at hand, in order to control +the true positive outliers rate. + +## Citation + +If you use this package for your own research, please cite the +corresponding R Journal article: + + + To cite roahd in publications use: + + Ieva, F., Paganoni, A. M., Romo, J., & Tarabelloni, N. (2019). roahd + Package: Robust Analysis of High Dimensional Data. The R Journal, + 11(2), pp. 291-307. + + A BibTeX entry for LaTeX users is + + @Article{, + title = {{roahd Package: Robust Analysis of High Dimensional Data}}, + author = {Francesca Ieva and Anna Maria Paganoni and Juan Romo and Nicholas Tarabelloni}, + journal = {{The R Journal}}, + year = {2019}, + volume = {11}, + number = {2}, + pages = {291--307}, + url = {https://doi.org/10.32614/RJ-2019-032}, + } diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 0000000..f79f47e --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1 @@ +url: https://astamm.github.io/roahd/ diff --git a/codecov.yml b/codecov.yml deleted file mode 100644 index 2faa2fc..0000000 --- a/codecov.yml +++ /dev/null @@ -1,7 +0,0 @@ -codecov: - branch: master - -coverage: - precision: 2 - round: down - range: "0...100" diff --git a/cran-comments.md b/cran-comments.md new file mode 100644 index 0000000..3a4f0c8 --- /dev/null +++ b/cran-comments.md @@ -0,0 +1,31 @@ +## Test environments +* local macOS R installation, R 4.1.2 +* macOS latest release (via [R-CMD-check](https://github.com/r-lib/actions/blob/master/examples/check-standard.yaml) github action) +* windows latest release (via [R-CMD-check](https://github.com/r-lib/actions/blob/master/examples/check-standard.yaml) github action) +* ubuntu 20.04 latest both release and devel (via [R-CMD-check](https://github.com/r-lib/actions/blob/master/examples/check-standard.yaml) github action) +* [win-builder](https://win-builder.r-project.org/) (release and devel) +* [R-hub](https://builder.r-hub.io) + - Windows Server 2008 R2 SP1, R-devel, 32/64 bit + - Ubuntu Linux 20.04.1 LTS, R-release, GCC + - Fedora Linux, R-devel, clang, gfortran + +## R CMD check results +There were no ERRORs or WARNINGs. + +There were 2 NOTEs: + + * checking installed package size ... NOTE + installed size is 5.2Mb + sub-directories of 1Mb or more: + data 2.9Mb + doc 1.7Mb + +The size varies according to the system on which the package is installed. + + * Maintainer: 'Aymeric Stamm ' + Possibly misspelled words in DESCRIPTION: + Aleman (42:55) + depthgram (43:57) + +This is due to a change of maintainer from Nicholas Tarabelloni to Aymeric Stamm. +Words listed as possibly misspelled are people name and name of a proposed mathematical method, hence they are spelled correctly. diff --git a/data/mfD_LBBB.rda b/data/mfD_LBBB.rda index 965d811..a5b351e 100644 Binary files a/data/mfD_LBBB.rda and b/data/mfD_LBBB.rda differ diff --git a/data/mfD_healthy.rda b/data/mfD_healthy.rda index 55a2041..05cee27 100644 Binary files a/data/mfD_healthy.rda and b/data/mfD_healthy.rda differ diff --git a/inst/CITATION b/inst/CITATION new file mode 100644 index 0000000..5f1a74d --- /dev/null +++ b/inst/CITATION @@ -0,0 +1,17 @@ +citHeader("To cite roahd in publications use:") + +citEntry( + entry = "Article", + title = "{roahd Package: Robust Analysis of High Dimensional Data}", + author = "Francesca Ieva and Anna Maria Paganoni and Juan Romo and + Nicholas Tarabelloni", + journal = "{The R Journal}", + year = "2019", + volume = "11", + number = "2", + pages = "291--307", + url = "https://doi.org/10.32614/RJ-2019-032", + textVersion = paste( + "Ieva, F., Paganoni, A. M., Romo, J., & Tarabelloni, N. (2019). roahd Package: Robust Analysis of High Dimensional Data. The R Journal, 11(2), pp. 291-307." + ) +) diff --git a/man/BCIntervalSpearman.Rd b/man/BCIntervalSpearman.Rd index e3ce1dd..fc6bd9f 100644 --- a/man/BCIntervalSpearman.Rd +++ b/man/BCIntervalSpearman.Rd @@ -4,8 +4,14 @@ \alias{BCIntervalSpearman} \title{Bootstrap Confidence Interval on Spearman's Correlation Coefficient between Univariate Functional Datasets} \usage{ -BCIntervalSpearman(fD1, fD2, ordering = "MEI", bootstrap_iterations = 1000, - alpha = 0.05, verbose = FALSE) +BCIntervalSpearman( + fD1, + fD2, + ordering = "MEI", + bootstrap_iterations = 1000, + alpha = 0.05, + verbose = FALSE +) } \arguments{ \item{fD1}{is the first univariate functional sample in form of an \code{fData} object.} @@ -36,29 +42,32 @@ same grid and have same number of observations) and computes a bootstrap confide their Spearman correlation coefficient. } \examples{ - set.seed(1) -N = 2e2 -P = 1e2 -grid = seq( 0, 1, length.out = P ) +N <- 200 +P <- 100 + +grid <- seq(0, 1, length.out = P) -# Creating an exponential covariance function to simulate guassian data -Cov = exp_cov_function( grid, alpha = 0.3, beta = 0.4 ) +# Creating an exponential covariance function to simulate Gaussian data +Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) -# Simulating (independent) gaussian functional data with given center and -# covariance function -Data_1 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) -Data_2 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) +# Simulating (independent) Gaussian functional data with given center and covariance function +Data_1 <- generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) +Data_2 <- generate_gauss_fdata( + N = N, + centerline = sin(2 * pi * grid), + Cov = Cov +) -# Using the simulated data as (independent) components of a bivariate functional -# dataset -mfD = mfData( grid, list( Data_1, Data_2 ) ) -\dontrun{ -BCIntervalSpearman(mfD$fDList[[1]], mfD$fDList[[2]], ordering = 'MEI') +# Using the simulated data as (independent) components of a bivariate functional dataset +mfD <- mfData(grid, list(Data_1, Data_2)) -BCIntervalSpearman(mfD$fDList[[1]], mfD$fDList[[2]], ordering = 'MHI') +\donttest{ +BCIntervalSpearman(mfD$fDList[[1]], mfD$fDList[[2]], ordering = "MEI") +BCIntervalSpearman(mfD$fDList[[1]], mfD$fDList[[2]], ordering = "MHI") } + # BC intervals contain zero since the functional samples are uncorrelated. } diff --git a/man/BCIntervalSpearmanMultivariate.Rd b/man/BCIntervalSpearmanMultivariate.Rd index 761d433..9c89006 100644 --- a/man/BCIntervalSpearmanMultivariate.Rd +++ b/man/BCIntervalSpearmanMultivariate.Rd @@ -4,8 +4,13 @@ \alias{BCIntervalSpearmanMultivariate} \title{Bootstrap Confidence Interval on Spearman's Correlation Coefficient of a Multivariate Functional Dataset} \usage{ -BCIntervalSpearmanMultivariate(mfD, ordering = "MEI", - bootstrap_iterations = 1000, alpha = 0.05, verbose = FALSE) +BCIntervalSpearmanMultivariate( + mfD, + ordering = "MEI", + bootstrap_iterations = 1000, + alpha = 0.05, + verbose = FALSE +) } \arguments{ \item{mfD}{is the multivariate functional sample in form of \code{mfData} object.} @@ -34,29 +39,39 @@ The function takes a multivariate functional dataset and computes a matrix of bo intervals for its Spearman correlation coefficients. } \examples{ - set.seed(1) -N = 2e2 -P = 1e2 -grid = seq( 0, 1, length.out = P ) +N <- 200 +P <- 100 +grid <- seq(0, 1, length.out = P) -# Creating an exponential covariance function to simulate guassian data -Cov = exp_cov_function( grid, alpha = 0.3, beta = 0.4 ) +# Creating an exponential covariance function to simulate Gaussian data +Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) -# Simulating (independent) gaussian functional data with given center and -# covariance function -Data_1 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) -Data_2 = generate_gauss_fdata( N, centerline = sin( 4 * pi * grid ), Cov = Cov ) -Data_3 = generate_gauss_fdata( N, centerline = sin( 6 * pi * grid ), Cov = Cov ) +# Simulating (independent) Gaussian functional data with given center and covariance function +Data_1 <- generate_gauss_fdata( + N = N, + centerline = sin(2 * pi * grid), + Cov = Cov +) +Data_2 <- generate_gauss_fdata( + N = N, + centerline = sin(4 * pi * grid), + Cov = Cov +) +Data_3 <- generate_gauss_fdata( + N = N, + centerline = sin(6 * pi * grid), + Cov = Cov +) -# Using the simulated data as (independent) components of a multivariate functional -# dataset -mfD = mfData( grid, list( Data_1, Data_2, Data_3 ) ) +# Using the simulated data as (independent) components of a multivariate functional dataset +mfD <- mfData(grid, list(Data_1, Data_2, Data_3)) -\dontrun{ -BCIntervalSpearmanMultivariate(mfD, ordering = 'MEI') +\donttest{ +BCIntervalSpearmanMultivariate(mfD, ordering = "MEI") } + # BC intervals contain zero since the functional samples are uncorrelated. } diff --git a/man/BD.Rd b/man/BD.Rd index 1f53000..a50630e 100644 --- a/man/BD.Rd +++ b/man/BD.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/BandDepths.R +% Please edit documentation in R/band_depths.R \name{BD} \alias{BD} \alias{BD.fData} diff --git a/man/BD_relative.Rd b/man/BD_relative.Rd index 06d681c..7936482 100644 --- a/man/BD_relative.Rd +++ b/man/BD_relative.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/BandDepths.R +% Please edit documentation in R/band_depths.R \name{BD_relative} \alias{BD_relative} \alias{BD_relative.fData} diff --git a/man/BTestSpearman.Rd b/man/BTestSpearman.Rd index 2a03bc8..e038c39 100644 --- a/man/BTestSpearman.Rd +++ b/man/BTestSpearman.Rd @@ -4,8 +4,14 @@ \alias{BTestSpearman} \title{Bootstrap Hypothesis Test on Spearman Correlation Coefficients for Multivariate Functional Data} \usage{ -BTestSpearman(mfD1, mfD2, bootstrap_iterations = 1000, ordering = "MEI", - normtype = "f", verbose = FALSE) +BTestSpearman( + mfD1, + mfD2, + bootstrap_iterations = 1000, + ordering = "MEI", + normtype = "f", + verbose = FALSE +) } \arguments{ \item{mfD1}{is the first functional dataset, specified in form of \code{mfData} object; it must @@ -17,7 +23,7 @@ be compatible with \code{mfD1}.} \item{bootstrap_iterations}{is the number of bootstrap iterations to be performed.} \item{ordering}{is the kind of ordering to be used in the computation of Spearman's correlation -coefficeint (default is \code{MEI}).} +coefficient (default is \code{MEI}).} \item{normtype}{is the norm to be used when comparing the Spearman correlation matrices of the two functional datasets (default is Frobenius, allowed values are the same as for parameter \code{type} in @@ -52,39 +58,59 @@ in slow performances of the test (you may consider setting \code{verbose} to \co hints on the process).}} } \examples{ - set.seed(1) -N = 2e2 -P = 1e2 -L = 2 -grid = seq( 0, 1, length.out = P ) -# Creating an exponential covariance function to simulate guassian data -Cov = exp_cov_function( grid, alpha = 0.3, beta = 0.4 ) +N <- 200 +P <- 100 +L <- 2 + +grid <- seq(0, 1, length.out = P) +# Creating an exponential covariance function to simulate Gaussian data +Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) # Simulating two populations of bivariate functional data # # The first population has very high correlation between first and second component -centerline_1 = matrix(rep(sin(2 * pi * grid)), nrow = 2, ncol=P, byrow=TRUE) -values1 = generate_gauss_mfdata( N, L, correlations = 0.9, - centerline = centerline_1, listCov = list(Cov, Cov) ) -mfD1 = mfData(grid, values1) +centerline_1 <- matrix( + data = rep(sin(2 * pi * grid)), + nrow = L, + ncol = P, + byrow = TRUE +) +values1 <- generate_gauss_mfdata( + N = N, + L = L, + correlations = 0.9, + centerline = centerline_1, + listCov = list(Cov, Cov) +) +mfD1 <- mfData(grid, values1) # Pointwise estimate cor_spearman(mfD1) # The second population has zero correlation between first and second component -centerline_2 = matrix(rep(cos(2 * pi * grid)), nrow = 2, ncol=P, byrow=TRUE) -values2 = generate_gauss_mfdata( N, L, correlations = 0, - centerline = centerline_1, listCov = list(Cov, Cov) ) -mfD2 = mfData(grid, values2) +centerline_2 <- matrix( + data = rep(cos(2 * pi * grid)), + nrow = L, + ncol = P, + byrow = TRUE +) +values2 <- generate_gauss_mfdata( + N = N, + L = L, + correlations = 0, + centerline = centerline_2, + listCov = list(Cov, Cov) +) +mfD2 <- mfData(grid, values2) # Pointwise estimate cor_spearman(mfD2) # Applying the test -\dontrun{ +\donttest{ BTestSpearman(mfD1, mfD2) } } diff --git a/man/EI.Rd b/man/EI.Rd index 228af1b..c0492a0 100644 --- a/man/EI.Rd +++ b/man/EI.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Indexes.R +% Please edit documentation in R/indices.R \name{EI} \alias{EI} \alias{EI.fData} diff --git a/man/HI.Rd b/man/HI.Rd index 157e631..17dfadd 100644 --- a/man/HI.Rd +++ b/man/HI.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Indexes.R +% Please edit documentation in R/indices.R \name{HI} \alias{HI} \alias{HI.fData} diff --git a/man/HRD.Rd b/man/HRD.Rd index 8f0e646..be8dba7 100644 --- a/man/HRD.Rd +++ b/man/HRD.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Indexes.R +% Please edit documentation in R/indices.R \name{HRD} \alias{HRD} \alias{HRD.fData} diff --git a/man/MBD.Rd b/man/MBD.Rd index 0e38f40..8377278 100644 --- a/man/MBD.Rd +++ b/man/MBD.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/BandDepths.R +% Please edit documentation in R/band_depths.R \name{MBD} \alias{MBD} \alias{MBD.fData} @@ -38,7 +38,7 @@ other elements of the dataset, i.e.: \tilde{\lambda}\big( {t : \min( X_{i_1}(t), X_{i_2}(t) ) \leq X(t) \leq \max( X_{i_1}(t), X_{i_2}(t) ) } \big), } -where \eqn{\tilde{\lambda}(\cdot)} is the normalised Lebesgue measure over +where \eqn{\tilde{\lambda}(\cdot)} is the normalized Lebesgue measure over \eqn{I=[a,b]}, that is \eqn{\tilde{\lambda(A)} = \lambda( A ) / ( b - a )}. See the References section for more details. diff --git a/man/MBD_relative.Rd b/man/MBD_relative.Rd index 8fc6380..f9d3a86 100644 --- a/man/MBD_relative.Rd +++ b/man/MBD_relative.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/BandDepths.R +% Please edit documentation in R/band_depths.R \name{MBD_relative} \alias{MBD_relative} \alias{MBD_relative.fData} @@ -45,7 +45,7 @@ elements of the former with respect to elements of the latter, i.e.: \max( Y_{i_1}(t), Y_{i_2}(t) ) } \big),} \eqn{\forall i = 1, \ldots, N}, where \eqn{\tilde{\lambda}(\cdot)} is the -normalised Lebesgue measure over \eqn{I=[a,b]}, that is +normalized Lebesgue measure over \eqn{I=[a,b]}, that is \eqn{\tilde{\lambda(A)} = \lambda( A ) / ( b - a )}. } \examples{ diff --git a/man/MEI.Rd b/man/MEI.Rd index 98e8fd5..e72b7b0 100644 --- a/man/MEI.Rd +++ b/man/MEI.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Indexes.R +% Please edit documentation in R/indices.R \name{MEI} \alias{MEI} \alias{MEI.fData} @@ -33,7 +33,7 @@ MEI, i.e.: \deqn{MEI( X(t) ) = \frac{1}{N} \sum_{i=1}^N \tilde{\lambda}( X(t) \leq X_i(t) ), } -where \eqn{\tilde{\lambda}(\cdot)} is the normalised Lebesgue measure over +where \eqn{\tilde{\lambda}(\cdot)} is the normalized Lebesgue measure over \eqn{I=[a,b]}, that is \eqn{\tilde{\lambda(A)} = \lambda( A ) / ( b - a )}. } \examples{ diff --git a/man/MHI.Rd b/man/MHI.Rd index 2cbf5dd..83fa844 100644 --- a/man/MHI.Rd +++ b/man/MHI.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Indexes.R +% Please edit documentation in R/indices.R \name{MHI} \alias{MHI} \alias{MHI.fData} @@ -33,7 +33,7 @@ MHI, i.e.: \deqn{MHI( X(t) ) = \frac{1}{N} \sum_{i=1}^N \tilde{\lambda}( X(t) \geq X_i(t) ), } -where \eqn{\tilde{\lambda}(\cdot)} is the normalised Lebesgue measure over +where \eqn{\tilde{\lambda}(\cdot)} is the normalized Lebesgue measure over \eqn{I=[a,b]}, that is \eqn{\tilde{\lambda(A)} = \lambda( A ) / ( b - a )}. } \examples{ diff --git a/man/MHRD.Rd b/man/MHRD.Rd index 0df799a..90b230d 100644 --- a/man/MHRD.Rd +++ b/man/MHRD.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/Indexes.R +% Please edit documentation in R/indices.R \name{MHRD} \alias{MHRD} \alias{MHRD.fData} diff --git a/man/area_ordered.Rd b/man/area_ordered.Rd index b4d3496..7a9e3b0 100644 --- a/man/area_ordered.Rd +++ b/man/area_ordered.Rd @@ -14,27 +14,26 @@ be compared, in form of \code{fData} object.} be compared , in form of \code{fData} object.} } \value{ -The function returns a logical vector of length \eqn{\max(N,M)} containing the -value of the predicate for all the corresponding elements. +The function returns a logical vector of length \eqn{\max(N,M)} +containing the value of the predicate for all the corresponding elements. } \description{ -This function implements an order relation between univariate functional - data based on the area-under-curve relation, that is to say a pre-order - relation obtained by comparing the area-under-curve of two - different functional data. +This function implements an order relation between univariate functional data +based on the area-under-curve relation, that is to say a pre-order relation +obtained by comparing the area-under-curve of two different functional data. } \details{ Given a univariate functional dataset, \eqn{X_1(t), X_2(t), \ldots, X_N(t)} - and another functional dataset \eqn{Y_1(t),} \eqn{Y_2(t), \ldots, Y_M(t)} - defined over the same compact interval \eqn{I=[a,b]}, the function computes - the area-under-curve (namely, the integral) in both the datasets, and checks - whether the first ones are lower or equal than the second ones. +and another functional dataset \eqn{Y_1(t),} \eqn{Y_2(t), \ldots, Y_M(t)} +defined over the same compact interval \eqn{I=[a,b]}, the function computes +the area-under-curve (namely, the integral) in both the datasets, and checks +whether the first ones are lower or equal than the second ones. By default the function tries to compare each \eqn{X_i(t)} with the - corresponding \eqn{Y_i(t)}, thus assuming \eqn{N=M}, but when either \eqn{N=1} - or \eqn{M=1}, the comparison is carried out cycling over the dataset with - fewer elements. In all the other cases (\eqn{N\neq M,} and either - \eqn{N \neq 1} or \eqn{M \neq 1}) the function stops. +corresponding \eqn{Y_i(t)}, thus assuming \eqn{N=M}, but when either +\eqn{N=1} or \eqn{M=1}, the comparison is carried out cycling over the +dataset with fewer elements. In all the other cases (\eqn{N\neq M,} and +either \eqn{N \neq 1} or \eqn{M \neq 1}) the function stops. } \examples{ @@ -63,8 +62,8 @@ area_ordered( fD_2, fD_3 ) } \references{ Valencia, D., Romo, J. and Lillo, R. (2015). A Kendall correlation -coefficient for functional dependence, -\emph{Universidad Carlos III de Madrid technical report}, +coefficient for functional dependence, \emph{Universidad Carlos III de Madrid +technical report}, \code{http://EconPapers.repec.org/RePEc:cte:wsrepe:ws133228}. } \seealso{ diff --git a/man/area_under_curve.Rd b/man/area_under_curve.Rd index 6e60205..19a9a7a 100644 --- a/man/area_under_curve.Rd +++ b/man/area_under_curve.Rd @@ -12,12 +12,24 @@ curve have to be computed, in form of \code{fData} object.} } \value{ The function returns a numeric vector containing the values of areas -under the curve for all the elements of the functional dataset \code{fData}. +under the curve for all the elements of the functional dataset +\code{fData}. } \description{ This method computes the (signed) area under the curve of elements of a univariate functional dataset, namely, their integral. } +\details{ +Given a univariate functional dataset, \eqn{X_1(t), X_2(t), \ldots, X_N(t)}, +defined over a compact interval \eqn{I=[a,b]} and observed on an evenly +spaced 1D grid \eqn{[a = t_0, t_1, \ldots, t_{P-1} = b] \subset I}, the +function computes: + +\deqn{ \sum_{i=1}^{P-2} \frac{X(t_{i+1}) - X(t_{i-1})}{2} h \approx \int_a^b +X(t) dt,} + +where \eqn{h = t_1 - t_0}. +} \examples{ P = 1e3 diff --git a/man/cor_kendall.Rd b/man/cor_kendall.Rd index 5426c85..aeac6e1 100644 --- a/man/cor_kendall.Rd +++ b/man/cor_kendall.Rd @@ -7,13 +7,13 @@ cor_kendall(mfD, ordering = "max") } \arguments{ -\item{mfD}{a bivariate functional dataset whose Kendall's tau -coefficient must be computed, in form of bivariate \code{mfData} object +\item{mfD}{a bivariate functional dataset whose Kendall's tau coefficient +must be computed, in form of bivariate \code{mfData} object (\code{mfD$L=2}).} \item{ordering}{the ordering relation to use on functional observations, -either \code{"max"} for the maximum relation or \code{"area"} for the -area under the curve relation (default is \code{"max"}).} +either \code{"max"} for the maximum relation or \code{"area"} for the area +under the curve relation (default is \code{"max"}).} } \value{ The function returns the Kendall's tau correlation coefficient for @@ -76,8 +76,8 @@ cor_kendall( mfD, ordering = 'area' ) } \references{ Valencia, D., Romo, J. and Lillo, R. (2015). A Kendall correlation -coefficient for functional dependence, -\emph{Universidad Carlos III de Madrid technical report}, +coefficient for functional dependence, \emph{Universidad Carlos III de Madrid +technical report}, \code{http://EconPapers.repec.org/RePEc:cte:wsrepe:ws133228}. } \seealso{ diff --git a/man/cor_spearman.Rd b/man/cor_spearman.Rd index 21a3744..4af7840 100644 --- a/man/cor_spearman.Rd +++ b/man/cor_spearman.Rd @@ -11,27 +11,28 @@ cor_spearman(mfD, ordering = "MEI") coefficient must be computed, in form of multivariate \code{mfData} object.} \item{ordering}{the ordering relation to use on functional observations, -either \code{"MEI"} for MEI or \code{"MHI"} for MHI (default is \code{"MEI"}).} +either \code{"MEI"} for MEI or \code{"MHI"} for MHI (default is +\code{"MEI"}).} } \value{ If the original dataset is bivariate, the function returns only the -scalar value of the correlation coefficient for the two components. -When the number of components is L >2, it returns the whole matrix of -Spearman's correlation coefficients for all the components. +scalar value of the correlation coefficient for the two components. When +the number of components is L >2, it returns the whole matrix of Spearman's +correlation coefficients for all the components. } \description{ This function computes the Spearman's correlation coefficient for a -multivariate functional dataset, with either a Modified Epigraph Index (MEI) or -Modified Hypograph Index (MHI) ranking of univariate elments of data +multivariate functional dataset, with either a Modified Epigraph Index (MEI) +or Modified Hypograph Index (MHI) ranking of univariate elements of data components. } \details{ Given a multivariate functional dataset, with first components \eqn{X^1_1(t), -X^1_2(t), \ldots, X^1_N(t)}, second components \eqn{X^2_1(t), X^2_2(t), \ldots, -X^2_N(t)}, etc., the function exploits either the MEI or MHI to compute the matrix of -Spearman's correlation coefficients. Such matrix is symmetrical and has ones on the -diagonal. The entry (i, j) represents the Spearman correlation coefficient between -curves of component i and j. +X^1_2(t), \ldots, X^1_N(t)}, second components \eqn{X^2_1(t), X^2_2(t), +\ldots, X^2_N(t)}, etc., the function exploits either the MEI or MHI to +compute the matrix of Spearman's correlation coefficients. Such matrix is +symmetrical and has ones on the diagonal. The entry (i, j) represents the +Spearman correlation coefficient between curves of component i and j. See the references for more details. } diff --git a/man/cor_spearman_accuracy.Rd b/man/cor_spearman_accuracy.Rd index b5667e8..85b99e5 100644 --- a/man/cor_spearman_accuracy.Rd +++ b/man/cor_spearman_accuracy.Rd @@ -4,60 +4,80 @@ \alias{cor_spearman_accuracy} \title{Bootstrap Spearman's correlation coefficient for multivariate functional data} \usage{ -cor_spearman_accuracy(mfD, ordering = "MEI", bootstrap_iterations = 1000, - verbose = FALSE) +cor_spearman_accuracy( + mfD, + ordering = "MEI", + bootstrap_iterations = 1000, + verbose = FALSE +) } \arguments{ \item{mfD}{a multivariate functional dataset whose Spearman's correlation coefficient must be computed, in form of multivariate \code{mfData} object.} \item{ordering}{the ordering relation to use on functional observations, -either \code{"MEI"} for MEI or \code{"MHI"} for MHI (default is \code{"MEI"}).} +either \code{"MEI"} for MEI or \code{"MHI"} for MHI (default is +\code{"MEI"}).} -\item{bootstrap_iterations}{the number of bootstrap iterations to be used for estimation of bias and -standard error.} +\item{bootstrap_iterations}{the number of bootstrap iterations to be used for +estimation of bias and standard error.} -\item{verbose}{a logical flag specifying whether to log information on the estimation progress.} +\item{verbose}{a logical flag specifying whether to log information on the +estimation progress.} } \value{ -a list of three elements: \code{mean}, the mean of the matrix of correlation coefficients; -\code{bias}, a matrix containing the estimated bias (mean - point estimate of correlation coefficients); -\code{sd}, a matrix containing the estiated standard deviation of the coefficients' matrix. In case -the multivariate functional dataset has only two components, the return type is scalar and not matrix. +a list of three elements: \code{mean}, the mean of the matrix of +correlation coefficients; \code{bias}, a matrix containing the estimated +bias (mean - point estimate of correlation coefficients); \code{sd}, a +matrix containing the estimated standard deviation of the coefficients' +matrix. In case the multivariate functional dataset has only two +components, the return type is scalar and not matrix. } \description{ -This function computes the bootstrap estimates of standard error and bias of the Spearman's -correlation coefficient for a multivariate functional dataset. +This function computes the bootstrap estimates of standard error and bias of +the Spearman's correlation coefficient for a multivariate functional dataset. } \details{ -Given a multivariate functional dataset \eqn{X_1^(i), \ldots, X_n^(i)}, \eqn{i=0, \ldots, L} -defined over the grid \eqn{I = t_0, \ldots, t_P}, having components \eqn{i=1, \ldots, L}, and a -chosen ordering strategy (MEI or MHI), the function computes the matrix of Speraman's correlation -indexes of the dataset's components, as well as their bias and standard deviation estimates -through a specified number of bootstrap iterations (bias and standard error are updated with -on-line formulas). +Given a multivariate functional dataset \eqn{X_1^(i), \ldots, X_n^(i)}, +\eqn{i=0, \ldots, L} defined over the grid \eqn{I = t_0, \ldots, t_P}, having +components \eqn{i=1, \ldots, L}, and a chosen ordering strategy (MEI or MHI), +the function computes the matrix of Spearman's correlation indices of the +dataset components, as well as their bias and standard deviation estimates +through a specified number of bootstrap iterations (bias and standard error +are updated with on-line formulas). } \examples{ +N <- 200 +P <- 100 -N = 2e2 -P = 1e2 -grid = seq( 0, 1, length.out = P ) +grid <- seq(0, 1, length.out = P) -# Creating an exponential covariance function to simulate guassian data -Cov = exp_cov_function( grid, alpha = 0.3, beta = 0.4 ) +# Creating an exponential covariance function to simulate Gaussian data +Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) -# Simulating (independent) gaussian functional data with given center and covariance function +# Simulating (independent) Gaussian functional data with given center and covariance function -Data_1 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) -Data_2 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = Cov ) +Data_1 <- generate_gauss_fdata( + N = N, + centerline = sin(2 * pi * grid), + Cov = Cov +) + +Data_2 <- generate_gauss_fdata( + N = N, + centerline = sin(2 * pi * grid), + Cov = Cov +) # Using the simulated data as (independent) components of a bivariate functional dataset -mfD = mfData( grid, list( Data_1, Data_2 ) ) -\dontrun{ -cor_spearman_accuracy(mfD, ordering='MEI') +mfD <- mfData(grid, list(Data_1, Data_2)) -cor_spearman_accuracy(mfD, ordering='MHI') +\donttest{ +# Computes bootstrap estimate of Spearman correlation +cor_spearman_accuracy(mfD, ordering = "MEI") +cor_spearman_accuracy(mfD, ordering = "MHI") } + } \seealso{ \code{\link{cor_spearman}}, \code{\link{mfData}} diff --git a/man/cov_fun.Rd b/man/cov_fun.Rd index c3e35ab..da8f73e 100644 --- a/man/cov_fun.Rd +++ b/man/cov_fun.Rd @@ -31,7 +31,7 @@ class \code{fData}, the cross-covariance function of the two datasets is returned;} \item{if \code{X} is of class \code{mfData} and \code{Y} is \code{NULL}, the upper-triangular blocks of the covariance function of \code{X} -are returned (in form of list and by row, i.e. in the squence 1_1, 1_2, ..., +are returned (in form of list and by row, i.e. in the sequence 1_1, 1_2, ..., 1_L, 2_2, ... - have a look at the labels of the list with \code{str});} \item{if \code{X} is of class \code{mfData} and \code{Y} is of class \code{fData}, @@ -41,7 +41,7 @@ returned (in form of list);} class \code{mfData}, the upper-triangular blocks of the cross-covariance of \code{X}'s and \code{Y}'s components are returned (in form of list and by row, i.e. in the -squence 1_1, 1_2, ..., 1_L, 2_2, ... - have a look at the labels +sequence 1_1, 1_2, ..., 1_L, 2_2, ... - have a look at the labels of the list with \code{str}));}} In any case, the return type is either an instance of the \code{S3} class \code{Cov} @@ -74,8 +74,6 @@ while the cross-covariance function is defined by the blocks: \deqn{C^{X,Y}_{i,j}(s,t) = Cov( X_i(s), Y_j(t))} - - The method \code{cov_fun} provides the sample estimator of the covariance or cross-covariance functions for univariate or multivariate functional datasets. diff --git a/man/depthGram.Rd b/man/depthGram.Rd new file mode 100644 index 0000000..76d7cd2 --- /dev/null +++ b/man/depthGram.Rd @@ -0,0 +1,117 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/depthgram.R +\name{depthgram} +\alias{depthgram} +\alias{depthgram.default} +\alias{depthgram.fData} +\alias{depthgram.mfData} +\title{Depthgram for univariate and multivariate functional data sets} +\usage{ +depthgram( + Data, + marginal_outliers = FALSE, + boxplot_factor = 1.5, + outliergram_factor = 1.5, + ids = NULL +) + +\method{depthgram}{default}( + Data, + marginal_outliers = FALSE, + boxplot_factor = 1.5, + outliergram_factor = 1.5, + ids = NULL +) + +\method{depthgram}{fData}( + Data, + marginal_outliers = FALSE, + boxplot_factor = 1.5, + outliergram_factor = 1.5, + ids = NULL +) + +\method{depthgram}{mfData}( + Data, + marginal_outliers = FALSE, + boxplot_factor = 1.5, + outliergram_factor = 1.5, + ids = NULL +) +} +\arguments{ +\item{Data}{A \code{\link[base]{list}} of length \code{L} (number of components) +in which each element is an \verb{N x P} matrix with \code{N} individuals and \code{P} +time points. Alternatively, it can also be an object of class +\code{\link{fData}} or of class \code{\link{mfData}}.} + +\item{marginal_outliers}{A boolean specifying whether the function should +return shape and amplitude outliers over each dimension. Defaults to +\code{FALSE}.} + +\item{boxplot_factor}{A numeric value specifying the inflation factor for +marginal functional boxplots. This is ignored if \code{marginal_outliers == FALSE}. Defaults to \code{1.5}.} + +\item{outliergram_factor}{A numeric value specifying the inflation factor for +marginal outliergrams. This is ignored if \code{marginal_outliers == FALSE}. +Defaults to \code{1.5}.} + +\item{ids}{A character vector specifying labels for individual observations. +Defaults to \code{NULL}, in which case observations will remain unlabelled.} +} +\value{ +An object of class \code{depthgram} which is a list with the following +items: +\itemize{ +\item \code{mbd.mei.d}: vector MBD of the MEI dimension-wise. +\item \code{mei.mbd.d}: vector MEI of the MBD dimension-wise. +\item \code{mbd.mei.t}: vector MBD of the MEI time-wise. +\item \code{mei.mbd.t}: vector MEI of the MEI time-wise. +\item \code{mbd.mei.t2}: vector MBD of the MEI time/correlation-wise. +\item \code{mei.mbd.t2}: vector MEI of the MBD time/correlation-wise. +\item \code{shp.out.det}: detected shape outliers by dimension. +\item \code{mag.out.det}: detected magnitude outliers by dimension. +\item \code{mbd.d}: matrix \verb{n x p} of MBD dimension-wise. +\item \code{mei.d}: matrix \verb{n x p} of MEI dimension-wise. +\item \code{mbd.t}: matrix \verb{n x p} of MBD time-wise. +\item \code{mei.t}: matrix \verb{n x p} of MEI time-wise. +\item \code{mbd.t2}: matrix \verb{n x p} of MBD time/correlation-wise +\item \code{mei.t2}: matrix \verb{n x p} of MBD time/correlation-wise. +} +} +\description{ +This function computes the three 'DepthGram' representations from a p-variate +functional data set. +} +\examples{ +N <- 2e2 +P <- 1e3 +grid <- seq(0, 1, length.out = P) +Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) + +Data <- list() +Data[[1]] <- generate_gauss_fdata( + N, + centerline = sin(2 * pi * grid), + Cov = Cov +) +Data[[2]] <- generate_gauss_fdata( + N, + centerline = sin(2 * pi * grid), + Cov = Cov +) +names <- paste0("id_", 1:nrow(Data[[1]])) + +DG1 <- depthgram(Data, marginal_outliers = TRUE, ids = names) + +fD <- fData(grid, Data[[1]]) +DG2 <- depthgram(fD, marginal_outliers = TRUE, ids = names) + +mfD <- mfData(grid, Data) +DG3 <- depthgram(mfD, marginal_outliers = TRUE, ids = names) +} +\references{ +Aleman-Gomez, Y., Arribas-Gil, A., Desco, M. Elias-Fernandez, A., and Romo, +J. (2021). "Depthgram: Visualizing Outliers in High Dimensional Functional +Data with application to Task fMRI data exploration". +} diff --git a/man/exp_cov_function.Rd b/man/exp_cov_function.Rd index 9353223..1ab30d2 100644 --- a/man/exp_cov_function.Rd +++ b/man/exp_cov_function.Rd @@ -14,13 +14,13 @@ exp_cov_function(grid, alpha, beta) \item{beta}{the beta parameter in the exponential covariance formula.} } \description{ -This function computes the discretisation of an exponential +This function computes the discretization of an exponential covariance function of the form: - \deqn{C( s, t ) = \alpha e^{ - \beta | s - t | }} +\deqn{C( s, t ) = \alpha e^{ - \beta | s - t | }} over a 1D grid \eqn{[t_0, t_1, \ldots, t_{P-1}]}, thus obtaining the \eqn{P \times P} matrix of values: - \deqn{ C_{i,j} = C( t_i, t_j ) = \alpha e^{ - \beta | t_i - t_j | } .} +\deqn{ C_{i,j} = C( t_i, t_j ) = \alpha e^{ - \beta | t_i - t_j | } .} } \examples{ diff --git a/man/fData.Rd b/man/fData.Rd index dfd6082..91743e5 100644 --- a/man/fData.Rd +++ b/man/fData.Rd @@ -11,7 +11,7 @@ fData(grid, values) measured. It must be a numeric vector of length \code{P}.} \item{values}{the values of the observations in the functional dataset, -prodived in form of a 2D data structure (e.g. matrix or array) having as +provided in form of a 2D data structure (e.g. matrix or array) having as rows the observations and as columns their measurements over the 1D grid of length \code{P} specified in \code{grid}.} } @@ -19,24 +19,24 @@ length \code{P} specified in \code{grid}.} The function returns a \code{S3} object of class \code{fData}, containing the following elements: \itemize{ - \item{"\code{N}"}{: the number of elements in the dataset;} - \item{"\code{P}"}{: the number of points in the 1D grid over which elements - are measured;} - \item{"\code{t0}"}{: the starting point of the 1D grid;} - \item{"\code{tP}"}{: the ending point of the 1D grid;} - \item{"\code{values}"}{: the matrix of measurements of the functional - observations on the 1D grid provided with \code{grid}.} +\item{"\code{N}"}{: the number of elements in the dataset;} +\item{"\code{P}"}{: the number of points in the 1D grid over which elements +are measured;} +\item{"\code{t0}"}{: the starting point of the 1D grid;} +\item{"\code{tP}"}{: the ending point of the 1D grid;} +\item{"\code{values}"}{: the matrix of measurements of the functional +observations on the 1D grid provided with \code{grid}.} } } \description{ This function implements a constructor for elements of \code{S3} class - \code{fData}, aimed at implementing a representation of a functional - dataset. +\code{fData}, aimed at implementing a representation of a functional +dataset. } \details{ The functional dataset is represented as a collection of measurement of the - observations on an evenly spaced, 1D grid of discrete points (representing, - e.g. time), namely, for functional data defined over a grid \eqn{[t_0, +observations on an evenly spaced, 1D grid of discrete points (representing, +e.g. time), namely, for functional data defined over a grid \eqn{[t_0, t_1, \ldots, t_{P-1}]}: \deqn{ f_{i,j} = f_i( t_0 + j h ), \quad h = \frac{t_P - t_0}{N}, diff --git a/man/fbplot.Rd b/man/fbplot.Rd index 3108364..265df7c 100644 --- a/man/fbplot.Rd +++ b/man/fbplot.Rd @@ -6,80 +6,108 @@ \alias{fbplot.mfData} \title{Functional boxplot of univariate and multivariate functional data} \usage{ -fbplot(Data, Depths = "MBD", Fvalue = 1.5, adjust = FALSE, - display = TRUE, xlab = NULL, ylab = NULL, main = NULL, ...) - -\method{fbplot}{fData}(Data, Depths = "MBD", Fvalue = 1.5, adjust = FALSE, - display = TRUE, xlab = NULL, ylab = NULL, main = NULL, ...) - -\method{fbplot}{mfData}(Data, Depths = list(def = "MBD", weights = "uniform"), - Fvalue = 1.5, adjust = FALSE, display = TRUE, xlab = NULL, - ylab = NULL, main = NULL, ...) +fbplot( + Data, + Depths = "MBD", + Fvalue = 1.5, + adjust = FALSE, + display = TRUE, + xlab = NULL, + ylab = NULL, + main = NULL, + ... +) + +\method{fbplot}{fData}( + Data, + Depths = "MBD", + Fvalue = 1.5, + adjust = FALSE, + display = TRUE, + xlab = NULL, + ylab = NULL, + main = NULL, + ... +) + +\method{fbplot}{mfData}( + Data, + Depths = list(def = "MBD", weights = "uniform"), + Fvalue = 1.5, + adjust = FALSE, + display = TRUE, + xlab = NULL, + ylab = NULL, + main = NULL, + ... +) } \arguments{ -\item{Data}{the univariate or multivariate functional dataset whose functional -boxplot must be determined, in form of \code{fData} or \code{mfData} object.} +\item{Data}{the univariate or multivariate functional dataset whose +functional boxplot must be determined, in form of \code{fData} or +\code{mfData} object.} \item{Depths}{either a vector containing the depths for each element of the dataset, or: \itemize{ -\item{"\emph{univariate case}"}{: a string containing the name of the method -you want to use to compute it. The default is \code{'MBD'}}; -\item{"\emph{multivariate case}"}{: a list with elements \code{def}, -containing the name of the depth notion to be used to compute depths -(\code{BD} or \code{MBD}), and \code{weights}, containing the value -of parameter \code{weights} to be passed to the depth function. Default is -\code{list( def = 'MBD', weights = 'uniform' ) }. } +\item \emph{univariate case}: a string containing the name of the method you +want to use to compute it. The default is \code{'MBD'}. +\item \emph{multivariate case}: a list with elements \code{def}, containing the +name of the depth notion to be used to compute depths (\code{BD} or +\code{MBD}), and \code{weights}, containing the value of parameter +\code{weights} to be passed to the depth function. Default is +\code{list(def = 'MBD', weights = 'uniform')}. } + In both cases the name of the functions to compute depths must be available in the caller's environment.} -\item{Fvalue}{the value of the inflation factor \eqn{F}, default is -\code{F = 1.5}.} +\item{Fvalue}{the value of the inflation factor \eqn{F}, default is \code{F = +1.5}.} \item{adjust}{either \code{FALSE} if you would like the default value for the inflation factor, \eqn{F = 1.5}, to be used, or (for now \bold{only in the univariate functional case}) a list specifying the parameters required by the adjustment: - \itemize{ - \item{"\code{N_trials}"}{: the number of repetitions of the adujustment - procedure based on the simulation of a gaussisan population of functional - data, each one producing an adjusted value of \eqn{F}, which will lead - to the averaged adjusted value \eqn{\bar{F}}. Default is 20;} - \item{"\code{trial_size}"}{: the number of elements in the gaussian - population of functional data that will be simulated at each repetition of - the adjustment procedure. Default is 8 * \code{Data$N};} - \item{"\code{TPR}"}{: the True Positive Rate of outliers, i.e. the proportion - of observations in a dataset without amplitude outliers that have to be - considered outliers. Default is \code{2 * pnorm( 4 * qnorm( 0.25 ) )};} - \item{"\code{F_min}"}{: the minimum value of \eqn{F}, defining the left - boundary for the optimisation problem aimed at finding, for a given dataset - of simulated gaussian data associated to \code{Data}, the optimal value of - \eqn{F}. Default is 0.5;} - \item{"\code{F_max}"}{: the maximum value of \eqn{F}, defining the right - boundary for the optimisation problem aimed at finding, for a given dataset - of simulated gaussian data associated to \code{Data}, the optimal value of - \eqn{F}. Default is 5;} - \item{"\code{tol}"}{: the tolerance to be used in the optimisation problem - aimed at finding, for a given dataset of simulated gaussian data associated - to \code{Data}, the optimal value of \eqn{F}. Default is \code{1e-3};} - \item{"\code{maxiter}"}{: the maximum number of iterations to solve the - optimisation problem aimed at finding, for a given dataset of simulated - gaussian data associated to \code{Data}, the optimal value of \eqn{F}. - Default is \code{100};} - \item{"\code{VERBOSE}"}{: a parameter controlling the verbosity of the - adjustment process;} - }} - -\item{display}{either a logical value indicating wether you want the -outliergram to be displayed, or the number of the graphical device -where you want the outliergram to be displayed.} +\itemize{ +\item \code{N_trials}: the number of repetitions of the adjustment procedure +based on the simulation of a gaussian population of functional data, each +one producing an adjusted value of \eqn{F}, which will lead to the averaged +adjusted value \eqn{\bar{F}}. Default is 20. +\item \code{trial_size}: the number of elements in the gaussian population of +functional data that will be simulated at each repetition of the adjustment +procedure. Default is 8 * \code{Data$N}. +\item \code{TPR}: the True Positive Rate of outliers, i.e. the proportion of +observations in a dataset without amplitude outliers that have to be +considered outliers. Default is \code{2 * pnorm(4 * qnorm(0.25))}. +\item \code{F_min}: the minimum value of \eqn{F}, defining the left boundary +for the optimization problem aimed at finding, for a given dataset of +simulated gaussian data associated to \code{Data}, the optimal value of +\eqn{F}. Default is 0.5. +\item \code{F_max}: the maximum value of \eqn{F}, defining the right boundary +for the optimization problem aimed at finding, for a given dataset of +simulated gaussian data associated to \code{Data}, the optimal value of +\eqn{F}. Default is 5. +\item \code{tol}: the tolerance to be used in the optimization problem aimed at +finding, for a given dataset of simulated gaussian data associated to +\code{Data}, the optimal value of \eqn{F}. Default is \code{1e-3}. +\item \code{maxiter}: the maximum number of iterations to solve the +optimization problem aimed at finding, for a given dataset of simulated +gaussian data associated to \code{Data}, the optimal value of \eqn{F}. +Default is \code{100}. +\item \code{VERBOSE}: a parameter controlling the verbosity of the adjustment +process. +}} + +\item{display}{either a logical value indicating whether you want the +functional boxplot to be displayed, or the number of the graphical device +where you want the functional boxplot to be displayed.} \item{xlab}{the label to use on the x axis when displaying the functional boxplot.} -\item{ylab}{the label (or list of labels for the multivariate functional case) -to use on the y axis when displaying the functional boxplot.} +\item{ylab}{the label (or list of labels for the multivariate functional +case) to use on the y axis when displaying the functional boxplot.} \item{main}{the main title (or list of titles for the multivariate functional case) to be used when displaying the functional boxplot.} @@ -87,11 +115,15 @@ case) to be used when displaying the functional boxplot.} \item{...}{additional graphical parameters to be used in plotting functions.} } \value{ -Even when used in graphical way to plot the functional boxplot, the function -returns a list of three elements: the first, \code{Depths}, contains the depths -of each element of the functional dataset; the second, \code{Fvalue}, is the -value of F used to obtain the outliers, and the third, \code{ID_out}, contains -the vector of indices of dataset's elements flagged as outliers (if any). +Even when used in graphical way to plot the functional boxplot, the +function returns a list of three elements: +\itemize{ +\item \code{Depths}: contains the depths of each element of the functional +dataset. +\item \code{Fvalue}: is the value of F used to obtain the outliers. +\item \code{ID_out}: contains the vector of indices of dataset elements flagged +as outliers (if any). +} } \description{ This function can be used to perform the functional boxplot of univariate or @@ -100,19 +132,18 @@ multivariate functional data. \section{Adjustment}{ -In the \bold{univariate functional case}, when the adjustment option is selected, -the value of \eqn{F} is optimised for the univariate functional dataset -provided with \code{Data}. +In the \bold{univariate functional case}, when the adjustment option is +selected, the value of \eqn{F} is optimized for the univariate functional +dataset provided with \code{Data}. In practice, a number \code{adjust$N_trials} of times a synthetic population (of size \code{adjust$tiral_size} with the same covariance (robustly estimated from data) and centerline as \code{fData} is simulated without -outliers and each time an optimised value \eqn{F_i} is computed so that a +outliers and each time an optimized value \eqn{F_i} is computed so that a given proportion (\code{adjust$TPR}) of observations is flagged as outliers. The final value of \code{F} for the functional boxplot is determined as an -average of \eqn{F_1, F_2, \ldots, F_{N_{trials}}}. -At each time step the optimisation problem is solved using -\code{stats::uniroot} (Brent's method. +average of \eqn{F_1, F_2, \dots, F_{N_{trials}}}. At each time step the +optimization problem is solved using \code{stats::uniroot} (Brent's method). } \examples{ @@ -137,7 +168,9 @@ D = D + rexp(N, rate = 0.05) fD = fData( grid, D ) dev.new() -par( mfrow = c(1,3) ) +oldpar <- par(mfrow = c(1, 1)) +par(mfrow = c(1, 3)) + plot( fD, lwd = 2, main = 'Functional dataset', xlab = 'time', ylab = 'values' ) @@ -145,6 +178,8 @@ fbplot( fD, main = 'Functional boxplot', xlab = 'time', ylab = 'values', Fvalue boxplot(fD$values[,1], ylim = range(fD$values), main = 'Boxplot of functional dataset at t_0 ' ) +par(oldpar) + # UNIVARIATE FUNCTIONAL BOXPLOT - WITH ADJUSTMENT @@ -163,7 +198,7 @@ Data = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), fD = fData( grid, Data ) dev.new() -\dontrun{ +\donttest{ fbplot( fD, adjust = list( N_trials = 10, trial_size = 5 * N, VERBOSE = TRUE ), @@ -204,11 +239,13 @@ fbplot( mfD, Fvalue = 2.5, xlab = 'time', ylab = list( 'Values 1', } \references{ -Sun, Y., & Genton, M. G. (2012). Functional boxplots. Journal of +\enumerate{ +\item Sun, Y., & Genton, M. G. (2012). Functional boxplots. Journal of Computational and Graphical Statistics. - -Sun, Y., & Genton, M. G. (2012). Adjusted functional boxplots for spatio- -temporal data visualization and outlier detection. Environmetrics, 23(1), 54-64. +\item Sun, Y., & Genton, M. G. (2012). Adjusted functional boxplots for +spatio-temporal data visualization and outlier detection. Environmetrics, +23(1), 54-64. +} } \seealso{ \code{\link{fData}}, \code{\link{MBD}}, \code{\link{BD}}, diff --git a/man/figures/README-fData-operations-1.png b/man/figures/README-fData-operations-1.png new file mode 100644 index 0000000..d75c856 Binary files /dev/null and b/man/figures/README-fData-operations-1.png differ diff --git a/man/figures/README-fData-operations-2.png b/man/figures/README-fData-operations-2.png new file mode 100644 index 0000000..88ead71 Binary files /dev/null and b/man/figures/README-fData-operations-2.png differ diff --git a/man/figures/README-fData-plot-1.png b/man/figures/README-fData-plot-1.png new file mode 100644 index 0000000..6ed022b Binary files /dev/null and b/man/figures/README-fData-plot-1.png differ diff --git a/man/figures/README-fData-plot-2.png b/man/figures/README-fData-plot-2.png new file mode 100644 index 0000000..e491f07 Binary files /dev/null and b/man/figures/README-fData-plot-2.png differ diff --git a/man/figures/logo.png b/man/figures/logo.png new file mode 100644 index 0000000..3e0d8f3 Binary files /dev/null and b/man/figures/logo.png differ diff --git a/man/generate_gauss_mfdata.Rd b/man/generate_gauss_mfdata.Rd index 61bc994..9d676cc 100644 --- a/man/generate_gauss_mfdata.Rd +++ b/man/generate_gauss_mfdata.Rd @@ -4,8 +4,14 @@ \alias{generate_gauss_mfdata} \title{Generation of gaussian multivariate functional data} \usage{ -generate_gauss_mfdata(N, L, centerline, correlations, listCov = NULL, - listCholCov = NULL) +generate_gauss_mfdata( + N, + L, + centerline, + correlations, + listCov = NULL, + listCholCov = NULL +) } \arguments{ \item{N}{the number of distinct functional observations to generate.} @@ -26,14 +32,14 @@ without the diagonal.} \item{listCov}{a list containing the \eqn{L} covariance operators (provided in form of a \eqn{P \times P}{P x P} matrix), one for each component of the -multivariate functional random vairable, that have to be used in the +multivariate functional random variable, that have to be used in the generation of the processes \eqn{\epsilon_1(t), \ldots, \epsilon_L(t)}. At least one argument between \code{listCov} and \code{listCholCov} must be different from \code{NULL}.} \item{listCholCov}{the Cholesky factor of the \eqn{L} covariance operators (in \eqn{P \times P}{P x P} matrix form), one for each component of the -multivariate functional random vairable, that have to be used in the +multivariate functional random variable, that have to be used in the generation of the processes \eqn{\epsilon_1(t), \ldots, \epsilon_L(t)}. At least one argument between \code{listCov} and \code{listCholCov} must be different from \code{NULL}.} diff --git a/man/max_ordered.Rd b/man/max_ordered.Rd index c79a21b..5728811 100644 --- a/man/max_ordered.Rd +++ b/man/max_ordered.Rd @@ -14,26 +14,26 @@ be compared, in form of \code{fData} object.} be compared, in form of \code{fData} object.} } \value{ -The function returns a logical vector of length \eqn{\max(N,M)} containing the -value of the predicate for all the corresponding elements. +The function returns a logical vector of length \eqn{\max(N,M)} +containing the value of the predicate for all the corresponding elements. } \description{ -This function implements an order relation between univariate functional - data based on the maximum relation, that is to say a pre-order relation - obtained by comparing the maxima of two different functional data. +This function implements an order relation between univariate functional data +based on the maximum relation, that is to say a pre-order relation obtained +by comparing the maxima of two different functional data. } \details{ Given a univariate functional dataset, \eqn{X_1(t), X_2(t), \ldots, X_N(t)} - and another functional dataset \eqn{Y_1(t),} \eqn{Y_2(t), \ldots, Y_M(t)} - defined over the same compact interval \eqn{I=[a,b]}, the function computes - the maxima in both the datasets, and checks whether the first ones are lower - or equal than the second ones. +and another functional dataset \eqn{Y_1(t),} \eqn{Y_2(t), \ldots, Y_M(t)} +defined over the same compact interval \eqn{I=[a,b]}, the function computes +the maxima in both the datasets, and checks whether the first ones are lower +or equal than the second ones. By default the function tries to compare each \eqn{X_i(t)} with the - corresponding \eqn{Y_i(t)}, thus assuming \eqn{N=M}, but when either \eqn{N=1} - or \eqn{M=1}, the comparison is carried out cycling over the dataset with - fewer elements. In all the other cases (\eqn{N\neq M,} and either - \eqn{N \neq 1} or \eqn{M \neq 1}) the function stops. +corresponding \eqn{Y_i(t)}, thus assuming \eqn{N=M}, but when either +\eqn{N=1} or \eqn{M=1}, the comparison is carried out cycling over the +dataset with fewer elements. In all the other cases (\eqn{N\neq M,} and +either \eqn{N \neq 1} or \eqn{M \neq 1}) the function stops. } \examples{ @@ -62,8 +62,8 @@ max_ordered( fD_2, fD_3 ) } \references{ Valencia, D., Romo, J. and Lillo, R. (2015). A Kendall correlation -coefficient for functional dependence, -\emph{Universidad Carlos III de Madrid technical report}, +coefficient for functional dependence, \emph{Universidad Carlos III de Madrid +technical report}, \code{http://EconPapers.repec.org/RePEc:cte:wsrepe:ws133228}. } \seealso{ diff --git a/man/maxima.Rd b/man/maxima.Rd index 0dcb6df..c81be29 100644 --- a/man/maxima.Rd +++ b/man/maxima.Rd @@ -23,12 +23,11 @@ the values of maxima, and \code{grid} contains the grid points where maxima are reached. } \description{ -This function computes the maximum value of each element of a -univariate functional dataset, optionally returing also the value of the -grid where they are fulfilled. +This function computes the maximum value of each element of a univariate +functional dataset, optionally returning also the value of the grid where +they are fulfilled. } \examples{ - P = 1e3 grid = seq( 0, 1, length.out = P ) diff --git a/man/mean.mfData.Rd b/man/mean.mfData.Rd index b7aaf1d..3557c20 100644 --- a/man/mean.mfData.Rd +++ b/man/mean.mfData.Rd @@ -48,14 +48,23 @@ mfD = mfData( grid, ) # Graphical representation of the mean -par( mfrow = c( 1, 3 ) ) +oldpar <- par(mfrow = c(1, 1)) +par(mfrow = c(1, L)) -for( iL in 1 : L ) +for(iL in 1:L) { - plot( mfD$fDList[[ 1 ]] ) - plot( mean( mfD )$fDList[[ 1 ]], col = 'black', - lwd = 2, lty = 2, add = TRUE ) + plot(mfD$fDList[[iL]]) + plot( + mean(mfD)$fDList[[iL]], + col = 'black', + lwd = 2, + lty = 2, + add = TRUE + ) } + +par(oldpar) + } \seealso{ \code{\link{mfData}} diff --git a/man/median_mfData.Rd b/man/median_mfData.Rd index 1f05b88..3d13b8f 100644 --- a/man/median_mfData.Rd +++ b/man/median_mfData.Rd @@ -60,14 +60,23 @@ mfD = mfData( grid, med_mfD = median_mfData( mfD, type = 'multiMBD', weights = 'uniform' ) # Graphical representation of the mean -par( mfrow = c( 1, 3 ) ) +oldpar <- par(mfrow = c(1, 1)) +par(mfrow = c(1, L)) -for( iL in 1 : L ) +for(iL in 1:L) { - plot( mfD$fDList[[ 1 ]] ) - plot( med_mfD$fDList[[ 1 ]], col = 'black', - lwd = 2, lty = 2, add = TRUE ) + plot(mfD$fDList[[iL]]) + plot( + med_mfD$fDList[[iL]], + col = 'black', + lwd = 2, + lty = 2, + add = TRUE + ) } + +par(oldpar) + } \seealso{ \code{\link{mfData}}, \code{\link{mean.mfData}}, diff --git a/man/mfD_LBBB.Rd b/man/mfD_LBBB.Rd index 2fa5599..1bb6d4d 100644 --- a/man/mfD_LBBB.Rd +++ b/man/mfD_LBBB.Rd @@ -1,10 +1,12 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/mfD_LBBB.R +% Please edit documentation in R/data.R \docType{data} \name{mfD_LBBB} \alias{mfD_LBBB} \title{ECG trace of subjects suffering from Left-Bundle-Branch-Block (LBBB)} -\format{A \code{\link{mfData}} object.} +\format{ +A \code{\link{mfData}} object. +} \usage{ mfD_LBBB } diff --git a/man/mfD_healthy.Rd b/man/mfD_healthy.Rd index da4e503..edcb4fe 100644 --- a/man/mfD_healthy.Rd +++ b/man/mfD_healthy.Rd @@ -1,10 +1,12 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/mfD_healthy.R +% Please edit documentation in R/data.R \docType{data} \name{mfD_healthy} \alias{mfD_healthy} \title{ECG trace of healthy subjects} -\format{A \code{\link{mfData}} object.} +\format{ +A \code{\link{mfData}} object. +} \usage{ mfD_healthy } diff --git a/man/mfData.Rd b/man/mfData.Rd index 5730f5f..ac4519d 100644 --- a/man/mfData.Rd +++ b/man/mfData.Rd @@ -19,14 +19,14 @@ or array) having as rows the \code{N} observations and as columns the The function returns a \code{S3} object of class \code{mfData}, containing the following elements: \itemize{ - \item{"\code{N}"}{: the number of elements in the dataset;} - \item{"\code{L}"}{: the number of components of the functional dataset;} - \item{"\code{P}"}{: the number of points in the 1D grid over which elements - are measured;} - \item{"\code{t0}"}{: the starting point of the 1D grid;} - \item{"\code{tP}"}{: the ending point of the 1D grid;} - \item{"\code{fDList}"}{: the list of \code{fData} objects representing the - \code{L} components as corresponding unviariate functional datasets.} +\item{"\code{N}"}{: the number of elements in the dataset;} +\item{"\code{L}"}{: the number of components of the functional dataset;} +\item{"\code{P}"}{: the number of points in the 1D grid over which elements +are measured;} +\item{"\code{t0}"}{: the starting point of the 1D grid;} +\item{"\code{tP}"}{: the ending point of the 1D grid;} +\item{"\code{fDList}"}{: the list of \code{fData} objects representing the +\code{L} components as corresponding univariate functional datasets.} } } \description{ diff --git a/man/minima.Rd b/man/minima.Rd index 64088fe..cddd030 100644 --- a/man/minima.Rd +++ b/man/minima.Rd @@ -24,8 +24,8 @@ are reached. } \description{ This function computes computes the minimum value of each element of a -univariate functional dataset, optionally returing also the value of the -grid where they are fulfilled. +univariate functional dataset, optionally returning also the value of the grid +where they are fulfilled. } \examples{ diff --git a/man/multivariate_outliergram.Rd b/man/multivariate_outliergram.Rd index cb078a0..085a7ed 100644 --- a/man/multivariate_outliergram.Rd +++ b/man/multivariate_outliergram.Rd @@ -4,9 +4,19 @@ \alias{multivariate_outliergram} \title{Outliergram for multivariate functional datasets} \usage{ -multivariate_outliergram(mfData, MBD_data = NULL, MEI_data = NULL, - weights = "uniform", p_check = 0.05, Fvalue = 1.5, shift = TRUE, - display = TRUE, xlab = NULL, ylab = NULL, main = NULL) +multivariate_outliergram( + mfData, + MBD_data = NULL, + MEI_data = NULL, + weights = "uniform", + p_check = 0.05, + Fvalue = 1.5, + shift = TRUE, + display = TRUE, + xlab = NULL, + ylab = NULL, + main = NULL +) } \arguments{ \item{mfData}{the multivariate functional dataset whose outliergram has to be @@ -31,7 +41,7 @@ Default is \code{1.5};} \item{shift}{whether to apply the shifting algorithm to properly manage observations having low or high MEI. Default is TRUE.} -\item{display}{either a logical value indicating wether you want the +\item{display}{either a logical value indicating whether you want the outliergram to be displayed, or the number of the graphical device where you want the outliergram to be displayed;} diff --git a/man/outliergram.Rd b/man/outliergram.Rd index e4b30aa..7c60b7c 100644 --- a/man/outliergram.Rd +++ b/man/outliergram.Rd @@ -2,11 +2,21 @@ % Please edit documentation in R/outliergram.R \name{outliergram} \alias{outliergram} -\title{Outliergram for univariate functional datasets} +\title{Outliergram for univariate functional data sets} \usage{ -outliergram(fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, - Fvalue = 1.5, adjust = FALSE, display = TRUE, xlab = NULL, - ylab = NULL, main = NULL, ...) +outliergram( + fData, + MBD_data = NULL, + MEI_data = NULL, + p_check = 0.05, + Fvalue = 1.5, + adjust = FALSE, + display = TRUE, + xlab = NULL, + ylab = NULL, + main = NULL, + ... +) } \arguments{ \item{fData}{the univariate functional dataset whose outliergram has to be @@ -31,37 +41,37 @@ adjusted for the dataset provided in \code{fData}.} \item{adjust}{either \code{FALSE} if you would like the default value for the inflation factor, \eqn{F = 1.5}, to be used, or a list specifying the parameters required by the adjustment. - \itemize{ - \item{"\code{N_trials}"}{: the number of repetitions of the adujustment - procedure based on the simulation of a gaussisan population of functional - data, each one producing an adjusted value of \eqn{F}, which will lead - to the averaged adjusted value \eqn{\bar{F}}. Default is 20;} - \item{"\code{trial_size}"}{: the number of elements in the gaussian - population of functional data that will be simulated at each repetition of - the adjustment procedure. Default is \code{5 * fData$N};} - \item{"\code{TPR}"}{: the True Positive Rate of outleirs, i.e. the proportion - of observations in a dataset without shape outliers that have to be considered - outliers. Default is \code{2 * pnorm( 4 * qnorm( 0.25 ) )};} - \item{"\code{F_min}"}{: the minimum value of \eqn{F}, defining the left - boundary for the optimisation problem aimed at finding, for a given dataset - of simulated gaussian data associated to \code{fData}, the optimal value of - \eqn{F}. Default is 0.5;} - \item{"\code{F_max}"}{: the maximum value of \eqn{F}, defining the right - boundary for the optimisation problem aimed at finding, for a given dataset - of simulated gaussian data associated to \code{fData}, the optimal value of - \eqn{F}. Default is 20;} - \item{"\code{tol}"}{: the tolerance to be used in the optimisation problem - aimed at finding, for a given dataset of simulated gaussian data associated - to \code{fData}, the optimal value of \eqn{F}. Default is \code{1e-3};} - \item{"\code{maxiter}"}{: the maximum number of iterations to solve the - optimisation problem aimed at finding, for a given dataset of simulated - gaussian data associated to \code{fData}, the optimal value of \eqn{F}. - Default is \code{100};} - \item{"\code{VERBOSE}"}{: a parameter controlling the verbosity of the - adjustment process;} - }} - -\item{display}{either a logical value indicating wether you want the +\itemize{ +\item{"\code{N_trials}"}{: the number of repetitions of the adjustment +procedure based on the simulation of a gaussian population of functional +data, each one producing an adjusted value of \eqn{F}, which will lead +to the averaged adjusted value \eqn{\bar{F}}. Default is 20;} +\item{"\code{trial_size}"}{: the number of elements in the gaussian +population of functional data that will be simulated at each repetition of +the adjustment procedure. Default is \code{5 * fData$N};} +\item{"\code{TPR}"}{: the True Positive Rate of outliers, i.e. the proportion +of observations in a dataset without shape outliers that have to be considered +outliers. Default is \code{2 * pnorm( 4 * qnorm( 0.25 ) )};} +\item{"\code{F_min}"}{: the minimum value of \eqn{F}, defining the left +boundary for the optimization problem aimed at finding, for a given dataset +of simulated gaussian data associated to \code{fData}, the optimal value of +\eqn{F}. Default is 0.5;} +\item{"\code{F_max}"}{: the maximum value of \eqn{F}, defining the right +boundary for the optimization problem aimed at finding, for a given dataset +of simulated gaussian data associated to \code{fData}, the optimal value of +\eqn{F}. Default is 20;} +\item{"\code{tol}"}{: the tolerance to be used in the optimization problem +aimed at finding, for a given dataset of simulated gaussian data associated +to \code{fData}, the optimal value of \eqn{F}. Default is \code{1e-3};} +\item{"\code{maxiter}"}{: the maximum number of iterations to solve the +optimization problem aimed at finding, for a given dataset of simulated +gaussian data associated to \code{fData}, the optimal value of \eqn{F}. +Default is \code{100};} +\item{"\code{VERBOSE}"}{: a parameter controlling the verbosity of the +adjustment process;} +}} + +\item{display}{either a logical value indicating whether you want the outliergram to be displayed, or the number of the graphical device where you want the outliergram to be displayed.} @@ -87,73 +97,90 @@ list containing: \item{\code{ID_outliers}}{: the vector of observations id corresponding to outliers.}} } \description{ -This function performs the outliergram of a univariate functional dataset, +This function performs the outliergram of a univariate functional data set, possibly with an adjustment of the true positive rate of outliers discovered under assumption of gaussianity. } \section{Adjustment}{ -When the adjustment option is selected, the value of \eqn{F} is optimised for +When the adjustment option is selected, the value of \eqn{F} is optimized for the univariate functional dataset provided with \code{fData}. In practice, a number \code{adjust$N_trials} of times a synthetic population (of size \code{adjust$trial_size} with the same covariance (robustly estimated from data) and centerline as \code{fData} is simulated without -outliers and each time an optimised value \eqn{F_i} is computed so that a +outliers and each time an optimized value \eqn{F_i} is computed so that a given proportion (\code{adjust$TPR}) of observations is flagged as outliers. The final value of \code{F} for the outliergram is determined as an average -of \eqn{F_1, F_2, \ldots, F_{N_{trials}}}. At each time step the optimisation +of \eqn{F_1, F_2, \ldots, F_{N_{trials}}}. At each time step the optimization problem is solved using \code{stats::uniroot} (Brent's method). } \examples{ - - -set.seed( 1618 ) - -N = 200 -P = 200 -N_extra = 4 - -grid = seq( 0, 1, length.out = P ) - -Cov = exp_cov_function( grid, alpha = 0.2, beta = 0.8 ) - -Data = generate_gauss_fdata( N, - centerline = sin( 4 * pi * grid ), - Cov = Cov ) - -Data_extra = array( 0, dim = c( N_extra, P ) ) - -Data_extra[ 1, ] = generate_gauss_fdata( 1, - sin( 4 * pi * grid + pi / 2 ), - Cov = Cov ) - -Data_extra[ 2, ] = generate_gauss_fdata( 1, - sin( 4 * pi * grid - pi / 2 ), - Cov = Cov ) - -Data_extra[ 3, ] = generate_gauss_fdata( 1, - sin( 4 * pi * grid + pi/ 3 ), - Cov = Cov ) - -Data_extra[ 4, ] = generate_gauss_fdata( 1, - sin( 4 * pi * grid - pi / 3), - Cov = Cov ) -Data = rbind( Data, Data_extra ) - -fD = fData( grid, Data ) - -outliergram( fD, display = TRUE ) - -outliergram( fD, Fvalue = 2.5, display = TRUE ) -\dontrun{ -outliergram( fD, - adjust = list( N_trials = 10, - trial_size = 5 * nrow( Data ), - TPR = 0.01, - VERBOSE = FALSE ), - display = TRUE ) +set.seed(1618) + +N <- 200 +P <- 200 +N_extra <- 4 + +grid <- seq(0, 1, length.out = P) + +Cov <- exp_cov_function(grid, alpha = 0.2, beta = 0.8) + +Data <- generate_gauss_fdata( + N = N, + centerline = sin(4 * pi * grid), + Cov = Cov +) + +Data_extra <- array(0, dim = c(N_extra, P)) + +Data_extra[1, ] <- generate_gauss_fdata( + N = 1, + centerline = sin(4 * pi * grid + pi / 2), + Cov = Cov +) + +Data_extra[2, ] <- generate_gauss_fdata( + N = 1, + centerline = sin(4 * pi * grid - pi / 2), + Cov = Cov +) + +Data_extra[3, ] <- generate_gauss_fdata( + N = 1, + centerline = sin(4 * pi * grid + pi / 3), + Cov = Cov +) + +Data_extra[4, ] <- generate_gauss_fdata( + N = 1, + centerline = sin(4 * pi * grid - pi / 3), + Cov = Cov +) + +Data <- rbind(Data, Data_extra) + +fD <- fData(grid, Data) + +# Outliergram with default Fvalue = 1.5 +outliergram(fD, display = TRUE) + +# Outliergram with Fvalue enforced to 2.5 +outliergram(fD, Fvalue = 2.5, display = TRUE) + +\donttest{ +# Outliergram with estimated Fvalue to ensure TPR of 1\% +outliergram( + fData = fD, + adjust = list( + N_trials = 10, + trial_size = 5 * nrow(Data), + TPR = 0.01, + VERBOSE = FALSE + ), + display = TRUE +) } } diff --git a/man/pipe.Rd b/man/pipe.Rd new file mode 100644 index 0000000..a648c29 --- /dev/null +++ b/man/pipe.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/utils-pipe.R +\name{\%>\%} +\alias{\%>\%} +\title{Pipe operator} +\usage{ +lhs \%>\% rhs +} +\arguments{ +\item{lhs}{A value or the magrittr placeholder.} + +\item{rhs}{A function call using the magrittr semantics.} +} +\value{ +The result of calling \code{rhs(lhs)}. +} +\description{ +See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. +} +\keyword{internal} diff --git a/man/plot.Cov.Rd b/man/plot.Cov.Rd index a9082a9..08bcec3 100644 --- a/man/plot.Cov.Rd +++ b/man/plot.Cov.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/fData.R \name{plot.Cov} \alias{plot.Cov} -\title{Specialised method to plot \code{Cov} objects} +\title{Specialized method to plot \code{Cov} objects} \usage{ \method{plot}{Cov}(x, ...) } @@ -13,7 +13,7 @@ } \description{ This function performs the plot of an object of class \code{Cov}, i.e. a -covariance or cross-covaraince function. +covariance or cross-covariance function. } \details{ It builds above the function \code{graphics::image}, therefore any additional diff --git a/man/plot.depthgram.Rd b/man/plot.depthgram.Rd new file mode 100644 index 0000000..74066a1 --- /dev/null +++ b/man/plot.depthgram.Rd @@ -0,0 +1,104 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/depthgram.R +\name{plot.depthgram} +\alias{plot.depthgram} +\title{Specialized method to plot 'depthgram' objects} +\usage{ +\method{plot}{depthgram}( + x, + limits = FALSE, + ids = NULL, + print = FALSE, + plot_title = "", + shorten = TRUE, + col = NULL, + pch = 19, + sp = 2, + st = 4, + sa = 10, + text_labels = "", + ... +) +} +\arguments{ +\item{x}{An object of class \code{depthgram} as output by the +\code{\link{depthgram}} function.} + +\item{limits}{A boolean specifying whether the empirical limits for outlier +detection should be drawn. Defaults to \code{FALSE}.} + +\item{ids}{A character vector specifying labels for individual observations. +Defaults to \code{NULL}, in which case observations will named by their id +number in order of appearance.} + +\item{print}{A boolean specifying whether the graphical output should be +optimized for printed version. Defaults to \code{FALSE}.} + +\item{plot_title}{A character string specifying the main title for the plot. +Defaults to \code{""}, which means no title.} + +\item{shorten}{A boolean specifying whether labels must be shorten to 15 +characters. Defaults to \code{TRUE}.} + +\item{col}{Color palette used for the plot. Defaults to \code{NULL}, in which case +a default palette produced by the \code{\link[grDevices]{hcl}} function is +used.} + +\item{pch}{Point shape. See \code{\link[plotly]{plotly}} for more details. +Defaults to \code{19}.} + +\item{sp}{Point size. See \code{\link[plotly]{plotly}} for more details. +Defaults to \code{2}.} + +\item{st}{Label size. See \code{\link[plotly]{plotly}} for more details. +Defaults to \code{4}.} + +\item{sa}{Axis title sizes. See \code{\link[plotly]{plotly}} for more +details. Defaults to \code{10}.} + +\item{text_labels}{A character vector specifying the labels for the +individuals. It is overridden if \code{limits = TRUE}, for which only outliers +labels are shown. See \code{\link[plotly]{plotly}} for more details. +Defaults to \code{""}.} + +\item{...}{Other arguments to be passed to the base \code{\link[base]{plot}} +function. Unused.} +} +\value{ +A list with the following items: +\itemize{ +\item \code{p}: list with all the interactive (plotly) depthGram plots; +\item \code{out}: outliers detected; +\item \code{colors}: used colors for plotting. +} +} +\description{ +This function plots the three 'DepthGram' representations from the output of +the \code{\link{depthgram}} function. +} +\examples{ +N <- 50 +P <- 50 +grid <- seq(0, 1, length.out = P) +Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) + +Data <- list() +Data[[1]] <- generate_gauss_fdata( + N, + centerline = sin(2 * pi * grid), + Cov = Cov +) +Data[[2]] <- generate_gauss_fdata( + N, + centerline = sin(2 * pi * grid), + Cov = Cov +) +names <- paste0("id_", 1:nrow(Data[[1]])) +DG <- depthgram(Data, marginal_outliers = TRUE, ids = names) +plot(DG) +} +\references{ +Aleman-Gomez, Y., Arribas-Gil, A., Desco, M. Elias-Fernandez, A., and Romo, +J. (2021). "Depthgram: Visualizing Outliers in High Dimensional Functional +Data with application to Task fMRI data exploration". +} diff --git a/man/plot.fData.Rd b/man/plot.fData.Rd index c895790..cce836f 100644 --- a/man/plot.fData.Rd +++ b/man/plot.fData.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/fData.R \name{plot.fData} \alias{plot.fData} -\title{Specialised method to plot \code{fData} objects} +\title{Specialized method to plot \code{fData} objects} \usage{ \method{plot}{fData}(x, ...) } @@ -14,7 +14,7 @@ \description{ This function performs the plot of a functional univariate dataset stored in an object of class \code{fData}. It is able to accept all the usual -customisable graphical parameters, otherwise it will use the default ones. +customizable graphical parameters, otherwise it will use the default ones. } \examples{ diff --git a/man/plot.mfData.Rd b/man/plot.mfData.Rd index f21a501..8377e41 100644 --- a/man/plot.mfData.Rd +++ b/man/plot.mfData.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/fData.R \name{plot.mfData} \alias{plot.mfData} -\title{Specialised method to plot \code{mfData} objects} +\title{Specialized method to plot \code{mfData} objects} \usage{ \method{plot}{mfData}(x, ...) } @@ -15,7 +15,7 @@ \description{ This function performs the plot of a functional multivariate dataset stored in an object of class \code{mfData}. It is able to accept all the usual -customisable graphical parameters, otherwise it will use the default ones. +customizable graphical parameters, otherwise it will use the default ones. } \details{ The current active graphical device is split into a number of sub-figures, diff --git a/man/roahd.Rd b/man/roahd.Rd index 1b72f0c..7458d12 100644 --- a/man/roahd.Rd +++ b/man/roahd.Rd @@ -1,14 +1,43 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/roahd.R +% Please edit documentation in R/roahd-package.R \docType{package} \name{roahd} \alias{roahd} \alias{roahd-package} \title{roahd: RObust Analysis for High dimensional Data.} \description{ -A package meant to collect and provide methods for the analysis of unviariate +A package meant to collect and provide methods for the analysis of univariate and multivariate functional datasets through the use of robust methods, with special focus on computation of depths and outlier detection. Special care was devoted to the efficient implementation of robust methods, so that they can be employed also in high-dimensional datasets, } +\seealso{ +Useful links: +\itemize{ + \item \url{https://astamm.github.io/roahd/} + \item \url{https://github.com/astamm/roahd} + \item Report bugs at \url{https://github.com/astamm/roahd/issues} +} + +} +\author{ +\strong{Maintainer}: Aymeric Stamm \email{aymeric.stamm@math.cnrs.fr} (\href{https://orcid.org/0000-0002-8725-3654}{ORCID}) [contributor] + +Authors: +\itemize{ + \item Nicholas Tarabelloni \email{nicholas.tarabelloni@polimi.it} + \item Ana Arribas-Gil \email{aarribas@est-econ.uc3m.es} + \item Francesca Ieva \email{francesca.ieva@polimi.it} + \item Anna Maria Paganoni \email{anna.paganoni@polimi.it} + \item Juan Romo \email{romo@est-econ.uc3m.es} +} + +Other contributors: +\itemize{ + \item Francesco Palma \email{frapalma7892@gmail.com} [contributor] + \item Antonio Elias-Fernandez [contributor] +} + +} +\keyword{internal} diff --git a/man/set_alpha.Rd b/man/set_alpha.Rd index 06c0195..fcb8389 100644 --- a/man/set_alpha.Rd +++ b/man/set_alpha.Rd @@ -2,17 +2,17 @@ % Please edit documentation in R/utils.R \name{set_alpha} \alias{set_alpha} -\title{Function to setup alpha value for a set of colours} +\title{Function to setup alpha value for a set of colors} \usage{ set_alpha(col, alpha) } \arguments{ -\item{col}{a vector of colours} +\item{col}{a vector of colors} \item{alpha}{the value(s) of alpha for (each of) the colors.} } \description{ -\code{set_alpha} manipulates a vector of colour representations in order +\code{set_alpha} manipulates a vector of color representations in order to setup the alpha value, and get the desired transparency level. } \examples{ @@ -24,21 +24,24 @@ alpha_col = set_alpha( original_col, 0.5 ) alpha_col = set_alpha( original_col, c(0.5, 0.5, 0.2, 0.1 ) ) dev.new() -par( mfrow = c( 1, 2 ) ) +oldpar <- par(mfrow = c(1, 1)) +par(mfrow = c(1, 2)) plot( seq_along( original_col ), seq_along( original_col ), col = original_col, pch = 16, cex = 2, - main = 'Original colours' ) + main = 'Original colors' ) plot( seq_along( alpha_col ), seq_along( alpha_col ), col = alpha_col, pch = 16, cex = 2, - main = 'Alpha colours' ) + main = 'Alpha colors' ) + +par(oldpar) } \seealso{ diff --git a/man/sub-.fData.Rd b/man/sub-.fData.Rd index eb9e6fe..8a77dd5 100644 --- a/man/sub-.fData.Rd +++ b/man/sub-.fData.Rd @@ -3,7 +3,7 @@ \name{sub-.fData} \alias{sub-.fData} \alias{[.fData} -\title{Operator \code{sub-.fData} to subset \code{fData} obejcts} +\title{Operator \code{sub-.fData} to subset \code{fData} objects} \usage{ \method{[}{fData}(fD, i, j, as_fData = TRUE) } @@ -51,22 +51,25 @@ fD = fData( grid, Cov = C ) ) dev.new() -par( mfrow = c( 2, 2 ) ) +oldpar <- par(mfrow = c(1, 1)) +par(mfrow = c(2, 2)) # Original data -plot( fD ) +plot(fD) # Subsetting observations -plot( fD[ c(1,2,3), , as_fData = TRUE ] ) +plot(fD[c(1, 2, 3), , as_fData = TRUE]) # Subsetting measurements -plot( fD[ , 1 : 30 ] ) +plot(fD[, 1:30]) # Subsetting both observations and measurements -plot( fD[ 1 : 10, 50 : P ] ) +plot(fD[1:10, 50:P]) + +par(oldpar) # Subsetting both observations and measurements but returning a matrix -fD[ 1 : 10, 50 : P, as_fData = FALSE ] +fD[1:10, 50:P, as_fData = FALSE] } \seealso{ diff --git a/man/sub-.mfData.Rd b/man/sub-.mfData.Rd index 6e1541f..494e3e5 100644 --- a/man/sub-.mfData.Rd +++ b/man/sub-.mfData.Rd @@ -3,7 +3,7 @@ \name{sub-.mfData} \alias{sub-.mfData} \alias{[.mfData} -\title{Operator \code{sub-.mfData} to subset \code{mfData} obejcts} +\title{Operator \code{sub-.mfData} to subset \code{mfData} objects} \usage{ \method{[}{mfData}(mfD, i, j) } diff --git a/man/toRowMatrixForm.Rd b/man/toRowMatrixForm.Rd index fbeaef7..6fbb2c9 100644 --- a/man/toRowMatrixForm.Rd +++ b/man/toRowMatrixForm.Rd @@ -15,7 +15,7 @@ This function manipulates a numeric data structure of vector/array/matrix type in order to obtain a matrix representation. For 1D data structures and column/row arrays and matrices the output is turned in a matrix format with just one row. -If the input structure is rectangualar, instead, it is only converted in +If the input structure is rectangular, instead, it is only converted in matrix format. } \section{Warning}{ diff --git a/man/unfold.Rd b/man/unfold.Rd index dd554e0..3cbd977 100644 --- a/man/unfold.Rd +++ b/man/unfold.Rd @@ -7,7 +7,7 @@ unfold(fData) } \arguments{ -\item{fData}{the unvariate functional dataset in form of \code{fData} object.} +\item{fData}{the univariate functional dataset in form of \code{fData} object.} } \value{ The function returns an \code{fData} object whose observations are @@ -19,7 +19,7 @@ This function operates on a univariate functional dataset and transforms its observations unfolding their values and turning them into monotone functions. } \details{ -Each function of the \code{fData} object is transformed into a nonmonotone +Each function of the \code{fData} object is transformed into a non-monotone function into a monotone function by ``unfolding'' it at any of its maxima. For more details about the definition of the transform, see the reference. } @@ -40,9 +40,13 @@ fD = fData( time_grid, D ) fD_unfold = unfold( fD ) dev.new() -par( mfrow = c( 1, 2 ) ) -plot( fD, main = 'Original data' ) -plot( fD_unfold, main = 'Unfolded data' ) +oldpar <- par(mfrow = c(1, 1)) +par(mfrow = c(1, 2)) + +plot(fD, main = 'Original data') +plot(fD_unfold, main = 'Unfolded data') + +par(oldpar) } \references{ diff --git a/man/warp.Rd b/man/warp.Rd index f36558d..d274daa 100644 --- a/man/warp.Rd +++ b/man/warp.Rd @@ -67,7 +67,9 @@ wfD = fData( time_grid, warpings ) fD_warped = warp( fD, wfD ) dev.new() -par( mfrow = c( 1, 3 ) ) +oldpar <- par(mfrow = c(1, 1)) +par(mfrow = c(1, 3)) + plot( fD, main = 'Unregistered functions', xlab = 'actual grid', ylab = 'values' ) plot( wfD, @@ -77,6 +79,8 @@ plot( fD_warped, main = 'Warped functions', xlab = 'registered grid', ylab = 'values' ) +par(oldpar) + } \seealso{ \code{\link{fData}} diff --git 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42b95bd..345d2c7 100644 --- a/roahd.Rproj +++ b/roahd.Rproj @@ -19,4 +19,4 @@ BuildType: Package PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source PackageCheckArgs: --as-cran -PackageRoxygenize: rd,collate,namespace,vignette +PackageRoxygenize: rd,collate,namespace diff --git a/tests/testthat/_snaps/correlation.md b/tests/testthat/_snaps/correlation.md new file mode 100644 index 0000000..2195805 --- /dev/null +++ b/tests/testthat/_snaps/correlation.md @@ -0,0 +1,144 @@ +# cor_kendall() and cor_spearman() work as expected + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT+ozWrgdOlA + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT+gKFeu5z/A + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT+mtF7yKlcf + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT+mtF7yKlcd + +# cor_kendall() & cor_spearman() work on Case Study 1 from Dalia Valencia, Rosa Lillo, Juan Romo. + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/VKMBC35uw + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/VKMBC35uw + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/d5CMdG0wK + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/d5CMdG0wK + +# cor_kendall() & cor_spearman() work on Case Study 2 from Dalia Valencia, Rosa Lillo, Juan Romo. + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/TR0MVG2BY + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/S9wONeqvI + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/d3HZeA1/2 + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/d3HZeA1/4 + +# cor_kendall() & cor_spearman() work on Case Study 6 from Dalia Valencia, Rosa Lillo, Juan Romo. + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/TR0MVG2BY + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/TR0MVG2BY + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/b0NgonaT2 + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/b0NgonaT2 + +# cor_kendall() & cor_spearman() work on Case Study 7 from Dalia Valencia, Rosa Lillo, Juan Romo. + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/YMHu3Rm0Y + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/Xj/yoBQP4 + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/hTvy3ulRM + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/hTvy3ulRM + +# cor_kendall() & cor_spearman() work on Case Study 8 from Dalia Valencia, Rosa Lillo, Juan Romo. + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb+lUOAGr/YA + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb+n0txDtZpw + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb+jCy3H0XEo + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb+jCy3H0XEq + +# cor_kendall() & cor_spearman() work on Case Study 9 from Dalia Valencia, Rosa Lillo, Juan Romo. + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb/XP70gZE9q + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb/twONeqvIA + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb/s4vFcRFBa + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb/s4vFcRFBa + +# cor_kendall() & cor_spearman() work on Case Study 10 from Dalia Valencia, Rosa Lillo, Juan Romo. + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT+xw2Va5/eg + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/bArd77MYc + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/ij1JuCyaD + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAT/ij1JuCyaD + +# cor_kendall() & cor_spearman() work on Case Study 11 from Dalia Valencia, Rosa Lillo, Juan Romo. + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb+CY+RqKWDA + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb+Pw9BaR3iA + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb+JQ90YD4Ej + +--- + + WAoAAAACAAQBAgACAwAAAAAOAAAAAb+JQ90YD4El + diff --git a/tests/testthat/_snaps/depthgram.md b/tests/testthat/_snaps/depthgram.md new file mode 100644 index 0000000..a04d183 --- /dev/null +++ b/tests/testthat/_snaps/depthgram.md @@ -0,0 +1,905 @@ +# `depthgram()` works as expected for all supported input types + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["mbd.mei.d", "mei.mbd.d", "mbd.mei.t", "mei.mbd.t", "mbd.mei.t2", "mei.mbd.t2", "shp.out.det", "mag.out.det", "mbd.d", "mei.d", "mbd.t", "mei.t", "mbd.t2", "mei.t2", "corr.mei"] + }, + "class": { + "type": "character", + "attributes": {}, + "value": ["depthgram", "list"] + } + }, + "value": [ + { + "type": "double", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["id_1", 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file mode 100644 index 0000000..80e1f42 --- /dev/null +++ b/tests/testthat/_snaps/fData.md @@ -0,0 +1,7495 @@ +# `fData() correctly creates `fData` objects + + WAoAAAACAAQBAgACAwAAAAMTAAAABgAAAA4AAAABAAAAAAAAAAAAAAAOAAAAAT/wAAAAAAAA + AAAADgAAAAE/hK/WoFK/WwAAAA0AAAABAAAAZAAAAA0AAAABAAAAZAAAAg4AACcQv+UoCRLz + BkY/w3M8q74S7D/jAdNxthpSv/SOgZQyvVo/zhXVGFHtoT/RvUm/j8Cov9Qlpq6XJNS/0ylp + g8FnP7/TyVP+45s9v98zGS20xTm/0LpF5+omjL/hf7QI5uKSv9s2A6YpsZg/ohORnjjbaz/g + 0TIBddgTv67thBad+Tm/0em9wkDB2b/f8PbR7jS9v91YrvyMqxU/9Svbi9WLfD+yzSt4wUMy + v9EzXAdKNxu/zuLaZROTOT/QHE2tVAeyv9hRXd6Lori/6WIDXpTjOD/UJcrjsiBQv+HxFceN + M1K/gPsuLqrrk7/gZ4xXC8JVP+NR8kvSvGW/0KvrErBFcL/Y3m8tu+kcv9GSO55H2vy/7I2m + XHxVCL/kdwqSfp2Pv/Ma22ZKNk6/54D11GyCgr/EoetXcXuPv9BU6V4uRnk/6WfOsaMi6b/i + uvBu7qXkv9378e4KvsW/w6yO0Kvvor/hbYwksnzhv+D5qzF/sUa/42h5TJWcu7/l8Zbsahvr + v9JcxiZot1O/0Wqs0Reefr/vp49FQtJyv9Rne7Po59K/42+GVgPSDL/hyhOuQbwhv7bCMFtD + bPU/07zMh8GOwj/s4anT6d3Nv9sb/Fq/5JU/7CWgEgCdG7/kSwVsB6K+P9cEJnirQok/9laW + 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b/tests/testthat/_snaps/fData/s3-plot-method-for-mfdata-objects.svg new file mode 100644 index 0000000..746bc3f --- /dev/null +++ b/tests/testthat/_snaps/fData/s3-plot-method-for-mfdata-objects.svg @@ -0,0 +1,312 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 + + + + + + +-2 +-1 +0 +1 +2 + + + + + + + + +First Component +values + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.2 +0.4 +0.6 +0.8 +1.0 + + + + + + +-2 +-1 +0 +1 +2 + + + + + + + + +Second Component +values + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tests/testthat/_snaps/fbplot.md b/tests/testthat/_snaps/fbplot.md new file mode 100644 index 0000000..2724e71 --- /dev/null +++ b/tests/testthat/_snaps/fbplot.md @@ -0,0 +1,150 @@ +# fbplot() works as expected for univariate data + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["Depth", "Fvalue", "ID_outliers"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.57142857, 0.56666667, 0.55238095, 0.52857143, 0.4952381, 0.45238095, 0.4, 0.33809524, 0.26666667, 0.18571429, 0.0952381, 0.56666667, 0.55238095, 0.52857143, 0.4952381, 0.45238095, 0.4, 0.33809524, 0.26666667, 0.18571429, 0.0952381] + }, + { + "type": "double", + "attributes": {}, + "value": [10] + }, + { + "type": "integer", + "attributes": {}, + "value": [] + } + ] + } + +--- + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["Depth", "Fvalue", "ID_outliers"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.57142857, 0.56666667, 0.55238095, 0.52857143, 0.4952381, 0.45238095, 0.4, 0.33809524, 0.26666667, 0.18571429, 0.0952381, 0.56666667, 0.55238095, 0.52857143, 0.4952381, 0.45238095, 0.4, 0.33809524, 0.26666667, 0.18571429, 0.0952381] + }, + { + "type": "double", + "attributes": {}, + "value": [1.5] + }, + { + "type": "integer", + "attributes": {}, + "value": [] + } + ] + } + +# `fbplot()` with adjustment works as expected for univariate data + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["Depth", "Fvalue", "ID_outliers"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.06207423, 0.4806182, 0.39098453, 0.04365739, 0.44692794, 0.36812168, 0.30055904, 0.45534798, 0.41595303, 0.33035271, 0.47350637, 0.4377111, 0.4663402, 0.47779142, 0.45792786, 0.46389371, 0.46314918, 0.39360946, 0.44196248, 0.07459094, 0.28035752, 0.32204265, 0.46513459, 0.45606381, 0.44474485, 0.13901643, 0.38058052, 0.37798124, 0.46975743, 0.30690918, 0.29414942, 0.46802741, 0.29861178, 0.23896657, 0.15493627, 0.05332858, 0.0502602, 0.10108313, 0.44699671, 0.41301162, 0.30115415, 0.33881876, 0.24859383, 0.27066341, 0.13664305, 0.38153796, 0.26053082, 0.19512882, 0.31794389, 0.43193331, 0.32979335, 0.45490613, 0.07715046, 0.17229579, 0.48855182, 0.26584545, 0.37015904, 0.2573911, 0.22661515, 0.34862349, 0.34306485, 0.03986244, 0.45025507, 0.484522, 0.4432824, 0.29552465, 0.40354886, 0.07868954, 0.25818725, 0.48746982, 0.3615277, 0.23698565, 0.47663118, 0.48278685, 0.07745523, 0.49507238, 0.21239776, 0.06406605, 0.47204377, 0.4683022, 0.47784048, 0.3171822, 0.26124409, 0.49313812, 0.37290982, 0.39004297, 0.35679295, 0.48855695, 0.45937603, 0.10688481, 0.48645996, 0.31865876, 0.10747271, 0.1654182, 0.34110605, 0.20022573, 0.39005996, 0.42419287, 0.42740505, 0.27486766, 0.16698437, 0.42153138, 0.46629371, 0.41049042, 0.17842806, 0.43998685, 0.35554341, 0.47339126, 0.39089042, 0.42854589, 0.44887198, 0.42911327, 0.38113347, 0.47026758, 0.48951038, 0.36730116, 0.33150076, 0.37961202, 0.44848176, 0.37384898, 0.37703487, 0.39525964, 0.15197403, 0.40868409, 0.03386437, 0.47405707, 0.16161683, 0.13616962, 0.29086445, 0.45372665, 0.28505267, 0.44772986, 0.14796297, 0.46537892, 0.09480208, 0.43990092, 0.41642613, 0.15094188, 0.40566509, 0.44213451, 0.4266004, 0.20504914, 0.24733932, 0.41687455, 0.43027768, 0.41629932, 0.11801715, 0.42715383, 0.32588313, 0.41554677, 0.46766397, 0.46349066, 0.30845371, 0.34268168, 0.41033074, 0.3523915, 0.45084441, 0.2289079, 0.1294921, 0.45032802, 0.24863439, 0.46717675, 0.44997034, 0.48205098, 0.48917098, 0.47209154, 0.03125627, 0.24451527, 0.4636707, 0.29808016, 0.24568465, 0.488438, 0.46023487, 0.01717259, 0.36580473, 0.26379014, 0.29447872, 0.048602, 0.43578004, 0.46799327, 0.03882068, 0.42604265, 0.12830253, 0.44276152, 0.47060361, 0.02815375, 0.25235142, 0.2213584, 0.3044186, 0.48106325, 0.37363976, 0.03335263, 0.22843222, 0.44412056, 0.1050485, 0.46802533, 0.46753202, 0.25527246, 0.49119872, 0.32369603, 0.26436954, 0.25860281, 0.42283671, 0.33976994, 0.46661291, 0.28427431, 0.21380697, 0.39952192, 0.45889042, 0.28176946, 0.4269281, 0.30782156, 0.3082602, 0.29828553, 0.35409475, 0.46670669, 0.48868938, 0.44322437, 0.48501595, 0.35160689, 0.24099784, 0.42907351, 0.04175519, 0.39168754, 0.48282549, 0.1503984, 0.01466421, 0.44697651, 0.38034677, 0.3062198, 0.46525307, 0.35367679, 0.37200577, 0.3884776, 0.2423216, 0.16018068, 0.00924297, 0.48338164, 0.38889988, 0.45931639, 0.46180168, 0.46662253, 0.32917002, 0.43351535, 0.43188184, 0.38939014, 0.29601828, 0.44588425, 0.44795719, 0.44096866, 0.34202774, 0.12822653, 0.44403094, 0.41212265, 0.37511102, 0.48503535, 0.21238477, 0.26890677, 0.25567663, 0.36975375, 0.39454782, 0.3838283, 0.36491447, 0.3636396, 0.37381275, 0.45337299, 0.47860569, 0.23546052, 0.4916388, 0.45184208, 0.46004601, 0.10908842, 0.47033523, 0.09201074, 0.15607888, 0.40458325, 0.28442052, 0.2077289, 0.46778421, 0.39061611, 0.422261, 0.43019848, 0.37458004, 0.19026517, 0.33738982, 0.42040048, 0.31637467, 0.37983487, 0.30151888, 0.45314661, 0.15880962, 0.41241267, 0.19359551, 0.36595591, 0.33761122, 0.06509707, 0.33355383, 0.44657908, 0.46137683, 0.47797098, 0.31381034, 0.42667078, 0.49185812, 0.35904898, 0.44158605, 0.45487984, 0.29125756, 0.36472754, 0.41214525, 0.39805451, 0.18613307, 0.40325451, 0.29638926, 0.45558749, 0.2828622, 0.19832449, 0.46185042, 0.12868633, 0.40489731, 0.42593956, 0.24906661, 0.45177379, 0.21410228, 0.33555158, 0.47830525, 0.18217363, 0.33507639, 0.18833218, 0.29787255, 0.45662942, 0.35874982, 0.34876393, 0.4887208, 0.46888048, 0.43081491, 0.28843735, 0.38617026, 0.04710894, 0.39965178, 0.46826228, 0.4611899, 0.43688609, 0.23377651, 0.39502894, 0.40967776, 0.29553154, 0.43065283, 0.02687631, 0.48409315, 0.45273331, 0.08710108, 0.40327198, 0.45636024, 0.47989451, 0.39721315, 0.36715367, 0.27233651, 0.22806717, 0.44060489, 0.22663359, 0.27842132, 0.1214485, 0.46468232, 0.44668778, 0.33505972, 0.28790445, 0.42702493, 0.41161571, 0.38898966, 0.0837398, 0.3670416, 0.20318862, 0.3077523, 0.44848673, 0.34558124, 0.48231022, 0.37865828, 0.39707351, 0.32118509, 0.4437297, 0.20429675, 0.07573403, 0.46227848, 0.36989483, 0.30132393, 0.2690222, 0.45340008, 0.42842148, 0.23154293, 0.48097411, 0.3334291, 0.02033619, 0.24501852, 0.21340922, 0.44857251, 0.4539992, 0.45028938, 0.33763014, 0.37012473, 0.33960737, 0.27397948, 0.46719535, 0.48083447, 0.40706293, 0.41262653, 0.48710846, 0.14777988, 0.3793204, 0.45910236, 0.47035575, 0.14485002, 0.47336946, 0.43320449, 0.15782044, 0.45351856, 0.25833635, 0.19352962, 0.4502315, 0.43398878, 0.49330581, 0.21125804, 0.46569154, 0.40486285, 0.46428617, 0.47337074, 0.43514693, 0.19152834, 0.45257379, 0.26191647, 0.33021034, 0.10338405, 0.08648192, 0.39853547, 0.18579447, 0.33341339, 0.20594293, 0.10760641, 0.16213226, 0.20651527, 0.28819158, 0.18151102, 0.41162485, 0.46112609, 0.2312715, 0.32131206, 0.34991343, 0.4655083, 0.371579, 0.45231679, 0.06499832, 0.37931752, 0.3307479, 0.28777876, 0.39664866, 0.29246092, 0.36591022, 0.35633828, 0.23253948, 0.4695208, 0.24691543, 0.47737267, 0.27367503, 0.44010148, 0.4243123, 0.3221305, 0.40212842, 0.08289475, 0.41320016, 0.41305315, 0.43551246, 0.28583407, 0.35271327, 0.43429467, 0.43764297, 0.42794212, 0.10367904, 0.4220683, 0.33364457, 0.29500842, 0.20349916, 0.44614236, 0.39764136, 0.1220008, 0.44833619, 0.47754036, 0.01773579, 0.0124085, 0.25297491, 0.23785651, 0.19689812, 0.34306998, 0.27047038, 0.11480802, 0.08017539, 0.26590814, 0.40486445, 0.08446188, 0.4535519, 0.2588784, 0.09570341] + }, + { + "type": "double", + "attributes": {}, + "value": [0.96298929] + }, + { + "type": "integer", + "attributes": {}, + "value": [1, 78, 167, 174, 186, 237, 486, 487] + } + ] + } + +# `fbplot()` works as expected for multivariate data + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["Depth", "Fvalue", "ID_outliers"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.0952381, 0.18571429, 0.26666667, 0.33809524, 0.4, 0.45238095, 0.4952381, 0.52857143, 0.55238095, 0.56666667, 0.57142857, 0.56666667, 0.55238095, 0.52857143, 0.4952381, 0.45238095, 0.4, 0.33809524, 0.26666667, 0.18571429, 0.0952381] + }, + { + "type": "double", + "attributes": {}, + "value": [3] + }, + { + "type": "integer", + "attributes": {}, + "value": [] + } + ] + } + +# `fbplot()` works for randomly generated multivariate data + + { + "type": "list", + "attributes": { + "names": { + "type": "character", + "attributes": {}, + "value": ["Depth", "Fvalue", "ID_outliers"] + } + }, + "value": [ + { + "type": "double", + "attributes": {}, + "value": [0.14345724, 0.41886869, 0.37811852, 0.1888633, 0.34543165, 0.42784108, 0.27882828, 0.37941279, 0.42894007, 0.31096296, 0.40013468, 0.29216027, 0.44789495, 0.46494276, 0.27443906, 0.44944377, 0.41166869, 0.3538303, 0.44499663, 0.04845118, 0.45154747, 0.44958249, 0.42211717, 0.42305455, 0.45173333, 0.08702896, 0.44257508, 0.20941414, 0.45265589, 0.37328754, 0.19676364, 0.31934007, 0.30053064, 0.37204444, 0.28934276, 0.25910034, 0.30412525, 0.40756768, 0.36152997, 0.39423704, 0.34783434, 0.37469764, 0.33186263, 0.35812795, 0.19602828, 0.37300337, 0.45065993, 0.10859259, 0.46736835, 0.33192054, 0.41109226, 0.33357576, 0.41260875, 0.3576835, 0.30891582, 0.35108552, 0.16064108, 0.26706667, 0.16541818, 0.47115286, 0.35508822, 0.16649293, 0.40502896, 0.37283502, 0.36127273, 0.27374276, 0.36073939, 0.19020067, 0.33977508, 0.41761886, 0.4115138, 0.48745993, 0.36871111, 0.43364848, 0.30495354, 0.38656162, 0.32835286, 0.18507205, 0.44089832, 0.39537104, 0.42852525, 0.47054411, 0.34441481, 0.42420337, 0.39881751, 0.3593468, 0.38677172, 0.27659798, 0.38469226, 0.23645253, 0.47510303, 0.39674613, 0.32961481, 0.37791246, 0.42756902, 0.2714303, 0.35122828, 0.22855084, 0.36893872, 0.30826801] + }, + { + "type": "double", + "attributes": {}, + "value": [2.5] + }, + { + "type": "integer", + "attributes": {}, + "value": [] + } + ] + } + diff --git a/tests/testthat/_snaps/outliergram.md b/tests/testthat/_snaps/outliergram.md new file mode 100644 index 0000000..95e1ea0 --- /dev/null +++ b/tests/testthat/_snaps/outliergram.md @@ -0,0 +1,61 @@ +# `outliergram()` works as expected + + WAoAAAACAAQBAQACAwAAAAITAAAAAwAAAA4AAAABP/gAAAAAAAAAAAAOAAAAzD+zCaEufl2M + P4gwDrl8QcA/eS21LarZAD88b7Af02gAP7xLABJcy+w/mPxsEs8VUD+iTynYcKMgP5Iz2RHw + nbA/ne7iJDEgID+7rwJ+VPfgP7ZQ5KwN8dg/sO2nkY4a0D+QtKqy/fhQP6BaKGFXQxg/etb2 + PJrvAD+HM63+pL3gP5dd9hQFeTA/vQnt9CC9bD+Msg13TmgAP1Knnnf1y8A/u7+2tvJZRD+u + Pmzfnz8IP6Sv5RJ7NiA/rtRPzqG/SD+g+zjSyV0IP2GOdt3uxuA/wruidAkI6D+SLLZsRW3Y + P6aLgBcOkmA/eIm2b4rowD+b60Qd85lwP7OgMpBeTvQ/opJY13RVMD+cbgOUGUSIP6X40MYK + C/A/kvu+K/TkQD+ZqORyHvgwP639pnGW03g/lOSDLUrFYD+Yc+bH+v+YP405UX2jiCA/pN0C + YLJasD9yaa202WVgP5kj26dF5YA/l6amZdSNkD+TUX1gHknwP5fICk15FhA/nx5h2FtT4D+2 + v9KeL+XMP64HrGlPxug/owGG1qEzMD+rvHdZRj9AP5JRZG37zMA/pV/NgcgJ2D+qZYL13DmQ + P6CXEc7uNyg/n2XbGz118D+DB1+1079AP43L0zrugAA/qJXBJVYgAD+9GcvxQBKoP11n7lUO + GcA/kLBcv1V5ED+fDwsB7X+QP6MaAswBXTg/gOE4jGU14D+pV0kFFS8gP3cpcpcpcqA/mMIB + SpC2ID+YZuqx6nuwP6jGQve3XDA/qgv7VeGb4D+r13ywZLp4P5kk3fbAcbA/roqqlj+GoD+Z + zWFZzfHwP7NUV+eY/JQ/lHa/aAIUsD+t4AV9owFQP7QAEzkv4pw/pY6nR6Y9iD+SFsFA4AXQ + P6AgvCQLRBA/opzCl7c+ID+vTgbKWWVoP13BoGoq/gA/q7ftwwvlkD+oEyNkHrhAP5fTaWZk + v7A/e2gCcmk1ID+QCZg310/wP4yhR75JASA/nO3nNuJ2YD+Qe1VEvkgAP4vsP4pOsLA/n6KB + 2abiuD+amkZBiNRwP5BLBy6HDig/sqrvieUymD8jd0C701eAP6BKr08ncfA/l0M6oixq8D+Z + gc73c9NQP6Sv0LG2PcQ/knmEe+DVkD+lPHSzP/yoP3GamMAdzeA/qjXOveIxWD+Q0xSjBKgQ + P5DIEnnijhA/ob82wSk3ID+6Ij1bp4BAP5tHnUmSVJA/i+zhl40eoD+cq+D5uRXAP6q1k0OA + riA/k3hR8/JZ0D+bKekXPxiwP7Gao5ZxL0A/laRrwLrOwD+oNEM79dV4P4PesmrzGuA/rdsB + OxRHMD+2TVDq8QPIP2H96bH3IiA/njmPD3BKAD/GVwrvDHeYP6IM2Md3jeA/mheXpdpK8D+a + 8NFSIgowP31haI0aOUA/mq95IB+MUD+ewYvnsRdQP8cZmlgsPx0/okIZvvKm4D+VQN8XkYT4 + P6bUS1nJoWg/sRXoQYpubD+ydyk531OQP7ArjT2FByA/mau6rj3FwD+z3V3dta2IP3jXnSuX + j6A/sqnAlTtfQD+91HLXORU8P6vs3DQquUg/nbRfsxpHkD+xh42WCle0P55gNDzVSwA/l9e5 + 8dDOMD+i2nt8yfUAP6ljRkeZ71g/wLkp55JSgj+8DHtgaNqUP4pJY13RVUA/liXDyJsi2D+U + CnH5ozKAP7xiqMaJgBQ/xvnsTai1kj+g75SHqrmQP6Uwxs4hK1g/oYr1Ga6M4D+hFjCsQlPo + P6il8t/1ZZg/h5NboMvIoD+wJ/WEBKYQP1R+bMULqoA/nx6bdFxmYD+o70SOsvUoP7x4hGbS + WLw/ugMcXNwOuD+JgNX5YBEgP6mGdc3EUug/ftD6KL0OoD+V/NBAd9WgP5/ZEdGAyxA/kN6w + 6J5I4D+SL88uFYnYP5yy3gbQqxA/nM5fLKkVMD+WytjZoxZYP6nWK1usOHA/Zwv/S61LgD+o + gJksoV24P5AALoJD1hA/p/WEBKYROD+US5hW70swP5OuT+mX2sA/txoAND3OMD+YvrlLzcPw + P7IBpsXQyCg/dE3N5m+OQD+Nlyc6wt4AP7i4dkKV/8A/X30OTW1gwD+ZJwLqA+mwP8A2+7jy + Dro/jZZBw6fo4D+tH3fn2XuwP28YBYygYaA/1vjXBdBfzD/XaXd8JVpeP8/nlNq36ZU/zdr9 + yYSNOAAAAA0AAAARAAAABQAAABIAAAAbAAAAPQAAAH8AAACGAAAAkQAAAJkAAACaAAAAngAA + AJ8AAACqAAAAxQAAAMkAAADKAAAAywAAAMwAAAQCAAAAAQAEAAkAAAAFbmFtZXMAAAAQAAAA + AwAEAAkAAAAGRnZhbHVlAAQACQAAAAFkAAQACQAAAAtJRF9vdXRsaWVycwAAAP4= + +# `outliergram()` correctly identifies outliers with Fvalue = 1.5) + + { + "type": "integer", + "attributes": {}, + "value": [5, 18, 27, 61, 127, 134, 145, 153, 154, 158, 159, 170, 197, 201, 202, 203, 204] + } + +# `outliergram()` correctly identifies outliers with Fvalue = 2.5) + + { + "type": "integer", + "attributes": {}, + "value": [27, 127, 134, 159, 201, 202, 203, 204] + } + +# `outliergram()` correctly identifies outliers with auto-adjusted Fvalue + + { + "type": "integer", + "attributes": {}, + "value": [127, 134, 159, 201, 202, 203, 204] + } + diff --git a/tests/testthat/_snaps/simulation.md b/tests/testthat/_snaps/simulation.md new file mode 100644 index 0000000..8e582ea --- /dev/null +++ b/tests/testthat/_snaps/simulation.md @@ -0,0 +1,112 @@ +# `generate_gauss_fdata()` works as expected using `Cov` argument + + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [30, 100] + } + }, + "value": [-0.38170771, 0.08773083, 0.34293041, -0.74177474, 0.13570114, 0.16002892, -0.18174873, -0.17286017, -0.17849539, -0.28145467, 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-0.28286465] + } + +# `generate_gauss_mfdata()` works as expected using `listCov` argument + + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [30, 100] + } + }, + "value": [-0.38170771, 0.08773083, 0.34293041, -0.74177474, 0.13570114, 0.16002892, -0.18174873, -0.17286017, -0.17849539, -0.28145467, -0.15090158, -0.31571752, -0.24547303, 0.02038367, 0.30341866, -0.03487534, -0.16159539, -0.28814529, -0.26473693, 0.76395416, 0.04240242, -0.1551685, -0.13931347, 0.14533494, -0.2193736, -0.4579626, 0.18175372, -0.32370836, -0.00478715, -0.29597294, -0.29537879, 0.14142756, 0.39141635, -0.6869193, 0.16613823, 0.19968354, -0.16173424, -0.13601516, -0.1206208, -0.22681817, -0.05806655, -0.27311527, -0.19872983, 0.07813141, 0.34626342, 0.00917075, -0.12008082, -0.24928031, -0.21129744, 0.81585932, 0.06947481, -0.10311979, -0.09787542, 0.18808464, -0.1587662, -0.38230802, 0.27789978, 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1.26327042, 2.10856361, 1.44290966, 1.41530941, 2.0289978, 2.04602258, 2.19432299, 2.49669742, 1.64415214, 1.76466659, 2.77506587, 2.62604398, 1.87546356, 2.45447553, 0.6810673, 2.11607143, 1.38681888, 0.79912673, 2.30258126, 2.99348911, 1.08401548, 1.86543242, 1.25684723, 2.44664544, 2.04975353, 1.39582389, 1.33358896, 2.83601866, 2.10421001, 2.2984924, 1.40743049, 2.20657122, 1.72128498, 1.5567962, 2.31010834, 2.08119202, 2.3433647, 2.64684185, 1.86827836, 1.88036261, 2.77642095, 2.71217624, 2.04879209, 2.48249631, 0.81449326, 2.36277278, 1.47763017, 0.82611033, 2.58130736, 3.09843996, 1.27715435, 1.81766548, 1.22650976, 2.63725642, 2.18822792, 1.51912063, 1.55281331, 2.90709627, 2.22954597, 2.39306578, 1.70268834, 2.31088802, 1.73861134, 1.70131794, 2.36362875, 2.11075054, 2.49134886, 2.65293123, 2.03202731, 1.91067136, 2.91760622, 2.89761597, 2.04081933, 2.72925811, 0.89993793, 2.47293735, 1.50889202, 0.86147717, 2.69978084, 3.15348984, 1.39119037, 1.89004438, 1.43927539, 2.73275486, 2.25978166, 1.68523518, 1.67432479, 2.99844972, 2.53045038, 2.50144186, 1.66432442, 2.34581422, 1.94163395, 1.69323142, 2.54985236, 2.2160938, 2.76859592, 2.83176414, 2.12820232, 2.05503566, 3.10918784, 2.97694732, 2.09475916, 2.81891376, 1.06069268, 2.593583, 1.57608057, 0.98719972, 2.84359096, 3.35321119, 1.4635138, 1.99444233, 1.56444264, 2.87490927, 2.31534159, 1.94207576, 1.9133223, 3.05044249, 2.62124817, 2.52741005, 1.83103838, 2.42625287, 2.07006816, 1.75203366, 2.50530234, 2.35617054, 2.85930008, 3.0131964, 2.20390873, 2.21096135, 3.2866677, 2.99330168, 2.21502732, 2.87627431, 1.10007181, 2.81587502, 1.7029076, 1.16857513, 2.9498494, 3.47610059, 1.56640208, 2.09930614, 1.82419706, 2.90567376, 2.42577546, 2.064323, 1.94941141, 3.19881709, 2.6441021, 2.69005303, 1.98967247, 2.57992363, 2.20784884, 1.72561303, 2.67432783, 2.46213777, 2.96625311, 3.09508652, 2.23530309, 2.38167651, 3.30139245, 3.10269475, 2.32664505, 2.9325304, 1.28881602, 2.92267912, 1.88450706, 1.05626325, 3.03354098, 3.60833538, 1.62303127, 2.32821741, 2.00447797, 3.04608041, 2.48404497, 2.03350155, 2.08129147, 3.12790397, 2.65419712, 2.84790044, 2.15924092, 2.71794505, 2.44984067, 1.78315071, 2.68140921, 2.58013604, 3.03168039, 3.11941076, 2.30882192, 2.55606296, 3.45333253, 3.28948934, 2.43439235, 3.09554356, 1.50534711, 2.90267467, 2.08123735, 1.16787968, 3.13813047, 3.73541356, 1.89754109, 2.49771248, 2.13344352, 3.35247917, 2.67469907, 2.10399839, 2.11674781, 3.27321037, 2.72367561, 2.91299191, 2.26787944, 2.7570177, 2.58310979, 1.92786373, 2.7440863, 2.69196134, 3.17014942, 3.18401405, 2.53095446, 2.60051978, 3.678798, 3.39738819, 2.59604167, 3.30817501, 1.64220033, 2.94800969, 2.23674278, 1.26778416, 3.35610056, 3.79797767, 1.94213995, 2.53460543, 2.31590624, 3.50554531, 2.800309, 2.36284648, 2.2865963, 3.42823894, 2.76295549, 3.08160995, 2.3657634, 2.91810547, 2.69128497, 2.03513325, 2.94496608, 2.94269906, 3.31108145, 3.3634999, 2.54082304, 2.71695827, 3.813142, 3.5754477, 2.62656937, 3.43764323, 1.78396826, 3.08769037, 2.28514493, 1.53562685, 3.69336832, 3.73219391, 2.18476833, 2.6780028, 2.54048799, 3.64234421, 2.92302149, 2.35433729, 2.4141515, 3.41988561, 2.79386412, 3.16243288, 2.50248061, 3.03829341, 2.78803903, 2.06563124, 3.06634286, 2.95370293, 3.4243942, 3.41185482, 2.60248933, 2.8214818, 3.79043409, 3.71610265, 2.91718811, 3.49708294, 1.99751803, 3.23931603, 2.38450617, 1.65666854, 3.82112562, 3.78251959, 2.31829115, 2.74612325, 2.67664172, 3.89277695, 3.00346966, 2.4739083, 2.4862322, 3.45250994, 2.85547689, 3.21740578, 2.53375306, 3.2294388, 2.8998852, 2.03832218, 3.18682836, 3.01846111, 3.53133147, 3.50242551, 2.78157063, 2.84408033, 3.92761406, 3.75871354, 3.15724267, 3.53240724, 2.10262628, 3.43451563, 2.43263064, 1.77525952, 3.78973885, 3.94500561, 2.42556519, 2.90684776, 2.89588434, 4.20467653, 3.21909671, 2.7793309, 2.54105787, 3.61196328, 3.10692876, 3.33600844, 2.68804545, 3.15419455, 2.92375733, 2.17086813, 3.33143328, 3.11482634, 3.64585297, 3.60114913, 2.92475873, 2.99608922, 3.98215838, 3.82385679, 3.3175679, 3.63388607, 2.25109457, 3.64855816, 2.62906714, 1.96312973, 3.9681833, 4.03793848, 2.6666361, 3.04784837, 2.99253316, 4.34648159, 3.41938445, 2.93698314, 2.7432916, 3.75236669, 3.14030957, 3.43331195, 2.77623587, 3.27264966, 2.91890599, 2.35967629, 3.48624286, 3.25448896, 3.91874557, 3.80349379, 2.9773228, 3.21578561, 4.16592778, 3.83955146, 3.50484768, 3.74766666, 2.45505777, 3.68820421, 2.81092443, 2.11005851, 4.1484348, 4.22427615, 2.60821623, 3.20958735, 3.08951482, 4.52110195, 3.48485666, 3.08792273, 2.94927563, 3.76848466, 3.29752869, 3.64104874, 2.88762573, 3.29867765, 3.02360119, 2.46172393, 3.61267552, 3.29276598, 3.98873683, 3.81833643, 3.14285616, 3.34604441, 4.3083458, 4.03867937, 3.64776394, 3.88080992, 2.64427973, 3.86054837, 2.92004515, 2.22597159, 4.23898406, 4.22785964, 2.65238741, 3.28377799, 3.25335709, 4.56031955, 3.58064577, 3.20496231, 3.21103155, 3.91783488, 3.4000862, 3.78073979, 3.09940385, 3.45077212, 3.27811953, 2.65469301, 3.56141068, 3.50128555, 4.00582251, 3.91939457, 3.30107233, 3.42510253, 4.38543527, 4.13518947, 3.63429537, 4.08095478, 2.83511058, 4.00755763, 3.15080697, 2.43144667, 4.28731936, 4.28011384, 2.79259656, 3.14231486, 3.40679051, 4.57594675, 3.62371495, 3.49635251, 3.34885333, 3.98811004, 3.716658, 3.98520476, 3.26396812, 3.498657, 3.39610416, 2.80949692, 3.66979105, 3.71089484, 4.05891725, 4.0104823, 3.42851636, 3.4552397, 4.35919091, 4.19650661, 3.75474409, 4.21510249, 2.8649356, 4.02248401, 3.41970506, 2.57502485, 4.34589728, 4.43820882, 2.82818539, 3.33723885, 3.58611024, 4.71626905, 3.79078899, 3.64092872, 3.40257419, 4.17937154, 3.77641288, 4.03055035, 3.31947142, 3.6791557, 3.63444432, 3.12109656, 3.78378732, 3.83130097, 4.21074579, 4.16535799, 3.60100288, 3.49561157, 4.39938703, 4.36222268, 3.81992684, 4.35158483, 2.98585274, 4.21387761, 3.57458057, 2.70598743, 4.32472216, 4.69960015, 2.86991899, 3.53291877, 3.7865724, 4.83797414, 4.07189182, 3.65991001, 3.61089077, 4.3901563, 3.802257, 4.09692161, 3.3398658, 3.95197862, 3.667227, 3.18707132, 3.9033516, 4.02795068, 4.37562548, 4.49350698, 3.65173184, 3.7658205, 4.51917884, 4.53377751, 4.10077299, 4.5470673, 3.11283333, 4.1929141, 3.70971882, 2.8615887, 4.42828296, 4.80286143, 2.93252905, 3.74346713, 3.90005529, 5.03295019, 4.08180541, 3.77915496, 3.69037458, 4.50847557, 3.92687187, 4.34418108, 3.4744865, 4.18874463, 3.82581176, 3.3568067, 4.17784846, 4.29683184, 4.50567144, 4.7162543, 3.71670085, 3.90742736, 4.68102276, 4.63010917, 4.15369766, 4.56211101, 3.26429614, 4.31563113, 3.90221797, 2.92795703, 4.58239803, 4.95819514, 3.06704986, 3.83058273, 4.03055756, 5.1662704, 4.13850242, 3.85894533, 3.8447728, 4.60551429, 4.08868727, 4.49038119, 3.61380638, 4.31232045, 3.9505757, 3.4516606, 4.27454231, 4.45184745, 4.44629095, 4.76063939, 3.74577172, 4.09680958, 4.85865412, 4.79916763, 4.28680564, 4.71031644, 3.51611431, 4.37246532, 3.98932102, 3.18089165, 4.62608214, 5.07041724, 3.24735382, 3.98339879, 4.108936, 5.26860023, 4.23130008, 3.97194312, 4.06706888, 4.80532539, 4.22748761, 4.67565238, 3.81354612, 4.43492574, 3.95735817, 3.52260274, 4.5155636, 4.66701212, 4.52074111, 4.78595323, 3.8383681, 4.38366751, 5.06712373, 5.05407641, 4.44812879, 4.8247801, 3.57135643, 4.460714, 4.17247102, 3.40315432, 4.79319979, 5.20966842, 3.38611436, 4.06236998, 4.29814596, 5.3851864, 4.46403125, 4.02725412, 4.25219531, 4.91639052, 4.40040795, 4.85343497, 4.01293619, 4.56901832, 4.1073516, 3.63094462, 4.66041075, 4.73128294, 4.66545399, 4.9508134, 4.00339188, 4.44010072, 5.23537188, 5.1407278, 4.63880825, 5.03285512, 3.6558794, 4.56258143, 4.42917533, 3.38025062, 4.8970613, 5.34168301, 3.56979799, 4.13116251, 4.39883597, 5.58590141, 4.59883407, 4.14631337, 4.50867689, 5.01289037, 4.40882732, 5.01906193, 4.12560076, 4.79160004, 4.27860155, 3.84168503, 4.7514401, 4.88440452, 4.76591392, 5.16224439, 4.13027321, 4.53286125, 5.43405173, 5.28114352, 4.82659439, 5.14583346, 3.84480058, 4.69821758, 4.67809508, 3.4781035, 5.07274603, 5.46318178, 3.73248147, 4.28331593, 4.49089106, 5.71012399, 4.73508702, 4.2526726, 4.64829798, 5.18545147, 4.60947776, 5.19498162, 4.21663033, 5.10506643, 4.4309281, 4.09809527, 5.04825714, 5.02623745, 4.90570724, 5.23345658, 4.28651486, 4.64773418, 5.58471, 5.43792131, 4.92575431, 5.23425976, 3.88156797, 4.82133415, 4.91900278, 3.56344684, 5.16240314, 5.4649779, 3.87672406, 4.41702635, 4.67834916, 5.99236212, 4.85803377, 4.42106211, 4.81640422, 5.2961094, 4.7273757, 5.32298669, 4.36556136, 5.23785995, 4.4912926, 4.33677784, 5.23355216, 5.26806864, 4.94743015, 5.4792473, 4.35933637, 4.76501607, 5.64254819, 5.50564111, 5.08153862, 5.48177783, 3.91779273, 4.92632166, 5.03339436, 3.66169523, 5.19502886, 5.56670812, 4.08505421, 4.63356932, 4.83058853, 6.21464288, 4.95502279, 4.63331116, 4.91068483, 5.4041951, 4.86569926, 5.66991581, 4.49977387, 5.36612655, 4.70919296, 4.5302755, 5.41691227, 5.52308024, 5.18118271, 5.61676921, 4.57141712, 5.0270776, 5.77105476, 5.61048912, 5.27114356, 5.59622143, 4.03718563, 5.08197799, 5.09255794, 3.86802753, 5.31096938, 5.67189108, 4.27880003, 4.81775647, 5.00244989, 6.15315874, 5.17544202, 4.79751691, 4.92618642, 5.51390244, 5.09505839, 5.81544353, 4.65870023, 5.55845161, 4.88728979] + } + ] + } + +# `generate_gauss_mfdata()` works as expected using `listCholCov` argument + + { + "type": "list", + "attributes": {}, + "value": [ + { + "type": "double", + "attributes": { + "dim": { + "type": "integer", + "attributes": {}, + "value": [30, 100] + } + }, + "value": [-0.38170771, 0.08773083, 0.34293041, -0.74177474, 0.13570114, 0.16002892, -0.18174873, -0.17286017, -0.17849539, -0.28145467, -0.15090158, -0.31571752, -0.24547303, 0.02038367, 0.30341866, -0.03487534, -0.16159539, -0.28814529, -0.26473693, 0.76395416, 0.04240242, -0.1551685, -0.13931347, 0.14533494, -0.2193736, -0.4579626, 0.18175372, -0.32370836, -0.00478715, -0.29597294, 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4.94743015, 5.4792473, 4.35933637, 4.76501607, 5.64254819, 5.50564111, 5.08153862, 5.48177783, 3.91779273, 4.92632166, 5.03339436, 3.66169523, 5.19502886, 5.56670812, 4.08505421, 4.63356932, 4.83058853, 6.21464288, 4.95502279, 4.63331116, 4.91068483, 5.4041951, 4.86569926, 5.66991581, 4.49977387, 5.36612655, 4.70919296, 4.5302755, 5.41691227, 5.52308024, 5.18118271, 5.61676921, 4.57141712, 5.0270776, 5.77105476, 5.61048912, 5.27114356, 5.59622143, 4.03718563, 5.08197799, 5.09255794, 3.86802753, 5.31096938, 5.67189108, 4.27880003, 4.81775647, 5.00244989, 6.15315874, 5.17544202, 4.79751691, 4.92618642, 5.51390244, 5.09505839, 5.81544353, 4.65870023, 5.55845161, 4.88728979] + } + ] + } + diff --git a/tests/testthat/helper-outliergram.R b/tests/testthat/helper-outliergram.R new file mode 100644 index 0000000..d1a942f --- /dev/null +++ b/tests/testthat/helper-outliergram.R @@ -0,0 +1,38 @@ +withr::local_seed(1234) + +N <- 200 +N_outliers <- 4 +P <- 200 +grid <- seq(0, 1, length.out = P) +Cov = exp_cov_function(grid, alpha = 0.2, beta = 0.8) + +Data <- generate_gauss_fdata( + N, + centerline = sin(4 * pi * grid), + Cov = Cov +) + +Data_out <- array(0, dim = c(N_outliers, P)) +Data_out[1, ] <- generate_gauss_fdata( + 1, + centerline = sin(4 * pi * grid + pi / 2), + Cov = Cov +) +Data_out[2, ] <- generate_gauss_fdata( + 1, + centerline = sin(4 * pi * grid - pi / 2), + Cov = Cov +) +Data_out[3, ] <- generate_gauss_fdata( + 1, + centerline = sin(4 * pi * grid + pi/ 3), + Cov = Cov +) +Data_out[4, ] <- generate_gauss_fdata( + 1, + centerline = sin(4 * pi * grid - pi / 3), + Cov = Cov +) + +Data <- rbind(Data, Data_out) +fDout <- fData(grid, Data) diff --git a/tests/testthat/helper-restyling.R b/tests/testthat/helper-restyling.R new file mode 100644 index 0000000..73a56ab --- /dev/null +++ b/tests/testthat/helper-restyling.R @@ -0,0 +1,8 @@ +withr::local_seed(1234) +N <- 100 +P <- 100 +grid <- seq(0, 1, length.out = P) +centerline <- sin(2 * pi * grid) +Cov <- exp_cov_function(grid, alpha = 0.2, beta = 0.3) +Data_restyling <- generate_gauss_fdata(N, centerline, Cov) +fD_restyling <- fData(grid, Data_restyling) diff --git a/tests/testthat/helper-simulation.R b/tests/testthat/helper-simulation.R new file mode 100644 index 0000000..9570929 --- /dev/null +++ b/tests/testthat/helper-simulation.R @@ -0,0 +1,22 @@ +N <- 30 +P <- 100 +L <- 3 + +t0 <- 0 +tP <- 1 +time_grid <- seq(t0, tP, length.out = P) + +C1 <- exp_cov_function(time_grid, alpha = 0.1, beta = 0.2) +C2 <- exp_cov_function(time_grid, alpha = 0.2, beta = 0.5) +C3 <- exp_cov_function(time_grid, alpha = 0.3, beta = 1) + +CholC1 <- chol(C1) +CholC2 <- chol(C2) +CholC3 <- chol(C3) + +centerline <- sin(2 * pi * time_grid) +centerlines <- matrix(c( + centerline, + sqrt(time_grid), + 10 * (time_grid - 0.5) * time_grid + ), nrow = 3, byrow = TRUE) diff --git a/tests/testthat/test-BD.R b/tests/testthat/test-BD.R new file mode 100644 index 0000000..d32839a --- /dev/null +++ b/tests/testthat/test-BD.R @@ -0,0 +1,23 @@ +test_that("BD function works as expected.", { + # Arrange + time_grid <- seq(0, 1, length.out = 1e2) + + D <- matrix(data = c( + 1 + sin(2 * pi * time_grid), + 0 + sin(4 * pi * time_grid), + 1 - sin(pi * (time_grid - 0.2)), + 0.1 + cos(2 * pi * time_grid), + 0.5 + sin(3 * pi + time_grid), + -2 + sin(pi * time_grid)), + nrow = 6, + ncol = length(time_grid), + byrow = TRUE + ) + + # Act + actual <- BD(D) + + # Assert + expected <- c(1/3, 1/3, 1/3, 1/3, 14/30, 1/3) + expect_equal(actual, expected) +}) diff --git a/tests/testthat/test-BD_relative.R b/tests/testthat/test-BD_relative.R new file mode 100644 index 0000000..ec1ab63 --- /dev/null +++ b/tests/testthat/test-BD_relative.R @@ -0,0 +1,144 @@ +test_that("it handles properly a single test function in row matrix form.", { + # Arrange + time_grid <- seq(0, 1, length.out = 1e2) + + Data_ref <- matrix(c( 0 + sin( 2 * pi * time_grid ), + 1 + sin( 2 * pi * time_grid ), + -1 + sin( 2 * pi * time_grid ) + ), + nrow = 3, ncol = length( time_grid ), byrow = TRUE ) + + Data_test <- matrix( c( 0.6 + sin( 2 * pi * time_grid ) ), + nrow = 1, ncol = length( time_grid ), byrow = TRUE ) + + # Act + actual <- BD_relative(Data_test, Data_ref) + + # Assert + expected <- 2/3 + expect_equal(actual, expected) +}) + +test_that("it handles properly a single test function in column matrix form.", { + # Arrange + time_grid <- seq(0, 1, length.out = 1e2) + + Data_ref <- matrix(c( 0 + sin( 2 * pi * time_grid ), + 1 + sin( 2 * pi * time_grid ), + -1 + sin( 2 * pi * time_grid ) + ), + nrow = 3, ncol = length( time_grid ), byrow = TRUE ) + + Data_test <- matrix( c( 0.6 + sin( 2 * pi * time_grid ) ), + nrow = length( time_grid ), ncol = 1, byrow = TRUE ) + + # Act + actual <- BD_relative(Data_test, Data_ref) + + # Assert + expected <- 2/3 + expect_equal(actual, expected) +}) + +test_that("it handles properly a single test function in vector form.", { + # Arrange + time_grid <- seq(0, 1, length.out = 1e2) + + Data_ref <- matrix(c( 0 + sin( 2 * pi * time_grid ), + 1 + sin( 2 * pi * time_grid ), + -1 + sin( 2 * pi * time_grid ) + ), + nrow = 3, ncol = length( time_grid ), byrow = TRUE ) + + Data_test <- 0.6 + sin( 2 * pi * time_grid ) + + # Act + actual <- BD_relative(Data_test, Data_ref) + + # Assert + expected <- 2/3 + expect_equal(actual, expected) +}) + +test_that("it handles properly a single test function in 1D array form.", { + # Arrange + time_grid <- seq(0, 1, length.out = 1e2) + + Data_ref <- matrix(c( 0 + sin( 2 * pi * time_grid ), + 1 + sin( 2 * pi * time_grid ), + -1 + sin( 2 * pi * time_grid ) + ), + nrow = 3, ncol = length( time_grid ), byrow = TRUE ) + + Data_test <- array( 0.6 + sin( 2 * pi * time_grid ), dim = length( time_grid ) ) + + # Act + actual <- BD_relative(Data_test, Data_ref) + + # Assert + expected <- 2/3 + expect_equal(actual, expected) +}) + +test_that("it handles properly a single test function in row-like 2D array form.", { + # Arrange + time_grid <- seq(0, 1, length.out = 1e2) + + Data_ref <- matrix(c( 0 + sin( 2 * pi * time_grid ), + 1 + sin( 2 * pi * time_grid ), + -1 + sin( 2 * pi * time_grid ) + ), + nrow = 3, ncol = length( time_grid ), byrow = TRUE ) + + Data_test <- array( 0.6 + sin( 2 * pi * time_grid ), dim = c( 1, length( time_grid ) ) ) + + # Act + actual <- BD_relative(Data_test, Data_ref) + + # Assert + expected <- 2/3 + expect_equal(actual, expected) +}) + +test_that("it handles properly a single test function in column-like 2D array form.", { + # Arrange + time_grid <- seq(0, 1, length.out = 1e2) + + Data_ref <- matrix(c( 0 + sin( 2 * pi * time_grid ), + 1 + sin( 2 * pi * time_grid ), + -1 + sin( 2 * pi * time_grid ) + ), + nrow = 3, ncol = length( time_grid ), byrow = TRUE ) + + Data_test <- array( 0.6 + sin( 2 * pi * time_grid ), dim = c( length( time_grid ), 1 ) ) + + # Act + actual <- BD_relative(Data_test, Data_ref) + + # Assert + expected <- 2/3 + expect_equal(actual, expected) +}) + +test_that("it handles properly multiple test function.", { + # Arrange + time_grid <- seq(0, 1, length.out = 1e2) + + Data_ref <- matrix(c( 0 + sin( 2 * pi * time_grid ), + 1 + sin( 2 * pi * time_grid ), + -1 + sin( 2 * pi * time_grid ) + ), + nrow = 3, ncol = length( time_grid ), byrow = TRUE ) + + Data_test <- matrix( c( 0.5 + sin( 2 * pi * time_grid ), + -0.5 + sin( 2 * pi * time_grid ), + 1.1 + sin( 2 * pi * time_grid ) ), + nrow = 3, ncol = length( time_grid ), byrow = TRUE ) + + # Act + actual <- BD_relative(Data_test, Data_ref) + + # Assert + expected <- c(2/3, 2/3, 0) + expect_equal(actual, expected) +}) diff --git a/tests/testthat/test-EI_and_MEI.R b/tests/testthat/test-EI_and_MEI.R new file mode 100644 index 0000000..ba92205 --- /dev/null +++ b/tests/testthat/test-EI_and_MEI.R @@ -0,0 +1,23 @@ +test_that("ei() & mei() work as expected", { + N <- 2 + time_grid <- seq(0, 1, length.out = N * 1e2) + Data <- matrix(0, nrow = N, ncol = length(time_grid)) + for (iObs in 1:N) + Data[iObs, ] <- as.numeric(time_grid >= (iObs - 1) / N & time_grid < iObs / N) + Data[N, length(time_grid)] <- 1 + + expect_equal(EI(Data), rep(1 / N, N)) + expect_equal(MEI(Data), rep(1 - ( N - 1 ) / N^2, N)) +}) + +test_that("ei() works on test by James Long (TAMU)", { + yints <- c( 1.27, .927, 1/2, .217, 0) + slopes <- c( -1, -1, 0, 1, 1 ) + time_grid <- (0:100) / 100 + + Data <- matrix(0, nrow = length(yints), ncol = length( time_grid)) + for (i in 1:length(yints)) + Data[i, ] <- yints[i] + time_grid * slopes[i] + + expect_equal(EI(Data), c(0.2, 0.4, 0.2, 0.2, 0.4)) +}) diff --git a/tests/testthat/test-HI_and_MHI.R b/tests/testthat/test-HI_and_MHI.R new file mode 100644 index 0000000..2364d45 --- /dev/null +++ b/tests/testthat/test-HI_and_MHI.R @@ -0,0 +1,24 @@ +test_that("hi() & mhi() work as expected", { + N <- 20 + time_grid <- seq(0, 1, length.out = N * 1e2) + + Data <- matrix(0, nrow = N, ncol = length(time_grid)) + for (iObs in 1:N) + Data[iObs, ] <- as.numeric(time_grid >= (iObs - 1) / N & time_grid < iObs / N) + Data[N, length(time_grid)] <- 1 + + expect_equal(HI(Data), rep(1 / N, N)) + expect_equal(MHI(Data), rep((N + (N - 1)^2) / N^2 , N)) +}) + +test_that("hi() works on test by James Long (TAMU)", { + yints <- c( 1.27, .927, 1/2, .217, 0) + slopes <- c(-1, -1, 0, 1, 1) + time_grid <- (0:100) / 100 + + Data <- matrix(0, nrow = length(yints), ncol = length(time_grid)) + for (i in 1:length(yints)) + Data[i, ] <- yints[i] + time_grid * slopes[i] + + expect_equal(HI(Data), c(0.4, 0.2, 0.2, 0.4, 0.2)) +}) diff --git a/tests/testthat/test-HRD_and_MHRD.R b/tests/testthat/test-HRD_and_MHRD.R new file mode 100644 index 0000000..092b5c4 --- /dev/null +++ b/tests/testthat/test-HRD_and_MHRD.R @@ -0,0 +1,46 @@ +test_that("hrd() & mhrd() work as expected", { + time_grid <- seq(0, 1, length.out = 1e2) + + D <- matrix(c( + sin(2 * pi * time_grid) + 10, + sin(2 * pi * time_grid) + 9, + sin(2 * pi * time_grid) + 8, + sin(2 * pi * time_grid) + 7, + sin(2 * pi * time_grid) + 6, + sin(2 * pi * time_grid) + 5, + sin(2 * pi * time_grid) + 4, + sin(2 * pi * time_grid) + 3, + sin(2 * pi * time_grid) + 2, + sin(2 * pi * time_grid) + 1, + sin(2 * pi * time_grid) + 0, + sin(2 * pi * time_grid) - 1, + sin(2 * pi * time_grid) - 2, + sin(2 * pi * time_grid) - 3, + sin(2 * pi * time_grid) - 4, + sin(2 * pi * time_grid) - 5, + sin(2 * pi * time_grid) - 6, + sin(2 * pi * time_grid) - 7, + sin(2 * pi * time_grid) - 8, + sin(2 * pi * time_grid) - 9, + sin(2 * pi * time_grid) - 10), + nrow = 21, ncol = length(time_grid), byrow = TRUE + ) + + N <- nrow(D) + id_vector <- 1:N + + expect_equal(HRD(D), mapply(min, id_vector / N, (N - id_vector + 1) / N)) + expect_equal(MHRD(D), mapply(min, id_vector / N, (N - id_vector + 1) / N)) +}) + +test_that("hrd() works on test by James Long (TAMU)", { + yints <- c( 1.27, .927, 1/2, .217, 0) + slopes <- c(-1, -1, 0, 1, 1) + time_grid <- (0:100) / 100 + + Data <- matrix(0, nrow = length(yints), ncol = length(time_grid)) + for (i in 1:length(yints)) + Data[i, ] <- yints[i] + time_grid * slopes[i] + + expect_equal(HRD(Data), rep(0.2, 5)) +}) diff --git a/tests/testthat/test-MBD_relative.R b/tests/testthat/test-MBD_relative.R new file mode 100644 index 0000000..c2cc8c1 --- /dev/null +++ b/tests/testthat/test-MBD_relative.R @@ -0,0 +1,104 @@ +test_that("`MBD_relative()` works for a single test function in row matrix form", { + time_grid <- seq(0, 1, length.out = 1e2) + Data_ref <- matrix(c( + 0 + sin(2 * pi * time_grid), + 1 + sin(2 * pi * time_grid), + -1 + sin(2 * pi * time_grid) + ), nrow = 3, ncol = length(time_grid), byrow = TRUE) + + Data_test_1 <- matrix(c( + 0.6 + sin(2 * pi * time_grid) + ), nrow = 1, ncol = length(time_grid), byrow = TRUE) + + expect_equal(MBD_relative(Data_test_1, Data_ref), 2/3) +}) + +test_that("`MBD_relative()` works for a single test function in column matrix form", { + time_grid <- seq(0, 1, length.out = 1e2) + Data_ref <- matrix(c( + 0 + sin(2 * pi * time_grid), + 1 + sin(2 * pi * time_grid), + -1 + sin(2 * pi * time_grid) + ), nrow = 3, ncol = length(time_grid), byrow = TRUE) + + Data_test_2 <- matrix(c( + 0.6 + sin(2 * pi * time_grid) + ), nrow = length(time_grid), ncol = 1, byrow = TRUE) + + expect_equal(MBD_relative(Data_test_2, Data_ref), 2/3) +}) + +test_that("`MBD_relative()` works for a single test function in vector form", { + time_grid <- seq(0, 1, length.out = 1e2) + Data_ref <- matrix(c( + 0 + sin(2 * pi * time_grid), + 1 + sin(2 * pi * time_grid), + -1 + sin(2 * pi * time_grid) + ), nrow = 3, ncol = length(time_grid), byrow = TRUE) + + Data_test_3 <- 0.6 + sin(2 * pi * time_grid) + + expect_equal(MBD_relative(Data_test_3, Data_ref), 2/3) +}) + +test_that("`MBD_relative()` works for a single test function in 1D array form", { + time_grid <- seq(0, 1, length.out = 1e2) + Data_ref <- matrix(c( + 0 + sin(2 * pi * time_grid), + 1 + sin(2 * pi * time_grid), + -1 + sin(2 * pi * time_grid) + ), nrow = 3, ncol = length(time_grid), byrow = TRUE) + + Data_test_4 <- array(0.6 + sin(2 * pi * time_grid), dim = length(time_grid)) + + expect_equal(MBD_relative(Data_test_4, Data_ref), 2/3) +}) + +test_that("`MBD_relative()` works for a single test function in row-like 2D array form", { + time_grid <- seq(0, 1, length.out = 1e2) + Data_ref <- matrix(c( + 0 + sin(2 * pi * time_grid), + 1 + sin(2 * pi * time_grid), + -1 + sin(2 * pi * time_grid) + ), nrow = 3, ncol = length(time_grid), byrow = TRUE) + + Data_test_5 <- array( + 0.6 + sin(2 * pi * time_grid), + dim = c(1, length(time_grid)) + ) + + expect_equal(MBD_relative(Data_test_5, Data_ref), 2/3) +}) + +test_that("`MBD_relative()` works for a single test function in column-like 2D array form", { + time_grid <- seq(0, 1, length.out = 1e2) + Data_ref <- matrix(c( + 0 + sin(2 * pi * time_grid), + 1 + sin(2 * pi * time_grid), + -1 + sin(2 * pi * time_grid) + ), nrow = 3, ncol = length(time_grid), byrow = TRUE) + + Data_test_6 <- array( + 0.6 + sin(2 * pi * time_grid), + dim = c(length(time_grid), 1) + ) + + expect_equal(MBD_relative(Data_test_6, Data_ref), 2/3) +}) + +test_that("`MBD_relative()` works for multiple test functions", { + time_grid <- seq(0, 1, length.out = 1e2) + Data_ref <- matrix(c( + 0 + sin(2 * pi * time_grid), + 1 + sin(2 * pi * time_grid), + -1 + sin(2 * pi * time_grid) + ), nrow = 3, ncol = length(time_grid), byrow = TRUE) + + Data_test_7 <- matrix(c( + 0.5 + sin(2 * pi * time_grid), + -0.5 + sin(2 * pi * time_grid), + 1.1 + sin(2 * pi * time_grid) + ), nrow = 3, ncol = length(time_grid), byrow = TRUE) + + expect_equal(MBD_relative(Data_test_7, Data_ref), c(2/3, 2/3, 0)) +}) diff --git a/tests/testthat/test-MBD_with_tied_data.R b/tests/testthat/test-MBD_with_tied_data.R new file mode 100644 index 0000000..1ddea3c --- /dev/null +++ b/tests/testthat/test-MBD_with_tied_data.R @@ -0,0 +1,52 @@ +test_that("`MBD()` works as expected in presence of ties", { + # Arrange + D <- matrix(c( + c(1, 0.5, 0.25, 0.1, 0.05), + c(1, 0.75, 0.25, 0.2, 0.1), + c(1, 0.7, 0.25, 0.25, 0.15), + c(1, 0.9, 0.35, 0.3, 0.25), + c(1, 0.6, 0.25, 0.2, 0.2) , + c(0.9, 0.8, 0.25, 0.1, 0.08), + c(1, 0.4, 0.3, 0.2, 0.1), + c(1, 0.4, 0.3, 0.2, 0.1) + ), ncol = 5, nrow = 8, byrow = TRUE) + + # Act + N <- nrow(D) + P <- ncol(D) + depths <- rep(0, N) + for (i in 1:N) + { + for (j in 1:(N - 1)) + { + for (k in (j + 1):N) + { + for (r in 1:P) + { + if ((D[j, r] - D[i, r]) * (D[k, r] - D[i, r]) <= 0) + depths[i] <- depths[i] + 1 + } + } + } + } + depths <- depths / (N * (N - 1) / 2 * P) + + # Assert + expect_equal(depths, MBD(D, manage_ties = TRUE)) +}) + +test_that("`MBD()` works as expected without checking for ties", { + N <- 3 + P <- 1e2 + time_grid <- seq(0, 1, length.out = P) + Data <- matrix(c( + 0 + sin(2 * pi * time_grid), + 1 + sin(2 * pi * time_grid), + -1 + sin(2 * pi * time_grid) + ), nrow = 3, ncol = length(time_grid), byrow = TRUE) + + expect_equal( + MBD(Data, manage_ties = TRUE), + MBD(Data, manage_ties = FALSE) + ) +}) diff --git a/tests/testthat/test-correlation.R b/tests/testthat/test-correlation.R new file mode 100644 index 0000000..13d8f01 --- /dev/null +++ b/tests/testthat/test-correlation.R @@ -0,0 +1,469 @@ +# maxima() & minima() ----------------------------------------------------- + +test_that("maxima() works for functional data when `which = TRUE`", { + # Arrange + P <- 1e4 + time_grid <- seq(0, 1, length.out = P) + h <- time_grid[2] - time_grid[1] + Data <- matrix(c(1 * time_grid, + 2 * time_grid, + 3 * ( 0.5 - abs( time_grid - 0.5))), + nrow = 3, ncol = P, byrow = TRUE) + fD <- fData(time_grid, Data) + + # Act + actual <- maxima(fD, which = TRUE) + + # Assert + expected_grid <- c(1, 1, 0 + 4999 * h) + expected_value <- c(1, 2, 3 * ( 0.5 - abs( 0.5 - 4999 * h))) + expect_equal(actual$grid, expected_grid) + expect_equal(actual$value, expected_value) +}) + +test_that("minima() works for functional data when `which = TRUE`", { + # Arrange + P <- 1e4 + time_grid <- seq(0, 1, length.out = P) + Data <- matrix(c(1 * time_grid, + 2 * time_grid, + 3 * ( 0.5 - abs( time_grid - 0.5))), + nrow = 3, ncol = P, byrow = TRUE) + fD <- fData(time_grid, Data) + + # Act + actual <- minima(fD, which = TRUE) + + # Assert + expected_grid <- rep(0, 3) + expected_value <- rep(0, 3) + expect_equal(actual$grid, expected_grid) + expect_equal(actual$value, expected_value) +}) + +test_that("maxima() works for functional data when `which = FALSE`", { + # Arrange + P <- 1e4 + time_grid <- seq(0, 1, length.out = P) + h <- time_grid[2] - time_grid[1] + Data <- matrix(c(1 * time_grid, + 2 * time_grid, + 3 * ( 0.5 - abs( time_grid - 0.5))), + nrow = 3, ncol = P, byrow = TRUE) + fD <- fData(time_grid, Data) + + # Act + actual <- maxima(fD, which = FALSE) + + # Assert + expected <- c(1, 2, 3 * (0.5 - abs( 0.5 - 4999 * h))) + expect_equal(actual, expected) +}) + +test_that("minima() works for functional data when `which = FALSE`", { + # Arrange + P <- 1e4 + time_grid <- seq(0, 1, length.out = P) + Data <- matrix(c(1 * time_grid, + 2 * time_grid, + 3 * ( 0.5 - abs( time_grid - 0.5))), + nrow = 3, ncol = P, byrow = TRUE) + fD <- fData(time_grid, Data) + + # Act + actual <- minima(fD, which = FALSE) + + # Assert + expected <- rep(0, 3) + expect_equal(actual, expected) +}) + +# area_under_curve() ------------------------------------------------------ + +test_that("area_under_curve() works for functional data", { + # Arrange + P <- 1e4 + time_grid <- seq(0, 1, length.out = P) + fD_1 <- fData(time_grid, + matrix(c(1 * time_grid, + 2 * time_grid, + 3 * ( 0.5 - abs( time_grid - 0.5))), + nrow = 3, ncol = P, byrow = TRUE)) + fD_2 <- fData(time_grid, + matrix(c(sin(2 * pi * time_grid), + cos(2 * pi * time_grid), + 4 * time_grid * (1 - time_grid)), + nrow = 3, ncol = P, byrow = TRUE)) + + # Act + actual <- area_under_curve(fD_1) + + # Assert + expected <- c(0.5, 1, 0.75) + expect_equal(actual, expected) + expect_true(all(c( + area_under_curve(fD_2)[1:2], + abs(area_under_curve(fD_2[3, ]) - 2/3) + ) <= .Machine$double.eps^0.5)) +}) + +# Ordering functions ------------------------------------------------------ + +test_that("max_ordered() works as expected", { + # Arrange + P <- 1e3 + time_grid <- seq(0, 1, length.out = P) + h <- time_grid[2] - time_grid[1] + + Data_1 <- matrix( + c(1 * time_grid, 2 * time_grid), + nrow = 2, ncol = P, byrow = TRUE + ) + Data_2 <- matrix( + 3 * (0.5 - abs(time_grid - 0.5)), + nrow = 1, byrow = TRUE + ) + Data_3 <- rbind(Data_1, Data_1) + + fD_1 <- fData(time_grid, Data_1) + fD_2 <- fData(time_grid, Data_2) + fD_3 <- fData(time_grid, Data_3) + + # Act + actual_max_1 <- max_ordered(fD_1, fD_2) + actual_max_2 <- max_ordered(fD_2, fD_1) + actual_max_3 <- max_ordered(fD_2, fD_3) + actual_max_4 <- max_ordered(fD_3, fD_2) + + # Assert + expected_max_1 <- c(TRUE, FALSE) + expect_equal(actual_max_1, expected_max_1) + + expected_max_2 <- c(FALSE, TRUE) + expect_equal(actual_max_2, expected_max_2) + + expect_error(max_ordered(fD_1, fD_3)) + expect_error(max_ordered(fD_3, fD_1)) + + expected_max_3 <- c(FALSE, TRUE, FALSE, TRUE) + expect_equal(actual_max_3, expected_max_3) + + expected_max_4 <- c(TRUE, FALSE, TRUE, FALSE) + expect_equal(actual_max_4, expected_max_4) +}) + +test_that("area_ordered() works as expected", { + # Arrange + P <- 1e3 + time_grid <- seq(0, 1, length.out = P) + h <- time_grid[2] - time_grid[1] + + Data_1 <- matrix( + c(1 * time_grid, 2 * time_grid), + nrow = 2, ncol = P, byrow = TRUE + ) + Data_2 <- matrix( + 3 * (0.5 - abs(time_grid - 0.5)), + nrow = 1, byrow = TRUE + ) + Data_3 <- rbind(Data_1, Data_1) + + fD_1 <- fData(time_grid, Data_1) + fD_2 <- fData(time_grid, Data_2) + fD_3 <- fData(time_grid, Data_3) + + # Act + actual_area_1 <- area_ordered(fD_1, fD_2) + actual_area_2 <- area_ordered(fD_2, fD_1) + actual_area_3 <- area_ordered(fD_2, fD_3) + actual_area_4 <- area_ordered(fD_3, fD_2) + + # Assert + expected_area_1 <- c(TRUE, FALSE) + expect_equal(actual_area_1, expected_area_1) + + expected_area_2 <- c(FALSE, TRUE) + expect_equal(actual_area_2, expected_area_2) + + expect_error(area_ordered(fD_1, fD_3)) + expect_error(area_ordered(fD_3, fD_1)) + + expected_area_3 <- c(FALSE, TRUE, FALSE, TRUE) + expect_equal(actual_area_3, expected_area_3) + + expected_area_4 <- c(TRUE, FALSE, TRUE, FALSE) + expect_equal(actual_area_4, expected_area_4) +}) + +# cor_kendall() & cor_spearman() ------------------------------------------ + +test_that("cor_kendall() and cor_spearman() work as expected", { + # Arrange + withr::local_seed(1234) + N <- 2e2 + P <- 1e3 + time_grid <- seq(0, 1, length.out = P) + Cov <- exp_cov_function(time_grid, alpha = 0.3, beta = 0.4) + Data_1 <- generate_gauss_fdata( + N, + centerline = sin(2 * pi * time_grid), + Cov = Cov + ) + Data_2 <- generate_gauss_fdata( + N, + centerline = sin(2 * pi * time_grid), + Cov = Cov + ) + mfD <- mfData(time_grid, list(Data_1, Data_2)) + + # Act + actual_kendall_max <- cor_kendall(mfD, ordering = 'max') + actual_kendall_area <- cor_kendall(mfD, ordering = 'area') + actual_spearman_mei <- cor_spearman(mfD, ordering = 'MEI') + actual_spearman_mhi <- cor_spearman(mfD, ordering = 'MHI') + + # Assert + expect_snapshot_value(actual_kendall_max, style = "serialize") + expect_snapshot_value(actual_kendall_area, style = "serialize") + expect_snapshot_value(actual_spearman_mei, style = "serialize") + expect_snapshot_value(actual_spearman_mhi, style = "serialize") +}) + +# Case studies from Dalia Valencia, Rosa Lillo, Juan Romo ----------------- + +test_that("cor_kendall() & cor_spearman() work on Case Study 1 from Dalia Valencia, Rosa Lillo, Juan Romo.", { + withr::local_seed(1234) + N <- 50 + P <- 50 + time_grid<- seq(0, 1, length.out = P) + + sigma_12 <- 0.8 + R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2) + Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R) + X <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1])^3 + + (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1])^2 + + (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1]) * 3 + Y <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])^2 + + (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2]) * 7 / 8 + + - 10 + + mfD <- mfData(time_grid, list(X, Y)) + + expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize") + expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize") +}) + +test_that("cor_kendall() & cor_spearman() work on Case Study 2 from Dalia Valencia, Rosa Lillo, Juan Romo.", { + withr::local_seed(1234) + N <- 50 + P <- 50 + time_grid<- seq(0, 1, length.out = P) + + sigma_12 <- - 0.7 + R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2) + Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R) + X <- sin(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1]) + Y <- cos(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2]) + + mfD <- mfData(time_grid, list(X, Y)) + + expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize") + expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize") +}) + +test_that("cor_kendall() & cor_spearman() work on Case Study 3 from Dalia Valencia, Rosa Lillo, Juan Romo.", { + withr::local_seed(1234) + N <- 50 + P <- 50 + time_grid<- seq(0, 1, length.out = P) + + sigma_12 <- 1 + Z <- rnorm(N, 0, 1) + X <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2 + Y <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^4 + + mfD <- mfData(time_grid, list(X, Y)) + + expect_equal(cor_kendall(mfD, ordering = 'max' ), 1) + expect_equal(cor_kendall(mfD, ordering = 'area'), 1) + + expect_equal(cor_spearman(mfD, ordering = 'MEI'), 1) + expect_equal(cor_spearman(mfD, ordering = 'MHI'), 1) +}) + +test_that("cor_kendall() & cor_spearman() work on Case Study 4 from Dalia Valencia, Rosa Lillo, Juan Romo.", { + withr::local_seed(1234) + N <- 50 + P <- 50 + time_grid<- seq(0, 1, length.out = P) + + sigma_12 <- 1 + Z <- rnorm(N, 0, 1) + X <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2 + + (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z) * 7 + + 2 + Y <- ((matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2 + + (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z) * 7 + + 2)^3 + + mfD <- mfData(time_grid, list(X, Y)) + + expect_equal(cor_kendall(mfD, ordering = 'max' ), 1) + expect_equal(cor_kendall(mfD, ordering = 'area'), 1) + + expect_equal(cor_spearman(mfD, ordering = 'MEI'), 1) + expect_equal(cor_spearman(mfD, ordering = 'MHI'), 1) +}) + +test_that("cor_kendall() & cor_spearman() work on Case Study 5 from Dalia Valencia, Rosa Lillo, Juan Romo.", { + withr::local_seed(1234) + N <- 50 + P <- 50 + time_grid<- seq(0, 1, length.out = P) + + sigma_12 <- 1 + Z <- rnorm(N, 0, 1) + X <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2 + + (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z) * 7 + + 2 + Y <- 1 - ((matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2 + + (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z) * 7 + + 2)^3 + + mfD <- mfData(time_grid, list(X, Y)) + + expect_equal(cor_kendall(mfD, ordering = 'max' ), -1) + expect_equal(cor_kendall(mfD, ordering = 'area'), -1) + + expect_equal(cor_spearman(mfD, ordering = 'MEI'), -1) + expect_equal(cor_spearman(mfD, ordering = 'MHI'), -1) +}) + +test_that("cor_kendall() & cor_spearman() work on Case Study 6 from Dalia Valencia, Rosa Lillo, Juan Romo.", { + withr::local_seed(1234) + N <- 50 + P <- 50 + time_grid<- seq(0, 1, length.out = P) + + sigma_12 <- 0.6 + R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2) + Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R) + X <- exp(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1]) + Y <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])^3 + + (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])^2 + + (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2]) * 3 + + mfD <- mfData(time_grid, list(X, Y)) + + expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize") + expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize") +}) + +test_that("cor_kendall() & cor_spearman() work on Case Study 7 from Dalia Valencia, Rosa Lillo, Juan Romo.", { + withr::local_seed(1234) + N <- 50 + P <- 50 + time_grid<- seq(0, 1, length.out = P) + + sigma_12 <- -0.8 + R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2) + Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R) + X <- exp(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1])^2 + Y <- cos((matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])) + + mfD <- mfData(time_grid, list(X, Y)) + + expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize") + expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize") +}) + +test_that("cor_kendall() & cor_spearman() work on Case Study 8 from Dalia Valencia, Rosa Lillo, Juan Romo.", { + withr::local_seed(1234) + N <- 50 + P <- 50 + time_grid<- seq(0, 1, length.out = P) + + sigma_12 <- 0.4 + R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2) + Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R) + X <- sin(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1]) + Y <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])^2 + + mfD <- mfData(time_grid, list(X, Y)) + + expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize") + expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize") +}) + +test_that("cor_kendall() & cor_spearman() work on Case Study 9 from Dalia Valencia, Rosa Lillo, Juan Romo.", { + withr::local_seed(1234) + N <- 50 + P <- 50 + time_grid<- seq(0, 1, length.out = P) + + sigma_12 <- 1 + Z <- rnorm(N, 0, 1) + X <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z)^2 + + (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z) * 9 + + - 5 + Y <- cos(matrix(3 * time_grid, nrow = N, ncol = P, byrow = TRUE) + Z) + + mfD <- mfData(time_grid, list(X, Y)) + + expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize") + expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize") +}) + +test_that("cor_kendall() & cor_spearman() work on Case Study 10 from Dalia Valencia, Rosa Lillo, Juan Romo.", { + withr::local_seed(1234) + N <- 50 + P <- 50 + time_grid<- seq(0, 1, length.out = P) + + sigma_12 <- 0.9 + R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2) + Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R) + X <- exp(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE)^2 + Z[, 1]) + Y <- (matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2])^2 + + matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) * (-8) + + (matrix(0, nrow = N, ncol = P, byrow = TRUE) + Z[, 2]) + + mfD <- mfData(time_grid, list(X, Y)) + + expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize") + expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize") +}) + +test_that("cor_kendall() & cor_spearman() work on Case Study 11 from Dalia Valencia, Rosa Lillo, Juan Romo.", { + withr::local_seed(1234) + N <- 50 + P <- 50 + time_grid <- seq(0, 1, length.out = P) + + sigma_12 <- 0. + R <- matrix(c(1, sigma_12, sigma_12, 1), ncol = 2, nrow = 2) + Z <- matrix(rnorm(N * 2, 0, 1), ncol = 2, nrow = N) %*% chol(R) + X <- exp(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 1]) + Y <- sin(matrix(time_grid, nrow = N, ncol = P, byrow = TRUE) + Z[, 2]) + + mfD <- mfData(time_grid, list(X, Y)) + + expect_snapshot_value(cor_kendall(mfD, ordering = 'max'), style = "serialize") + expect_snapshot_value(cor_kendall(mfD, ordering = 'area'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MEI'), style = "serialize") + expect_snapshot_value(cor_spearman(mfD, ordering = 'MHI'), style = "serialize") +}) diff --git a/tests/testthat/test-depthgram.R b/tests/testthat/test-depthgram.R new file mode 100644 index 0000000..33db420 --- /dev/null +++ b/tests/testthat/test-depthgram.R @@ -0,0 +1,33 @@ +test_that("`depthgram()` works as expected for all supported input types", { + # Arrange + withr::local_seed(1234) + N <- 100 + P <- 100 + grid <- seq(0, 1, length.out = P) + Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) + + Data <- list() + Data[[1]] <- generate_gauss_fdata( + N, + centerline = sin(2 * pi * grid), + Cov = Cov + ) + Data[[2]] <- generate_gauss_fdata( + N, + centerline = sin(2 * pi * grid), + Cov = Cov + ) + names <- paste0("id_", 1:nrow(Data[[1]])) + fD <- fData(grid, Data[[1]]) + mfD <- mfData(grid, Data) + + # Act + actual_list <- depthgram(Data, marginal_outliers = TRUE, ids = names) + actual_fData <- depthgram(fD, marginal_outliers = TRUE, ids = names) + actual_mfData <- depthgram(mfD, marginal_outliers = TRUE, ids = names) + + # Assert + expect_snapshot_value(actual_list, style = "json2") + expect_snapshot_value(actual_fData, style = "json2") + expect_snapshot_value(actual_mfData, style = "json2") +}) diff --git a/tests/testthat/test-fData.R b/tests/testthat/test-fData.R new file mode 100644 index 0000000..cb09b0a --- /dev/null +++ b/tests/testthat/test-fData.R @@ -0,0 +1,342 @@ +test_that("`fData() correctly creates `fData` objects", { + withr::local_seed(1234) + N <- 100 + P <- 100 + time_grid <- seq(0, 1, length.out = P) + Cov <- exp_cov_function(time_grid, alpha = 0.3, beta = 0.4) + Data <- generate_gauss_fdata(N, centerline = sin(2 * pi * time_grid), Cov = Cov) + fD <- fData(time_grid, Data) + + expect_snapshot_value(fData(time_grid, Data), style = "serialize") + expect_snapshot_value(fData(time_grid, 1:P), style = "serialize") + + vdiffr::expect_doppelganger( + "S3 Plot Method for fData objects", + plot( + fD, + xlab = 'time', ylab = 'values', + main = 'A functional dataset' + ) + ) +}) + +test_that("statistical summaries work as expected on `fData` objects", { + withr::local_seed(1234) + N <- 100 + P <- 100 + time_grid <- seq(0, 1, length.out = P) + Cov <- exp_cov_function(time_grid, alpha = 0.3, beta = 0.4) + Data <- generate_gauss_fdata(N, centerline = sin(2 * pi * time_grid), Cov = Cov) + fD <- fData(time_grid, Data) + + expect_equal( + as.numeric(mean(fD)$values), + colMeans(fD$values) + ) + expect_equal( + as.numeric(median_fData(fD)$value), + fD$values[which.max(MBD(fD$values)), ] + ) + expect_equal( + as.numeric(median_fData(fD, type = 'MBD', manage_ties = TRUE)$values), + fD$values[which.max(MBD(fD$values, manage_ties = TRUE)), ] + ) + expect_equal( + as.numeric(median_fData(fD, type = 'MHRD')$value), + fD$values[which.max(MHRD(fD$values)), ] + ) + expect_equal( + cov(fD$values), + cov_fun(fD)$values + ) + expect_equal( + cov(fD$values, (fD + 1:P)$values), + cov_fun(fD, fD + 1:P)$values + ) + + expect_error(cov_fun(1)) + expect_error(cov_fun(fD, fD[-1, ])) + expect_error(cov_fun(fD, fD[, 1:10])) +}) + +test_that("statistical summaries work as expected on `mfData` objects", { + withr::local_seed(1234) + N <- 100 + P <- 100 + time_grid <- seq(0, 1, length.out = P) + Cov <- exp_cov_function(time_grid, alpha = 0.3, beta = 0.4) + Data <- generate_gauss_fdata( + N, + centerline = sin(2 * pi * time_grid), + Cov = Cov + ) + fD <- fData(time_grid, Data) + mfD <- mfData(time_grid, list('comp1' = Data, 'comp2' = Data, 'comp3' = Data)) + mfD2 <- mfData(time_grid, list(Data, Data, Data)) + mfD3 <- mfData(time_grid, list(Data, Data)) + rhs <- lapply(1:(2 * mfD$L), function( i ) cov_fun(mfD$fDList[[1]])) + names(rhs) <- c('comp1_1', 'comp1_2', 'comp1_3', 'comp2_2', 'comp2_3', 'comp3_3') + + expect_equal(cov_fun(mfD, mfD2), rhs) + expect_equal( + cov_fun(mfD2, fD), + lapply(1:mfD$L, function(i) cov_fun(mfD$fDList[[1]])) + ) + expect_equal( + names(cov_fun(mfD)), + c( + 'comp1_comp1', 'comp1_comp2', 'comp1_comp3', + 'comp2_comp2', 'comp2_comp3', 'comp3_comp3' + ) + ) + expect_equal( + names(cov_fun(mfD2)), + c('1_1', '1_2', '1_3', '2_2', '2_3', '3_3') + ) + + expect_error(cov_fun(mfD, mfD3)) + expect_error(cov_fun(mfD, fD[, 1:10])) +}) + +test_that("Algebraic operations work as expected on `fData` objects", { + fD <- fData( + grid = seq(0, 1, length.out = 10), + values = matrix(seq(1, 10), nrow = 21, ncol = 10, byrow = TRUE) + ) + fD.2 <- fData( + grid = seq(0, 1, length.out = 11), + values = matrix(seq(1, 11), nrow = 21, ncol = 11, byrow = TRUE) + ) + + expect_equal( + sum((fD + 1:10)$values - + matrix(2 * seq(1, 10), nrow = 21, ncol = 10, byrow = TRUE)), + 0 + ) + expect_equal(sum((fD - 1:10)$values), 0) + expect_equal( + sum((fD + array(1, dim = c(1, 10)))$values - + matrix(seq(2, 11), nrow = 21, ncol = 10, byrow = TRUE)), + 0 + ) + expect_equal( + sum((fD + fD - matrix( + 2 * seq(1, 10), + nrow = 21, ncol = 10, + byrow = TRUE + ))$values), + 0 + ) + expect_equal(fD * 2, fD + fD) + expect_equal((fD * 4) / 2, fD + fD) + + expect_error(fD + fD.2, regexp = 'Error.*') + +}) + +test_that("Subsetting operations work as expected on `fData` objects", { + fD <- fData( + grid = seq(0, 1, length.out = 10), + values = matrix(seq(1, 10), nrow = 21, ncol = 10, byrow = TRUE) + ) + + expect_identical(fD[1, ], fData(seq(0, 1, length.out = 10), 1:10)) + expect_identical( + fD[, 1:2], + fData( + grid = seq(0, 1, length.out = 10)[1:2], + values = matrix(1:2, nrow = 21, ncol = 2, byrow = TRUE) + ) + ) + expect_identical( + fD[1:2, 1:2, as_fData = FALSE], + matrix(seq(1, 2), nrow = 2, ncol = 2, byrow = TRUE) + ) + + fD_logical_subset <- fD[, c(rep(FALSE, 5), rep(TRUE, 5))] + expect_identical( + fD_logical_subset$values, + matrix(seq(6, 10), nrow = 21, ncol = 5, byrow = TRUE) + ) + expect_identical( + c( + fD_logical_subset$t0, + fD_logical_subset$tP, + fD_logical_subset$h, + fD_logical_subset$P + ), + c(5 * fD$h, 1, fD$h, 5) + ) + + fD_logical_subset <- fD[, c(rep(TRUE, 5), rep(FALSE, 5))] + + expect_identical( + fD_logical_subset$values, + matrix(seq(1, 5), nrow = 21, ncol = 5, byrow = TRUE) + ) + expect_identical( + c( + fD_logical_subset$t0, + fD_logical_subset$tP, + fD_logical_subset$h, + fD_logical_subset$P + ), + c(0, 4 * fD$h, fD$h, 5) + ) +}) + +test_that("Subsetting operations work as expected on `mfData` objects", { + N <- 3 + P <- 20 + L <- 2 + grid <- seq(0, 1, length.out = P) + values1 <- rep(1, P) + values2 <- cos(2 * pi * grid) + values3 <- sin(2 * pi * grid) + values4 <- rep(2, P) + values5 <- cos(4 * pi * grid) + values6 <- sin(4 * pi * grid) + mfD <- mfData(grid, list( + matrix(c(values1, values2, values3), nrow = 3, ncol = P, byrow = TRUE), + matrix(c(values4, values5, values6), nrow = 3, ncol = P, byrow = TRUE) + )) + + expect_equal(mfD[1, ]$fDList[[1]]$values, t(as.matrix(values1))) + expect_equal(mfD[1, ]$fDList[[2]]$values, t(as.matrix(values4))) + expect_equal( + mfD[1:2, ]$fDList[[1]]$values, + matrix(c(values1, values2), nrow = 2, ncol = P, byrow = TRUE) + ) + expect_equal( + mfD[1:2, ]$fDList[[2]]$values, + matrix(c(values4, values5), nrow = 2, ncol = P, byrow = TRUE) + ) + expect_equal( + mfD[, 1:5]$fDList[[1]]$values, + matrix( + c(values1[1:5], values2[1:5], values3[1:5]), + nrow = 3, ncol = 5, byrow = TRUE + ) + ) + expect_equal( + mfD[, 1:5]$fDList[[2]]$values, + matrix( + c(values4[1:5], values5[1:5], values6[1:5]), + nrow = 3, ncol = 5, byrow = TRUE + ) + ) + expect_equal( + append_mfData(mfD[1:2, ], mfD[3, ])$fDList[[1]]$values, + mfD$fDList[[1]]$values + ) + expect_equal( + append_fData(mfD[1:2, ]$fDList[[1]], mfD[3, ]$fDList[[1]])$values, + mfD$fDList[[1]]$values + ) + + expect_true( + (mfD[1:2, ]$N == 2) & (mfD[1:2, ]$P == P) & (mfD[1:2, ]$L == 2 ) + ) + + expect_error(mfD[ , c(1:5, 7:8)]) +}) + +test_that("`mfData() correctly creates `mfData` objects", { + withr::local_seed(1234) + N <- 100 + P <- 100 + time_grid <- seq(0, 1, length.out = P) + Cov <- exp_cov_function(time_grid, alpha = 0.3, beta = 0.4) + Data_1 <- generate_gauss_fdata(N, centerline = sin(2 * pi * time_grid), Cov = Cov) + Data_2 <- generate_gauss_fdata(N, centerline = sin(2 * pi * time_grid), Cov = Cov) + + expect_snapshot_value(mfData(time_grid, list(Data_1, Data_2)), style = "serialize") + expect_snapshot_value(mfData(time_grid, list(1:P, 1:P)), style = "serialize") + + vdiffr::expect_doppelganger( + "S3 Plot Method for mfData objects", + plot( + mfData(time_grid, list(Data_1, Data_2)), + xlab = 'time', ylab = list('values', 'values'), + main = list('First Component', 'Second Component') + ) + ) + + mfD <- mfData(time_grid, list(Data_1, Data_2)) + + vdiffr::expect_doppelganger( + "S3 Plot Method for fData object subsetted from mfData object", + plot(mfD$fDList[[1]]) + ) + + expect_snapshot_value(as.mfData(list( + fData(time_grid, Data_1), + fData(time_grid, Data_2) + )), style = "serialize") + + expect_identical( + toListOfValues(mfData(time_grid, list(Data_1, Data_2))), + list(Data_1, Data_2) + ) +}) + +test_that("`unfold()` works as expected", { + P <- 100 + time_grid <- seq(0, 1, length.out = P) + D <- matrix(c( + sin(2 * pi * time_grid), + cos(2 * pi * time_grid), + sin(10 * pi * time_grid) * time_grid + 2 + ), ncol = P, nrow = 3, byrow = TRUE) + fD <- fData(time_grid, D) + fD_unfold <- unfold(fD) + + # Ana's implementation + mon_func <- function(x) { + x<- as.matrix(x) + if (ncol(x) == 1) x <- t(x) + P <- ncol(x) + x_mon <- x + diff_x <- t(abs(apply(x_mon, 1, diff))) + for (j in 2:P) + x_mon[, j] <- x_mon[, j - 1] + diff_x[, j - 1] + x_mon + } + + expect_true(all(apply(fD_unfold$values, 1, function(x) all(diff(x) >= 0)))) + expect_equal(fD_unfold$values, mon_func(fD$values)) +}) + +test_that("`warp()` works as expected", { + withr::local_seed(1234) + N <- 30 + P <- 101 + t0 <- 0 + t1 <- 1 + time_grid <- seq(t0, t1, length.out = P) + means <- round(runif(N, t0 + (t1 - t0) / 8, t1 - (t1 - t0) / 8), 3) + Data <- matrix( + sapply(means, function(m) dnorm(time_grid, mean = m, sd = 0.05)), + ncol = P, nrow = N, byrow = TRUE + ) + fD <- fData(time_grid, Data) + + # Piecewise linear warpings + template_warping <- function(m) c( + time_grid[time_grid <= 0.5] * m / 0.5, + (time_grid[time_grid > 0.5] - 0.5) * (1 - m) / 0.5 + m + ) + warpings <- matrix( + sapply(means, template_warping), + ncol = P, nrow = N, byrow = TRUE + ) + wfD <- fData(time_grid, warpings) + fD_warped <- warp(fD, wfD) + + expect_true(all( + maxima(fD_warped) - dnorm(0.5, 0.5, 0.05) <= .Machine$double.eps + )) + expect_true(all( + maxima(fD_warped, which = TRUE)$grid - 0.5 <= .Machine$double.eps + )) +}) diff --git a/tests/testthat/test-fbplot.R b/tests/testthat/test-fbplot.R new file mode 100644 index 0000000..86e43eb --- /dev/null +++ b/tests/testthat/test-fbplot.R @@ -0,0 +1,121 @@ +# Functional boxplot for univariate functional data ----------------------- + +test_that("fbplot() works as expected for univariate data", { + time_grid <- seq(0, 1, length.out = 1e2) + D <- matrix(c( + sin(2 * pi * time_grid) + 0, + sin(2 * pi * time_grid) + 1, + sin(2 * pi * time_grid) + 2, + sin(2 * pi * time_grid) + 3, + sin(2 * pi * time_grid) + 4, + sin(2 * pi * time_grid) + 5, + sin(2 * pi * time_grid) + 6, + sin(2 * pi * time_grid) + 7, + sin(2 * pi * time_grid) + 8, + sin(2 * pi * time_grid) + 9, + sin(2 * pi * time_grid) + 10, + sin(2 * pi * time_grid) - 1, + sin(2 * pi * time_grid) - 2, + sin(2 * pi * time_grid) - 3, + sin(2 * pi * time_grid) - 4, + sin(2 * pi * time_grid) - 5, + sin(2 * pi * time_grid) - 6, + sin(2 * pi * time_grid) - 7, + sin(2 * pi * time_grid) - 8, + sin(2 * pi * time_grid) - 9, + sin(2 * pi * time_grid) - 10 + ), nrow = 21, ncol = length(time_grid), byrow = TRUE) + fD <- fData(time_grid, D) + + expect_snapshot_value(fbplot(fD, Fvalue = 10, display = FALSE), style = "json2") + expect_snapshot_value(fbplot( + fD, + display = FALSE, + xlab = 'time', + ylab = 'value', + main = 'My Functional Boxplot' + ), style = "json2") +}) + +# Adjusted functional boxplot for univariate data ------------------------- + +test_that("`fbplot()` with adjustment works as expected for univariate data", { + withr::local_seed(1234) + time_grid <- seq(0, 1, length.out = 1e2) + N <- 5e2 + Data <- generate_gauss_fdata( + N, + centerline = sin(2 * pi * time_grid), + Cov = exp_cov_function(time_grid, alpha = 0.3, beta = 0.4) + ) + fD <- fData(time_grid, Data) + + expect_warning(expect_warning(fbplot( + fD, + adjust = list(N_trials = 1, trial_size = N, foo = 'bar', baz = 'qux'), + display = FALSE + ))) + + skip_on_cran() + expect_snapshot_value(fbplot( + fD, + adjust = list(N_trials = 10, trial_size = N, VERBOSE = FALSE), + display = FALSE, + xlab = 'time', ylab = 'Values', + main = 'My adjusted functional boxplot' + ), style = "json2") + +}) + +# For multivariate functional data ---------------------------------------- + +test_that("`fbplot()` works as expected for multivariate data", { + time_grid <- seq(0, 1, length.out = 1e2) + D <- matrix(c( + sin(2 * pi * time_grid) - 10, + sin(2 * pi * time_grid) - 9, + sin(2 * pi * time_grid) - 8, + sin(2 * pi * time_grid) - 7, + sin(2 * pi * time_grid) - 6, + sin(2 * pi * time_grid) - 5, + sin(2 * pi * time_grid) - 4, + sin(2 * pi * time_grid) - 3, + sin(2 * pi * time_grid) - 2, + sin(2 * pi * time_grid) - 1, + sin(2 * pi * time_grid) + 0, + sin(2 * pi * time_grid) + 1, + sin(2 * pi * time_grid) + 2, + sin(2 * pi * time_grid) + 3, + sin(2 * pi * time_grid) + 4, + sin(2 * pi * time_grid) + 5, + sin(2 * pi * time_grid) + 6, + sin(2 * pi * time_grid) + 7, + sin(2 * pi * time_grid) + 8, + sin(2 * pi * time_grid) + 9, + sin(2 * pi * time_grid) + 10 + ), nrow = 21, ncol = length(time_grid), byrow = TRUE) + mfD <- mfData(time_grid, list(D, D * abs(1:21 - 11) / 5)) + + expect_snapshot_value(fbplot(mfD, Fvalue = 3, display = FALSE), style = "json2") + expect_error(fbplot(mfD, adjust = list(N_trials = 2), display = FALSE)) +}) + +test_that("`fbplot()` works for randomly generated multivariate data", { + withr::local_seed(1234) + P <- 1e2 + N <- 1e2 + L <- 3 + time_grid <- seq(0, 1, length.out = P) + C1 <- exp_cov_function(time_grid, alpha = 0.3, beta = 0.4) + C2 <- exp_cov_function(time_grid, alpha = 0.3, beta = 0.4) + C3 <- exp_cov_function(time_grid, alpha = 0.3, beta = 0.4) + Data <- generate_gauss_mfdata( + N, L, + centerline = matrix(sin(2 * pi * time_grid), nrow = 3, ncol = P, byrow = TRUE ), + correlations = rep(0.5, 3), + listCov = list(C1, C2, C3) + ) + mfD <- mfData(time_grid, Data) + + expect_snapshot_value(fbplot(mfD, Fvalue = 2.5, display = FALSE), style = "json2") +}) diff --git a/tests/testthat/test-multiMBD.R b/tests/testthat/test-multiMBD.R new file mode 100644 index 0000000..2516bf8 --- /dev/null +++ b/tests/testthat/test-multiMBD.R @@ -0,0 +1,64 @@ +test_that("`multiMBD()` works as expected", { + # Arrange + withr::local_seed(1234) + N <- 1e2 + P <- 1e3 + time_grid <- seq(0, 10, length.out = P) + Cov <- exp_cov_function(time_grid, alpha = 0.2, beta = 0.8) + Data_1 <- generate_gauss_fdata(N, centerline = rep(0, P), Cov = Cov) + Data_2 <- generate_gauss_fdata(N, centerline = rep(0, P), Cov = Cov) + + # Act + actual_wo_ties <- multiMBD( + list(Data_1, Data_2), + weights = 'uniform', + manage_ties = FALSE + ) - (MBD(Data_1, manage_ties = FALSE) + + MBD(Data_2, manage_ties = FALSE)) / 2 + actual_with_ties <- multiMBD( + list(Data_1, Data_2), + weights = 'uniform', + manage_ties = TRUE + ) - (MBD(Data_1, manage_ties = TRUE) + + MBD(Data_2, manage_ties = TRUE)) / 2 + actual_non_uniform_weights <- multiMBD( + list(Data_1, Data_2), + weights = c(1/3, 2/3), + manage_ties = FALSE + ) - (1/3 * MBD(Data_1, manage_ties = FALSE) + + 2/3 * MBD(Data_2, manage_ties = FALSE)) + + # Assert + expect_equal(actual_with_ties, rep(0, N)) + expect_equal(actual_wo_ties, rep(0, N)) + expect_equal(actual_non_uniform_weights, rep(0, N)) + expect_error(multiMBD(list(Data_1, Data_2), weights = c(1/2, 1))) + expect_error(multiMBD(list(Data_1, Data_2), weights = 'unif')) +}) + +test_that("`multiBD()` works as expected", { + # Arrange + withr::local_seed(1234) + N <- 1e2 + P <- 1e3 + time_grid <- seq(0, 10, length.out = P) + Cov <- exp_cov_function(time_grid, alpha = 0.2, beta = 0.8) + Data_1 <- generate_gauss_fdata(N, centerline = rep(0, P), Cov = Cov) + Data_2 <- generate_gauss_fdata(N, centerline = rep(0, P), Cov = Cov) + + # Act + actual_uniform_weights <- multiBD( + list(Data_1, Data_2), + weights = 'uniform' + ) - (BD(Data_1) + BD(Data_2)) / 2 + actual_non_uniform_weights <- multiBD( + list(Data_1, Data_2), + weights = c(1/3, 2/3) + ) - (1/3 * BD(Data_1) + 2/3 * BD(Data_2)) + + # Assert + expect_equal(actual_uniform_weights, rep(0, N)) + expect_equal(actual_non_uniform_weights, rep(0, N)) + expect_error(multiBD(list(Data_1, Data_2), weights = c(1/2, 1))) + expect_error(multiBD(list(Data_1, Data_2), weights = 'unif')) +}) diff --git a/tests/testthat/test-outliergram.R b/tests/testthat/test-outliergram.R new file mode 100644 index 0000000..f4644ed --- /dev/null +++ b/tests/testthat/test-outliergram.R @@ -0,0 +1,43 @@ +test_that("`outliergram()` works as expected", { + expect_snapshot_value(outliergram(fDout, display = FALSE), style = "serialize") +}) + +test_that("`outliergram()` correctly identifies outliers with Fvalue = 1.5)", { + expect_snapshot_value( + outliergram(fDout, display = FALSE)$ID_outliers, + style = "json2" + ) +}) + +test_that("`outliergram()` correctly identifies outliers with Fvalue = 2.5)", { + expect_snapshot_value( + outliergram(fDout, Fvalue = 2.5, display = FALSE)$ID_outliers, + style = "json2" + ) +}) + +test_that("`outliergram()` correctly identifies outliers with auto-adjusted Fvalue", { + skip_on_cran() + expect_snapshot_value( + outliergram( + fDout, + adjust = list( + N_trials = 10, + trial_size = 5 * fDout$N, + TPR = 0.01, + VERBOSE = FALSE + ), + display = FALSE)$ID_outliers, + style = "json2" + ) +}) + +test_that("`outliergram()` warns if unrecognized argument in `adjust` list", { + expect_warning(expect_warning( + outliergram( + fDout, + adjust = list(N_trials = 1, trial_size = fDout$N, foo = 'bar', baz = 'qux'), + display = FALSE + ) + )) +}) diff --git a/tests/testthat/test-restyling.R b/tests/testthat/test-restyling.R new file mode 100644 index 0000000..8f7fe42 --- /dev/null +++ b/tests/testthat/test-restyling.R @@ -0,0 +1,80 @@ +test_that("`BD()` handles equally arrays and fData objects", { + expect_identical( + BD(fD_restyling), + BD(Data_restyling) + ) +}) + +test_that("`MBD(, manage_ties = TRUE)` handles equally arrays and fData objects", { + expect_identical( + MBD(fD_restyling, manage_ties = TRUE), + MBD(Data_restyling, manage_ties = TRUE) + ) +}) + +test_that("`MBD(, manage_ties = FALSE)` handles equally arrays and fData objects", { + expect_identical( + MBD(fD_restyling, manage_ties = FALSE), + MBD(Data_restyling, manage_ties = FALSE) + ) +}) + +test_that("`EI()` handles equally arrays and fData objects", { + expect_identical( + EI(fD_restyling), + EI(Data_restyling) + ) +}) + +test_that("`MEI()` handles equally arrays and fData objects", { + expect_identical( + MEI(fD_restyling), + MEI(Data_restyling) + ) +}) + +test_that("`HI()` handles equally arrays and fData objects", { + expect_identical( + HI(fD_restyling), + HI(Data_restyling) + ) +}) + +test_that("`MHI()` handles equally arrays and fData objects", { + expect_identical( + MHI(fD_restyling), + MHI(Data_restyling) + ) +}) + +test_that("`HRD()` handles equally arrays and fData objects", { + expect_identical( + HRD(fD_restyling), + HRD(Data_restyling) + ) +}) + +test_that("`MHRD()` handles equally arrays and fData objects", { + expect_identical( + MHRD(fD_restyling), + MHRD(Data_restyling) + ) +}) + +test_that("`BD_relative()` and `MBD_relative()` handle equally arrays and fData objects", { + centerline_1 <- sin(2 * pi * grid) + centerline_2 <- sin(2 * pi * grid) + 0.5 + Data_reference <- generate_gauss_fdata(N, centerline_1, Cov) + Data_target <- generate_gauss_fdata(N, centerline_2, Cov) + fD_target <- fData(grid, Data_target) + fD_reference <- fData(grid, Data_reference) + + expect_identical( + BD_relative(fD_target, fD_reference), + BD_relative(Data_target, Data_reference) + ) + expect_identical( + MBD_relative(fD_target, fD_reference), + MBD_relative(Data_target, Data_reference) + ) +}) diff --git a/tests/testthat/test-simulation.R b/tests/testthat/test-simulation.R new file mode 100644 index 0000000..571b64f --- /dev/null +++ b/tests/testthat/test-simulation.R @@ -0,0 +1,73 @@ +test_that("`generate_gauss_fdata()` works as expected using `Cov` argument", { + withr::local_seed(1234) + expect_snapshot_value( + generate_gauss_fdata(N, centerline, Cov = C1), + style = "json2" + ) +}) + +test_that("`generate_gauss_fdata()` works as expected using `CholCov` argument", { + withr::local_seed(1234) + expect_snapshot_value( + generate_gauss_fdata(N, centerline, CholCov = CholC1), + style = "json2" + ) +}) + +test_that("`generate_gauss_mfdata()` works as expected using `listCov` argument", { + withr::local_seed(1234) + expect_snapshot_value( + generate_gauss_mfdata( + N, L, + centerlines, + correlations = c(0.5, 0.5, 0.5), + listCov = list(C1, C2, C3) + ), + style = "json2" + ) +}) + +test_that("`generate_gauss_mfdata()` works as expected using `listCholCov` argument", { + withr::local_seed(1234) + expect_snapshot_value( + generate_gauss_mfdata( + N, L, + centerlines, + correlations = c(0.5, 0.5, 0.5), + listCholCov = list(CholC1, CholC2, CholC3) + ), + style = "json2" + ) +}) + +test_that("`generate_gauss_mfdata()` fails when dimensions mismatch (1/3)", { + withr::local_seed(1234) + expect_error(generate_gauss_mfdata( + N, L, + centerlines, + correlations = c(0.5, 0.5, 0.5), + listCholCov = list(CholC1[-1, ], CholC2[-1, ], CholC3[-1, ]) + )) +}) + +test_that("`generate_gauss_mfdata()` fails when dimensions mismatch (2/3)", { + withr::local_seed(1234) + expect_error(generate_gauss_mfdata( + N, L, + centerlines[-1, ], + correlations = c(0.5, 0.5, 0.5), + listCov = c(C1, C2, C3), + listCholCov = list(CholC1[-1, ], CholC2[-1, ], CholC3[-1, ]) + )) +}) + +test_that("`generate_gauss_mfdata()` fails when dimensions mismatch (3/3)", { + withr::local_seed(1234) + expect_error(generate_gauss_mfdata( + N, L, + centerlines[, -1], + correlations = c(0.5, 0.5), + listCov = c(C1, C2, C3), + listCholCov = list(CholC1, CholC2, CholC3) + )) +}) diff --git a/tests/testthat/test_BD.R b/tests/testthat/test_BD.R deleted file mode 100644 index ae7aa0d..0000000 --- a/tests/testthat/test_BD.R +++ /dev/null @@ -1,16 +0,0 @@ - -# TESTING BDs ------------------------------------------------------------- - -time_grid = seq( 0, 1, length.out = 1e2 ) - - -D = matrix( c( 1 + sin( 2 * pi * time_grid ), - 0 + sin( 4 * pi * time_grid ), - 1 - sin( pi * ( time_grid - 0.2 ) ), - 0.1 + cos( 2 * pi * time_grid ), - 0.5 + sin( 3 * pi + time_grid ), - -2 + sin( pi * time_grid ) ), - nrow = 6, ncol = length( time_grid ), byrow = TRUE ) - -test_that( "Correctness of BD method", - expect_equal( BD( D ), c( 1/3, 1/3, 1/3, 1/3, 14 / 30, 1 / 3 ) ) ) diff --git a/tests/testthat/test_BD_relative.R b/tests/testthat/test_BD_relative.R deleted file mode 100644 index 4c20937..0000000 --- a/tests/testthat/test_BD_relative.R +++ /dev/null @@ -1,51 +0,0 @@ - -# TESTING BDs ------------------------------------------------------------- - -time_grid = seq( 0, 1, length.out = 1e2 ) - - -Data_ref = matrix( c( 0 + sin( 2 * pi * time_grid ), - 1 + sin( 2 * pi * time_grid ), - -1 + sin( 2 * pi * time_grid ) - ), - nrow = 3, ncol = length( time_grid ), byrow = TRUE ) - -Data_test_1 = matrix( c( 0.6 + sin( 2 * pi * time_grid ) ), - nrow = 1, ncol = length( time_grid ), byrow = TRUE ) - -Data_test_2 = matrix( c( 0.6 + sin( 2 * pi * time_grid ) ), - nrow = length( time_grid ), ncol = 1, byrow = TRUE ) - -Data_test_3 = 0.6 + sin( 2 * pi * time_grid ) - -Data_test_4 = array( 0.6 + sin( 2 * pi * time_grid ), dim = length( time_grid ) ) - -Data_test_5 = array( 0.6 + sin( 2 * pi * time_grid ), dim = c( 1, length( time_grid ) ) ) - -Data_test_6 = array( 0.6 + sin( 2 * pi * time_grid ), dim = c( length( time_grid ), 1 ) ) - -Data_test_7 = matrix( c( 0.5 + sin( 2 * pi * time_grid ), - -0.5 + sin( 2 * pi * time_grid ), - 1.1 + sin( 2 * pi * time_grid ) ), - nrow = 3, ncol = length( time_grid ), byrow = TRUE ) - -test_that( "Correctness of relative BD (single test function in row matrix form)", - expect_equal( BD_relative( Data_test_1, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative BD (single test function in column matrix form)", - expect_equal( BD_relative( Data_test_2, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative BD (single test function in vector form)", - expect_equal( BD_relative( Data_test_3, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative BD (single test function in 1D array form)", - expect_equal( BD_relative( Data_test_4, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative BD (single test function in row-like 2D array form)", - expect_equal( BD_relative( Data_test_5, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative BD (single test function in column-like 2D array form)", - expect_equal( BD_relative( Data_test_6, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative BD (multiple test function)", - expect_equal( BD_relative( Data_test_7, Data_ref ), c( 2/3, 2/3, 0 ) )) diff --git a/tests/testthat/test_EI_and_MEI.R b/tests/testthat/test_EI_and_MEI.R deleted file mode 100644 index 4bd87f7..0000000 --- a/tests/testthat/test_EI_and_MEI.R +++ /dev/null @@ -1,41 +0,0 @@ - - -# TESTING EI AND MEI ------------------------------------------------------ - -N = 2 - -time_grid = seq( 0, 1, length.out = N * 1e2 ) - -Data = matrix( 0, nrow = N, ncol = length( time_grid ) ) - -for( iObs in 1 : N ) -{ - Data[ iObs, ] = as.numeric( time_grid >= ( iObs - 1 ) / N & time_grid < iObs / N ) -} - -Data[ N, length( time_grid ) ] = 1 - -test_that( "Correctness of EI", - expect_equal( EI( Data ), rep( 1 / N, N ) ) ) - -test_that( "Correctness of MEI", - expect_equal( MEI( Data ), rep( 1 - ( N - 1 ) / N^2, N ) ) ) - - - -# TEST BY JAMES LONG (TAMU) ------------------------------------------------------------------- - -yints = c( 1.27, .927, 1/2, .217, 0) -slopes = c( -1, -1, 0, 1, 1 ) -time_grid = ( 0 : 100 ) / 100 - -Data = matrix( 0, nrow = length( yints ), - ncol = length( time_grid ) ) - -for( i in 1 : length( yints ) ) - - Data[ i, ] = yints[ i ] + time_grid * slopes[ i ] - -test_that( "Correctness of EI - James Long test", - expect_equal( EI( Data ), c( 0.2, 0.4, 0.2, 0.2, 0.4 ) ) ) - diff --git a/tests/testthat/test_HI_and_MHI.R b/tests/testthat/test_HI_and_MHI.R deleted file mode 100644 index 63ed742..0000000 --- a/tests/testthat/test_HI_and_MHI.R +++ /dev/null @@ -1,40 +0,0 @@ - - -# TESTING HI AND MHI ------------------------------------------------------ - -N = 20 - -time_grid = seq( 0, 1, length.out = N * 1e2 ) - -Data = matrix( 0, nrow = N, ncol = length( time_grid ) ) - -for( iObs in 1 : N ) -{ - Data[ iObs, ] = as.numeric( time_grid >= ( iObs - 1 ) / N & time_grid < iObs / N ) -} - -Data[ N, length( time_grid ) ] = 1 - -test_that( "Correctness of HI", - expect_equal( HI( Data ), rep( 1 / N, N ) ) ) - -test_that( "Correctness of MHI", - expect_equal( MHI( Data ), rep( ( N + ( N - 1 )^2 ) / N^2 , N ) ) ) - - -# TEST BY JAMES LONG (TAMU) ------------------------------------------------------------------- - -yints = c( 1.27, .927, 1/2, .217, 0) -slopes = c( -1, -1, 0, 1, 1 ) -time_grid = ( 0 : 100 ) / 100 - -Data = matrix( 0, nrow = length( yints ), - ncol = length( time_grid ) ) - -for( i in 1 : length( yints ) ) - - Data[ i, ] = yints[ i ] + time_grid * slopes[ i ] - -test_that( "Correctness of HI - James Long test", - expect_equal( HI( Data ), c( 0.4, 0.2, 0.2, 0.4, 0.2 ) ) ) - diff --git a/tests/testthat/test_HRD_and_MHRD.R b/tests/testthat/test_HRD_and_MHRD.R deleted file mode 100644 index cdbd140..0000000 --- a/tests/testthat/test_HRD_and_MHRD.R +++ /dev/null @@ -1,56 +0,0 @@ - -# CHECKING HRD AND MHRD --------------------------------------------------- - -time_grid = seq( 0, 1, length.out = 1e2 ) - -D = matrix( c( sin( 2 * pi * time_grid ) + 10, - sin( 2 * pi * time_grid ) + 9, - sin( 2 * pi * time_grid ) + 8, - sin( 2 * pi * time_grid ) + 7, - sin( 2 * pi * time_grid ) + 6, - sin( 2 * pi * time_grid ) + 5, - sin( 2 * pi * time_grid ) + 4, - sin( 2 * pi * time_grid ) + 3, - sin( 2 * pi * time_grid ) + 2, - sin( 2 * pi * time_grid ) + 1, - sin( 2 * pi * time_grid ) + 0, - sin( 2 * pi * time_grid ) - 1, - sin( 2 * pi * time_grid ) - 2, - sin( 2 * pi * time_grid ) - 3, - sin( 2 * pi * time_grid ) - 4, - sin( 2 * pi * time_grid ) - 5, - sin( 2 * pi * time_grid ) - 6, - sin( 2 * pi * time_grid ) - 7, - sin( 2 * pi * time_grid ) - 8, - sin( 2 * pi * time_grid ) - 9, - sin( 2 * pi * time_grid ) - 10), - nrow = 21, ncol = length( time_grid ), byrow = T ) - -N = nrow( D ) - -id_vector = 1 : N - -test_that( "Correctness of HRD", - expect_equal( HRD( D ), mapply( min, id_vector / N, ( N - id_vector + 1 ) / N ) ) ) - -test_that( "Correctness of MHRD", - expect_equal( MHRD( D ), mapply( min, id_vector / N, ( N - id_vector + 1 ) / N ) ) ) - - -# TEST BY JAMES LONG (TAMU) ------------------------------------------------------------------- - -yints = c( 1.27, .927, 1/2, .217, 0) -slopes = c( -1, -1, 0, 1, 1 ) -time_grid = ( 0 : 100 ) / 100 - -Data = matrix( 0, nrow = length( yints ), - ncol = length( time_grid ) ) - -for( i in 1 : length( yints ) ) - - Data[ i, ] = yints[ i ] + time_grid * slopes[ i ] - -test_that( "Correctness of HRD - James Long test", - expect_equal( HRD( Data ), rep( 0.2, 5 ) ) ) - - diff --git a/tests/testthat/test_MBD_relative.R b/tests/testthat/test_MBD_relative.R deleted file mode 100644 index 896dac2..0000000 --- a/tests/testthat/test_MBD_relative.R +++ /dev/null @@ -1,54 +0,0 @@ - -# TESTING MBDs ------------------------------------------------------------ - -time_grid = seq( 0, 1, length.out = 1e2 ) - - -Data_ref = matrix( c( 0 + sin( 2 * pi * time_grid ), - 1 + sin( 2 * pi * time_grid ), - -1 + sin( 2 * pi * time_grid ) ), - nrow = 3, ncol = length( time_grid ), byrow = TRUE ) - -Data_test_1 = matrix( c( 0.6 + sin( 2 * pi * time_grid ) ), - nrow = 1, ncol = length( time_grid ), byrow = TRUE ) - -Data_test_2 = matrix( c( 0.6 + sin( 2 * pi * time_grid ) ), - nrow = length( time_grid ), ncol = 1, byrow = TRUE ) - -Data_test_3 = 0.6 + sin( 2 * pi * time_grid ) - -Data_test_4 = array( 0.6 + sin( 2 * pi * time_grid ), dim = length( time_grid ) ) - -Data_test_5 = array( 0.6 + sin( 2 * pi * time_grid ), dim = c( 1, length( time_grid ) ) ) - -Data_test_6 = array( 0.6 + sin( 2 * pi * time_grid ), dim = c( length( time_grid ), 1 ) ) - -Data_test_7 = matrix( c( 0.5 + sin( 2 * pi * time_grid ), - -0.5 + sin( 2 * pi * time_grid ), - 1.1 + sin( 2 * pi * time_grid ) ), - nrow = 3, ncol = length( time_grid ), byrow = TRUE ) - -test_that( "Correctness of relative MBD (single test function in row matrix form)", - expect_equal( MBD_relative( Data_test_1, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative MBD (single test function in column matrix form)", - expect_equal( MBD_relative( Data_test_2, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative MBD (single test function in vector form)", - expect_equal( MBD_relative( Data_test_3, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative MBD (single test function in 1D array form)", - expect_equal( MBD_relative( Data_test_4, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative MBD (single test function in row-like 2D array form)", - expect_equal( MBD_relative( Data_test_5, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative MBD (single test function in column-like 2D array form)", - expect_equal( MBD_relative( Data_test_6, Data_ref ), 2/ 3 ) ) - -test_that( "Correctness of relative MBD (multiple test function)", - expect_equal( MBD_relative( Data_test_7, Data_ref ), c( 2/3, 2/3, 0 ) )) - - - - diff --git a/tests/testthat/test_MBD_with_tied_data.R b/tests/testthat/test_MBD_with_tied_data.R deleted file mode 100644 index 4f38a01..0000000 --- a/tests/testthat/test_MBD_with_tied_data.R +++ /dev/null @@ -1,56 +0,0 @@ - -# TESTING MBDs IN PRESENCE OF TIED DATA ----------------------------------- - -D = matrix( c( c( 1, 0.5, 0.25, 0.1, 0.05 ), - c( 1, 0.75, 0.25, 0.2, 0.1 ), - c( 1, 0.7, 0.25, 0.25, 0.15 ), - c( 1, 0.9, 0.35, 0.3, 0.25 ), - c( 1, 0.6, 0.25, 0.2, 0.2 ) , - c( 0.9, 0.8, 0.25, 0.1, 0.08 ), - c( 1, 0.4, 0.3, 0.2, 0.1 ), - c( 1, 0.4, 0.3, 0.2, 0.1 ) ), - ncol = 5, nrow = 8, byrow = T ) - -# Direct computation of MBDs -N = nrow( D ) -P = ncol( D ) - -depths = rep( 0, N ) - -for( i in 1 : N ) -{ - for( j in 1 : ( N - 1 ) ) - { - for( k in ( j + 1 ) : N ) - { - for( r in 1 : P ) - { - if( ( D[ j, r ] - D[ i, r ] ) * ( D[ k, r ] - D[ i, r ] ) <= 0 ) - { - depths[ i ] = depths[ i ] + 1 - } - } - } - } -} -depths = depths / ( N * ( N - 1 ) / 2 * P ) - -test_that( "Correct behaviour of MBD in presence of ties", - expect_equal( depths, MBD( D, manage_ties = TRUE ) ) ) - - -# TESTING NO MANAEGMENT OF TIES ------------------------------------------- - -N = 3 -P = 1e2 - -time_grid = seq( 0, 1, length.out = P ) - -Data = matrix( c( 0 + sin( 2 * pi * time_grid ), - 1 + sin( 2 * pi * time_grid ), - -1 + sin( 2 * pi * time_grid ) ), - nrow = 3, ncol = length( time_grid ), byrow = TRUE ) - -test_that( "Correct behaviour of MBD without checking for ties", - expect_equal( MBD( Data, manage_ties = TRUE ), - MBD( Data, manage_ties = FALSE ) ) ) diff --git a/tests/testthat/test_correlation.R b/tests/testthat/test_correlation.R deleted file mode 100644 index e394e58..0000000 --- a/tests/testthat/test_correlation.R +++ /dev/null @@ -1,409 +0,0 @@ - -# TESTING MAX-MIN FUNCTIONS ----------------------------------------------- - -P = 1e4 - -time_grid = seq( 0, 1, length.out = P ) - -h = time_grid[ 2 ] - time_grid[ 1 ] - -Data = matrix( c( 1 * time_grid, - 2 * time_grid, - 3 * ( 0.5 - abs( time_grid - 0.5 ) ) ), - nrow = 3, ncol = P, byrow = TRUE ) - -fD = fData( time_grid, Data ) - -test_that( 'Max function for functional data, which = TRUE, grid', - expect_equal( maxima( fD, which = TRUE )$grid, - c( 1, 1, 0 + 4999 * h ) ) ) - -test_that( 'Max function for functional data, which = TRUE, value', - expect_equal( maxima( fD, which = TRUE )$value, - c( 1, 2, 3 * ( 0.5 - abs( 0.5 - 4999 * h) ) ) ) ) - -test_that( 'Min function for functional data, which = TRUE, grid', - expect_equal( minima( fD, which = TRUE )$grid, - c( 0, 0, 0 ) ) ) - -test_that( 'Min function for functional data, which = TRUE, value', - expect_equal( minima( fD, which = TRUE )$value, - c( 0, 0, 0 ) ) ) - -test_that( 'Max function for functional data, which = FALSE', - expect_equal( maxima( fD, which = FALSE ), - c( 1, 2, 3 * ( 0.5 - abs( 0.5 - 4999 * h) ) ) ) ) - -test_that( 'Min function for functional data, which = FALSE', - expect_equal( minima( fD, which = FALSE ), - c( 0, 0, 0 ) ) ) - -test_that( 'Area under the curve - 1', - expect_equal( area_under_curve( fD ), - c( 0.5, 1, 0.75 ) ) ) - -fD = fData( time_grid, - matrix( c( sin( 2 * pi * time_grid ), - cos( 2 * pi * time_grid ), - 4 * time_grid * ( 1 - time_grid ) ), - nrow = 3, ncol = P, byrow = TRUE ) ) - -test_that( 'Area under the curve - 2', - expect_true( - all( c( area_under_curve( fD )[1:2], - abs( area_under_curve( fD[ 3, ] ) - 2/3 ) ) <= - .Machine$double.eps^0.5 ) ) ) - - -# ORDERING ---------------------------------------------------------------- - -P = 1e3 - -time_grid = seq( 0, 1, length.out = P ) - -h = time_grid[ 2 ] - time_grid[ 1 ] - -Data_1 = matrix( c( 1 * time_grid, - 2 * time_grid ), - nrow = 2, ncol = P, byrow = TRUE ) - -Data_2 = matrix( 3 * ( 0.5 - abs( time_grid - 0.5 ) ), - nrow = 1, byrow = TRUE ) - -Data_3 = rbind( Data_1, Data_1 ) - - -fD_1 = fData( time_grid, Data_1 ) -fD_2 = fData( time_grid, Data_2 ) -fD_3 = fData( time_grid, Data_3 ) - -# MAX ORDERING -test_that( 'Max_ordering - case 1', - expect_equal( max_ordered( fD_1, fD_2 ), - c( TRUE, FALSE ) ) ) - -test_that( 'Max_ordering - case 2', - expect_equal( max_ordered( fD_2, fD_1 ), - c( FALSE, TRUE ) ) ) - -test_that( 'Max_ordering - case 3', - expect_error( max_ordered( fD_1, fD_3 ) ) ) - -test_that( 'Max_ordering - case 4', - expect_error( max_ordered( fD_3, fD_1 ) ) ) - -test_that( 'Max_ordering - case 5', - expect_equal( max_ordered( fD_2, fD_3 ), - c( FALSE, TRUE, FALSE, TRUE ) ) ) - -test_that( 'Max_ordering - case 6', - expect_equal( max_ordered( fD_3, fD_2 ), - c( TRUE, FALSE, TRUE, FALSE ) ) ) - -# AREA ORDERING -test_that( 'Area ordering - case 1', - expect_equal( area_ordered( fD_1, fD_2 ), - c( TRUE, FALSE ) ) ) - -test_that( 'Area ordering - case 2', - expect_equal( area_ordered( fD_2, fD_1 ), - c( FALSE, TRUE ) ) ) - -test_that( 'Area ordering - case 3', - expect_error( area_ordered( fD_1, fD_3 ) ) ) - -test_that( 'Area ordering - case 4', - expect_error( area_ordered( fD_3, fD_1 ) ) ) - -test_that( 'Area ordering - case 5', - expect_equal( area_ordered( fD_2, fD_3 ), - c( FALSE, TRUE, FALSE, TRUE ) ) ) - -test_that( 'Area ordering - case 6', - expect_equal( area_ordered( fD_3, fD_2 ), - c( TRUE, FALSE, TRUE, FALSE ) ) ) - - -# KENDALL CORRELATION ----------------------------------------------------- - -N = 2e2 - -P = 1e3 - -t0 = 0 -t1 = 1 - -time_grid = seq( t0, t1, length.out = P ) - -Cov = exp_cov_function( time_grid, alpha = 0.3, beta = 0.4 ) - -Data_1 = generate_gauss_fdata( N, centerline = sin( 2 * pi * time_grid ), Cov = Cov ) -Data_2 = generate_gauss_fdata( N, centerline = sin( 2 * pi * time_grid ), Cov = Cov ) - -mfD = mfData( time_grid, list( Data_1, Data_2 ) ) - -test_that( 'Kendall correlation with max ordering ', - expect_silent( invisible( cor_kendall( mfD, ordering = 'max' ) ) ) ) - -test_that( 'Kendall correlation with area ordering', - expect_silent( invisible( cor_kendall( mfD, ordering = 'area' ) ) ) ) - -# SPEARMAN RANK CORRELATION ----------------------------------------------- - -N = 2e2 - -P = 1e3 - -t0 = 0 -t1 = 1 - -time_grid = seq( t0, t1, length.out = P ) - -Cov = exp_cov_function( time_grid, alpha = 0.3, beta = 0.4 ) - -Data_1 = generate_gauss_fdata( N, centerline = sin( 2 * pi * time_grid ), Cov = Cov ) -Data_2 = generate_gauss_fdata( N, centerline = sin( 2 * pi * time_grid ), Cov = Cov ) - -mfD = mfData( time_grid, list( Data_1, Data_2 ) ) - -test_that( 'Kendall correlation with MEI ordering ', - expect_silent( invisible( cor_spearman( mfD, ordering = 'MEI' ) ) ) ) - -test_that( 'Kendall correlation with MHI ordering', - expect_silent( invisible( cor_spearman( mfD, ordering = 'MHI' ) ) ) ) - - -# CASE STUDIES ------------------------------------------------------------ - -# ( With reference to the paper by Dalia Valencia, Rosa Lillo e Juan Romo) - -N = 50 -P = 50 - -t0 = 0 -t1 = 1 -time_grid = seq( t0, t1, length.out = P ) - -# Case 1 -sigma_12 = 0.8 - -R = matrix( c( 1, sigma_12, sigma_12, 1 ), ncol = 2, nrow = 2 ) - -Z = matrix( rnorm( N * 2, 0, 1 ), ncol = 2, nrow = N ) %*% chol( R ) - -# X = t( ( t( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) ) + Z[ 1,] ) )^3 -X = ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 1 ] )^3 + - ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 1 ] )^2 + - ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 1 ] ) * 3 - -Y = ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 2 ] )^2 + - ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 2 ] ) * 7 / 8 + - - 10 - -mfD = mfData( time_grid, list( X, Y ) ) - -cor_kendall( mfD, ordering = 'max' ) -cor_kendall( mfD, ordering = 'area' ) -cor_spearman( mfD, ordering = 'MEI' ) -cor_spearman( mfD, ordering = 'MHI' ) - -# Case 2 -sigma_12 = - 0.7 - -R = matrix( c( 1, sigma_12, sigma_12, 1 ), ncol = 2, nrow = 2 ) - -Z = matrix( rnorm( N * 2, 0, 1 ), ncol = 2, nrow = N ) %*% chol( R ) - -X = sin( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 1 ] ) - -Y = cos( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 2 ] ) - -mfD = mfData( time_grid, list( X, Y ) ) - -cor_kendall( mfD, ordering = 'max' ) -cor_kendall( mfD, ordering = 'area' ) -cor_spearman( mfD, ordering = 'MEI' ) -cor_spearman( mfD, ordering = 'MHI' ) - - -# Case 3 -sigma_12 = 1 - -Z = rnorm( N, 0, 1) - -X = ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z )^2 - -Y = ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z )^4 - -mfD = mfData( time_grid, list( X, Y ) ) - -test_that( ' Kendall correlation coeff with max ordering - Case 3', - expect_equal( cor_kendall( mfD, ordering = 'max' ), 1 ) ) -test_that( ' Kendall correlation coeff with area ordering - Case 3', - expect_equal( cor_kendall( mfD, ordering = 'area' ), 1 ) ) -test_that( ' Spearman correlation coeff with MEI ordering - Case 3', - expect_equal( cor_spearman( mfD, ordering = 'MEI' ), 1 ) ) -test_that( ' Spearman correlation coeff with MHI ordering - Case 3', - expect_equal( cor_spearman( mfD, ordering = 'MHI' ), 1 ) ) - - -# Case 4 -sigma_12 = 1 - -Z = rnorm( N, 0, 1) - -X = ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z )^2 + - ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z ) * 7 + - 2 - -Y = ( ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z )^2 + - ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z ) * 7 + - 2 )^3 -mfD = mfData( time_grid, list( X, Y ) ) - -test_that( ' Kendall correlation coeff with max ordering - Case 4', - expect_equal( cor_kendall( mfD, ordering = 'max' ), 1 ) ) -test_that( ' Kendall correlation coeff with area ordering - Case 4', - expect_equal( cor_kendall( mfD, ordering = 'area' ), 1 ) ) -test_that( ' Spearman correlation coeff with MEI ordering - Case 4', - expect_equal( cor_spearman( mfD, ordering = 'MEI' ), 1 ) ) -test_that( ' Spearman correlation coeff with MHI ordering - Case 4', - expect_equal( cor_spearman( mfD, ordering = 'MHI' ), 1 ) ) - -# Case 5 -sigma_12 = 1 - -Z = rnorm( N, 0, 1) - -X = ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z )^2 + - ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z ) * 7 + - 2 - -Y = 1 - ( ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z )^2 + - ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z ) * 7 + - 2 )^3 -mfD = mfData( time_grid, list( X, Y ) ) - -test_that( ' Kendall correlation coeff with max ordering - Case 5', - expect_equal( cor_kendall( mfD, ordering = 'max' ), -1 ) ) -test_that( ' Kendall correlation coeff with area ordering - Case 5', - expect_equal( cor_kendall( mfD, ordering = 'area' ), -1 ) ) -test_that( ' Spearman correlation coeff with MEI ordering - Case 5', - expect_equal( cor_spearman( mfD, ordering = 'MEI' ), -1 ) ) -test_that( ' Spearman correlation coeff with MHI ordering - Case 5', - expect_equal( cor_spearman( mfD, ordering = 'MHI' ), -1 ) ) - - -# Case 6 -sigma_12 = 0.6 - -R = matrix( c( 1, sigma_12, sigma_12, 1 ), ncol = 2, nrow = 2 ) - -Z = matrix( rnorm( N * 2, 0, 1 ), ncol = 2, nrow = N ) %*% chol( R ) - -X = exp( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 1 ] ) - -Y = ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 2 ] )^3 + - ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 2 ] )^2 + - ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 2 ] ) * 3 - -mfD = mfData( time_grid, list( X, Y ) ) - -cor_kendall( mfD, ordering = 'max' ) -cor_kendall( mfD, ordering = 'area' ) -cor_spearman( mfD, ordering = 'MEI' ) -cor_spearman( mfD, ordering = 'MHI' ) - - -# Case 7 -sigma_12 = -0.8 - -R = matrix( c( 1, sigma_12, sigma_12, 1 ), ncol = 2, nrow = 2 ) - -Z = matrix( rnorm( N * 2, 0, 1 ), ncol = 2, nrow = N ) %*% chol( R ) - -X = exp( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 1 ] )^2 - -Y = cos( ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 2 ] ) ) - -mfD = mfData( time_grid, list( X, Y ) ) - -cor_kendall( mfD, ordering = 'max' ) -cor_kendall( mfD, ordering = 'area' ) -cor_spearman( mfD, ordering = 'MEI' ) -cor_spearman( mfD, ordering = 'MHI' ) - - -# Case 8 -sigma_12 = 0.4 - -R = matrix( c( 1, sigma_12, sigma_12, 1 ), ncol = 2, nrow = 2 ) - -Z = matrix( rnorm( N * 2, 0, 1 ), ncol = 2, nrow = N ) %*% chol( R ) - -X = sin( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 1 ] ) - -Y = ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 2 ] )^2 - -mfD = mfData( time_grid, list( X, Y ) ) - -cor_kendall( mfD, ordering = 'max' ) -cor_kendall( mfD, ordering = 'area' ) -cor_spearman( mfD, ordering = 'MEI' ) -cor_spearman( mfD, ordering = 'MHI' ) - -# Case 9 -sigma_12 = 1 - -Z = rnorm( N, 0, 1 ) - -X = ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z )^2 + - ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z ) * 9 + - - 5 - -Y = cos( matrix( 3 * time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z ) - -mfD = mfData( time_grid, list( X, Y ) ) - -cor_kendall( mfD, ordering = 'max' ) -cor_kendall( mfD, ordering = 'area' ) -cor_spearman( mfD, ordering = 'MEI' ) -cor_spearman( mfD, ordering = 'MHI' ) - -# Case 10 -sigma_12 = 0.9 - -R = matrix( c( 1, sigma_12, sigma_12, 1 ), ncol = 2, nrow = 2 ) - -Z = matrix( rnorm( N * 2, 0, 1 ), ncol = 2, nrow = N ) %*% chol( R ) - -X = exp( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE )^2 + Z[ , 1 ] ) - -Y = ( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 2 ] )^2 + - matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) * ( - 8 ) + - ( matrix( 0, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 2 ] ) - -mfD = mfData( time_grid, list( X, Y ) ) - -cor_kendall( mfD, ordering = 'max' ) -cor_kendall( mfD, ordering = 'area' ) -cor_spearman( mfD, ordering = 'MEI' ) -cor_spearman( mfD, ordering = 'MHI' ) - -# Case 11 -sigma_12 = 0. - -R = matrix( c( 1, sigma_12, sigma_12, 1 ), ncol = 2, nrow = 2 ) - -Z = matrix( rnorm( N * 2, 0, 1 ), ncol = 2, nrow = N ) %*% chol( R ) - -X = exp( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE )+ Z[ , 1 ] ) - -Y = sin( matrix( time_grid, nrow = N, ncol = P, byrow = TRUE ) + Z[ , 2 ] ) - -mfD = mfData( time_grid, list( X, Y ) ) - -cor_kendall( mfD, ordering = 'max' ) -cor_kendall( mfD, ordering = 'area' ) -cor_spearman( mfD, ordering = 'MEI' ) -cor_spearman( mfD, ordering = 'MHI' ) diff --git a/tests/testthat/test_fData.R b/tests/testthat/test_fData.R deleted file mode 100644 index b67f44c..0000000 --- a/tests/testthat/test_fData.R +++ /dev/null @@ -1,401 +0,0 @@ - - -# TESTING FDATA ----------------------------------------------------------- - -N = 1e2 - -P = 1e3 -t0 = 0 -t1 = 1 - -time_grid = seq( t0, t1, length.out = P ) - -Cov = exp_cov_function( time_grid, alpha = 0.3, beta = 0.4 ) - -Data = generate_gauss_fdata( N, centerline = sin( 2 * pi * time_grid ), Cov = Cov ) - -test_that( 'Creation of fData object', - expect_silent( fData( time_grid, Data ) ) ) - -fD = fData( time_grid, Data ) - -test_that( 'Plot of fData object', - expect_silent( plot.fData( fData( time_grid, Data ), - xlab = 'time', ylab = 'values', - main = 'A functional dataset' ) ) ) - -test_that( 'Creation of 1-element fData object', - expect_silent( fData( time_grid, 1 : P ) ) ) - - -# TESTING STATISTICS OPERATIONS ------------------------------------------- - -test_that( 'Mean of fData object', - expect_equal( as.numeric( mean( fD )$values ), - colMeans( fD$values ) ) ) - -test_that( 'Median of fData obejct - MBD', - expect_equal( as.numeric( median_fData( fD )$value ), - fD$values[ which.max( MBD( fD$values ) ), ]) ) - -test_that( 'Median of fData object - MBD with ties', - expect_equal( as.numeric( median_fData( fD, type = 'MBD', - manage_ties = TRUE )$values ), - fD$values[ which.max( MBD( fD$values, - manage_ties = TRUE ) ), ] ) ) - -test_that( 'Median of fData obejct - MHRD', - expect_equal( as.numeric( median_fData( fD, type = 'MHRD' )$value ), - fD$values[ which.max( MHRD( fD$values ) ), ]) ) - -test_that( 'Covariance of fData object', - expect_equal( cov( fD$values ), cov_fun( fD )$values ) ) - -test_that( 'Covariance of fData object - error 1', - expect_error( cov_fun( 1 ) ) ) - -test_that( 'Covariance of fData object - error 2', - expect_error( cov_fun( fD, fD[-1,] ) ) ) - -test_that( 'Covariance of fData object - error 3', - expect_error( cov_fun( fD, fD[,1:10] ) ) ) - -test_that( 'Cross covariance of fData objects', - expect_equal( cov( fD$values, ( fD + 1 : P )$values ), - cov_fun( fD, fD + 1 : P )$values ) ) - -N = 1e2 - -P = 1e2 -t0 = 0 -t1 = 1 - -time_grid = seq( t0, t1, length.out = P ) - -Cov = exp_cov_function( time_grid, alpha = 0.3, beta = 0.4 ) - -Data = generate_gauss_fdata( N, centerline = sin( 2 * pi * time_grid ), Cov = Cov ) - -fD = fData( time_grid, Data ) -mfD = mfData( time_grid, list( 'comp1' = Data, 'comp2' = Data, 'comp3' = Data ) ) -mfD2 = mfData( time_grid, list( Data, Data, Data ) ) -mfD3 = mfData( time_grid, list( Data, Data ) ) - -rhs = lapply( 1 : ( 2 * mfD$L ), - function( i ) cov_fun( mfD$fDList[[1]] ) ) -names( rhs ) = c( 'comp1_1', 'comp1_2', 'comp1_3', - 'comp2_2', 'comp2_3', - 'comp3_3' ) - - -test_that( 'Cross covariance of mfData and mfData', - expect_equal( cov_fun( mfD, mfD2 ), - rhs ) ) - -test_that( 'Cross covariance of mfData and fData', - expect_equal( cov_fun( mfD2, fD ), - lapply( 1 : mfD$L, - function( i ) cov_fun( mfD$fDList[[1]] ) ) ) ) - -test_that( 'Cross covariance of mfData and mfData - error ', - expect_error( cov_fun( mfD, mfD3 ) ) ) - -test_that( 'Cross covariance of mfData and fData - error ', - expect_error( cov_fun( mfD, fD[ , 1:10 ] ) ) ) - -test_that( 'Covariance of mfData - names 1', - expect_equal( names( cov_fun( mfD ) ), - c( 'comp1_comp1', 'comp1_comp2', 'comp1_comp3', - 'comp2_comp2', 'comp2_comp3', - 'comp3_comp3' ) ) ) - -test_that( 'Covariance of mfData - names 2', - expect_equal( names( cov_fun( mfD2 ) ), - c( '1_1', '1_2', '1_3', - '2_2', '2_3', - '3_3' ) ) ) - -# TESTING ALGEBRAIC OPERATIONS -------------------------------------------- - -fD = fData( seq( 0, 1, length.out = 10 ), values = matrix( seq( 1, 10 ), - nrow = 21, ncol = 10, - byrow = TRUE ) ) - -test_that( 'Sum of fData and raw vector', - expect_equal( sum( ( fD + 1 : 10 )$values - - matrix( 2 * seq( 1, 10 ), nrow = 21, ncol = 10, - byrow = TRUE ) ), 0 ) ) - -test_that( 'Sum of fData and raw vector', - expect_equal( sum( ( fD - 1 : 10 )$values ), 0 ) ) - -test_that( 'Sum of fData and array', - expect_equal( sum( ( fD + array( 1, dim = c( 1, 10 ) ) )$values - - matrix( seq( 2, 11 ), - nrow = 21, ncol = 10, byrow = TRUE ) ), - 0 ) ) - -fD.2 = fData( seq( 0, 1, length.out = 11 ), values = matrix( seq( 1, 11 ), - nrow = 21, ncol = 11, - byrow = TRUE ) ) - -test_that( 'Sum of two compliant fData objects', - expect_equal( sum( ( fD + fD - - matrix( 2 * seq( 1, 10 ), - nrow = 21, ncol = 10, - byrow = TRUE ) )$values ), 0 ) ) - -test_that( 'Sum of two noncompliant fData objects', - expect_error( fD + fD.2, regexp = 'Error.*') ) - -test_that( 'Product of fData object with scalar', - expect_equal( fD * 2, fD + fD ) ) - -test_that( 'Division of fData object by scalar', - expect_equal( ( fD * 4 ) / 2, fD + fD ) ) - - -# TESTING FUNCTIONAL DATA SUBSETTING -------------------------------------- - -test_that( 'Functional data subsetting - case 1', - expect_identical( fD[ 1, ], - fData( seq( 0, 1, length.out = 10 ), 1 : 10 ) ) ) - -test_that( 'Functional data subsetting - case 2', - expect_identical( fD[ , 1:2 ], - fData( seq( 0, 1, length.out = 10 )[1:2], - matrix( 1 : 2, - nrow = 21, ncol = 2, - byrow = T ) ) ) ) - -test_that( 'Functional data subsetting - case 3', - expect_identical( fD[ 1:2, 1:2, as_fData = FALSE ], - matrix( seq( 1, 2 ), - nrow = 2, ncol = 2, - byrow = TRUE ) ) ) - -fD_logical_subset = fD[ , c( rep( FALSE, 5 ), rep( TRUE, 5 ) ) ] - -test_that( 'Functional data subsetting - case 4', - expect_identical( fD_logical_subset$values, - matrix( seq( 6, 10 ), - nrow = 21, ncol = 5, - byrow = TRUE ) ) ) - -test_that( 'Functional data subsetting - case 4 bis', - expect_identical( c( fD_logical_subset$t0, fD_logical_subset$tP, fD_logical_subset$h, fD_logical_subset$P ), - c( 5 * fD$h, 1, fD$h, 5 ) ) ) - -fD_logical_subset = fD[ , c( rep( TRUE, 5 ), rep( FALSE, 5 ) ) ] - -test_that( 'Functional data subsetting - case 5', - expect_identical( fD_logical_subset$values, - matrix( seq( 1, 5 ), - nrow = 21, ncol = 5, - byrow = TRUE ) ) ) - -test_that( 'Functional data subsetting - case 5 bis', - expect_identical( c( fD_logical_subset$t0, fD_logical_subset$tP, fD_logical_subset$h, fD_logical_subset$P ), - c( 0, 4 * fD$h, fD$h, 5 ) ) ) - - - -N = 3 -P = 20 -L = 2 - -grid = seq( 0, 1, length.out = P ) - -values1 = rep( 1, P) -values2 = cos( 2 * pi * grid ) -values3 = sin( 2 * pi * grid ) - -values4 = rep( 2, P) -values5 = cos( 4 * pi * grid ) -values6 = sin( 4 * pi * grid ) - -mfD = mfData( grid, list( matrix( c( values1, values2, values3 ), nrow=3, ncol=P, byrow=TRUE), - matrix( c( values4, values5, values6 ), nrow=3, ncol=P, byrow=TRUE)) ) - - -test_that( 'Multivariate functional data subsetting - case 1', - expect_error( mfD[ , c(1:5, 7:8)])) - -test_that( 'Multivariate functional data subsetting - case 2', - expect_equal( mfD[1,]$fDList[[1]]$values, t(as.matrix(values1)) ) ) - -test_that( 'Multivariate functional data subsetting - case 3', - expect_equal( mfD[1,]$fDList[[2]]$values, t(as.matrix(values4)) ) ) - -test_that( 'Multivariate functional data subsetting - case 4', - expect_equal( mfD[1:2,]$fDList[[1]]$values, matrix(c(values1, values2), nrow=2, - ncol=P, byrow=TRUE) ) ) -test_that( 'Multivariate functional data subsetting - case 5', - expect_equal( mfD[1:2,]$fDList[[2]]$values, matrix(c(values4, values5), nrow=2, - ncol=P, byrow=TRUE) ) ) -test_that( 'Multivariate functional data subsetting - case 6', - expect_equal( mfD[,1:5]$fDList[[1]]$values, matrix(c(values1[1:5], values2[1:5], - values3[1:5]), nrow=3, - ncol=5, byrow=TRUE) ) ) -test_that( 'Multivariate functional data subsetting - case 7', - expect_equal( mfD[,1:5]$fDList[[2]]$values, matrix(c(values4[1:5], values5[1:5], - values6[1:5]), nrow=3, - ncol=5, byrow=TRUE) ) ) -test_that( 'Multivariate functional data subsetting - case 8', - expect_true( (mfD[1:2,]$N == 2) & (mfD[1:2,]$P == P) & (mfD[1:2,]$L == 2 ))) - -test_that( 'Multivariate functional data appending - case 1', - expect_equal( append_mfData(mfD[1:2,], mfD[3,])$fDList[[1]]$values, mfD$fDList[[1]]$values )) - -test_that( 'Multivariate functional data appending - case 2', - expect_equal( append_fData(mfD[1:2,]$fDList[[1]], mfD[3,]$fDList[[1]])$values, - mfD$fDList[[1]]$values )) - -# TESTING MFDATA ---------------------------------------------------------- - -N = 1e2 - -P = 1e3 - -t0 = 0 -t1 = 1 - -time_grid = seq( t0, t1, length.out = P ) - -Cov = exp_cov_function( time_grid, alpha = 0.3, beta = 0.4 ) - -Data_1 = generate_gauss_fdata( N, centerline = sin( 2 * pi * time_grid ), Cov = Cov ) -Data_2 = generate_gauss_fdata( N, centerline = sin( 2 * pi * time_grid ), Cov = Cov ) - -test_that( 'Creation of mfData object', - expect_silent( mfData( time_grid, list( Data_1, Data_2 ) ) ) ) - -test_that( 'Creation of 1-element mfData object', - expect_silent( mfData( time_grid, list( 1 : P, - 1 : P ) ) ) ) - -test_that( 'Plot of mfData object', - expect_silent( plot.mfData( mfData( time_grid, - list( Data_1, Data_2 ) ), - xlab = 'time', ylab = list( 'values', - 'values' ), - main = list( 'First Component', - 'Second Component' ) ) ) ) -mfD = mfData( time_grid, - list( Data_1, Data_2 )) - -test_that( 'Plotting a component', - expect_silent( plot( mfD$fDList[[ 1 ]] ) ) ) - - -test_that( 'As.mfdata.list', - expect_silent( as.mfData( list( fData( time_grid, - Data_1 ), - fData( time_grid, - Data_2 ) - ) ) ) ) - - -test_that( 'Extracting list of values from multivariate functional dataset', - expect_identical( toListOfValues( mfData( time_grid, - list( Data_1, Data_2 ) ) ), - list( Data_1, Data_2 ) ) ) - - -# TESTING FUNCTIONAL DATA UNFOLDING --------------------------------------- - -P = 1e3 - -t0 = 0 -t1 = 1 - -time_grid = seq( t0, t1, length.out = P ) - -D = matrix( c( sin( 2 * pi * time_grid ), - cos( 2 * pi * time_grid ), - sin( 10 * pi * time_grid ) * time_grid + 2 ), - ncol = P, nrow = 3, byrow = TRUE ) - -fD = fData( time_grid, D ) - -fD_unfold = unfold( fD ) - -# Ana's implementation -mon_func = function( x ) -{ - x = as.matrix(x) - - if ( ncol( x ) == 1 ) - { - x = t( x ) - } - P = ncol( x ) - - x_mon = x - - diff_x = t( abs( apply( x_mon, 1, diff ) ) ) - - for ( j in 2 : P ) - { - x_mon[ , j ] = x_mon[ , j - 1 ] + diff_x[ , j - 1 ] - } - return( x_mon ) -} - - - -test_that( ' Unfolding of univariate functional dataset', - expect_true( all( apply( fD_unfold$values, - 1, - function( x ) ( all( diff( x ) >= 0 ) ) ) - ) ) ) - -test_that( ' Unfolding of univariate functional dataset - - consistency with Ana\'s', - expect_equal( fD_unfold$values, - mon_func( fD$values ) ) ) - - -# TESTING WARPING --------------------------------------------------------- - -N = 30 - -t0 = 0 -t1 = 1 -P = 1e3 + 1 - -time_grid = seq( t0, t1, length.out = P ) - -means = round( runif( N, - t0 + (t1 - t0) / 8, - t1 - (t1 - t0) / 8 ), 3 ) - -Data = matrix( sapply( means, - function( m )( dnorm( time_grid, mean = m, sd = 0.05 ) ) ), - ncol = P, nrow = N, byrow = TRUE ) - -fD = fData( time_grid, Data ) - -# Piecewise linear warpings -template_warping = function( m )( c( time_grid[ time_grid <= 0.5 ] * m / 0.5, - ( time_grid[ time_grid > 0.5 ] - - 0.5 ) * (1 - m ) / 0.5 + m ) ) - - -warpings = matrix( sapply( means, template_warping ), - ncol = P, - nrow = N, byrow = T ) - -wfD = fData( time_grid, warpings ) - -fD_warped = warp( fD, wfD ) - -test_that( ' Warping - max', - expect_true( all( maxima( fD_warped ) - - dnorm( 0.5, 0.5, 0.05 ) <= .Machine$double.eps ) - ) ) - -test_that( ' Warping - which.max', - expect_true( all( maxima( fD_warped, which = TRUE )$grid - - 0.5 <= .Machine$double.eps ) - ) ) diff --git a/tests/testthat/test_fbplot.R b/tests/testthat/test_fbplot.R deleted file mode 100644 index d83239a..0000000 --- a/tests/testthat/test_fbplot.R +++ /dev/null @@ -1,137 +0,0 @@ - - - -# TESTING THE FUNCTIONAL BOXPLOT FOR UNVIARIATE FUNCTIONAL DATA ----------- - -time_grid = seq( 0, 1, length.out = 1e2 ) - -D = matrix( c( sin( 2 * pi * time_grid ) + 0, - sin( 2 * pi * time_grid ) + 1, - sin( 2 * pi * time_grid ) + 2, - sin( 2 * pi * time_grid ) + 3, - sin( 2 * pi * time_grid ) + 4, - sin( 2 * pi * time_grid ) + 5, - sin( 2 * pi * time_grid ) + 6, - sin( 2 * pi * time_grid ) + 7, - sin( 2 * pi * time_grid ) + 8, - sin( 2 * pi * time_grid ) + 9, - sin( 2 * pi * time_grid ) + 10, - sin( 2 * pi * time_grid ) - 1, - sin( 2 * pi * time_grid ) - 2, - sin( 2 * pi * time_grid ) - 3, - sin( 2 * pi * time_grid ) - 4, - sin( 2 * pi * time_grid ) - 5, - sin( 2 * pi * time_grid ) - 6, - sin( 2 * pi * time_grid ) - 7, - sin( 2 * pi * time_grid ) - 8, - sin( 2 * pi * time_grid ) - 9, - sin( 2 * pi * time_grid ) - 10), - nrow = 21, ncol = length( time_grid ), byrow = T ) - -fD = fData( time_grid, D ) - -test_that( 'Functional boxplot for univariate data - simple - 1', - expect_silent( fbplot( fD, Fvalue = 10, display = FALSE ) ) ) - -test_that( 'Functional boxplot for univariate data - simple - 2', - expect_silent( - fbplot( fD, display = FALSE, xlab = 'time', ylab = 'value', - main = 'My Functional Boxplot' ) - ) ) - - -# TESTING THE ADJUSTED FUNCTIONAL BOXPLOT OF UNIVARIATE DATA -------------- - -set.seed( 161803 ) - -time_grid = seq( 0, 1, length.out = 1e2 ) - -N = 5e2 - -Data = generate_gauss_fdata( N, centerline = sin( 2 * pi * time_grid ), - Cov = exp_cov_function( time_grid, - alpha = 0.3, - beta = 0.4 ) ) -fD = fData( time_grid, Data ) - -test_that( 'Functional boxplot for univariate data - adjusted',{ - testthat::skip_on_cran() - expect_silent( - fbplot( fD, adjust = list( N_trials = 10, - trial_size = N, - VERBOSE = FALSE ), - display = FALSE, - xlab = 'time', ylab = 'Values', - main = 'My adjusted functional boxplot' ) - ) }) - -test_that( 'Functional boxplot for univariate data - warning generation', - expect_warning( fbplot( fD, - adjust = list( N_trials = 1, trial_size = N, - foo = 'bar', baz = 'qux' ), - display = FALSE ) ) ) - - -# TESTING THE FUNCTIONAL BOXPLOT FOR MULTIVARIATE FUNCTIONAL DATA --------- - -# 1 ) - -time_grid = seq( 0, 1, length.out = 1e2 ) - -D = matrix( c( sin( 2 * pi * time_grid ) - 10, - sin( 2 * pi * time_grid ) - 9, - sin( 2 * pi * time_grid ) - 8, - sin( 2 * pi * time_grid ) - 7, - sin( 2 * pi * time_grid ) - 6, - sin( 2 * pi * time_grid ) - 5, - sin( 2 * pi * time_grid ) - 4, - sin( 2 * pi * time_grid ) - 3, - sin( 2 * pi * time_grid ) - 2, - sin( 2 * pi * time_grid ) - 1, - sin( 2 * pi * time_grid ) + 0, - sin( 2 * pi * time_grid ) + 1, - sin( 2 * pi * time_grid ) + 2, - sin( 2 * pi * time_grid ) + 3, - sin( 2 * pi * time_grid ) + 4, - sin( 2 * pi * time_grid ) + 5, - sin( 2 * pi * time_grid ) + 6, - sin( 2 * pi * time_grid ) + 7, - sin( 2 * pi * time_grid ) + 8, - sin( 2 * pi * time_grid ) + 9, - sin( 2 * pi * time_grid ) + 10 ), - nrow = 21, ncol = length( time_grid ), byrow = T ) - -mfD = mfData( time_grid, list( D, D * abs( 1 : 21 - 11 ) / 5 ) ) - -test_that( 'Functional boxplot for multivariate data - simple - 1', - expect_silent( fbplot( mfD, Fvalue = 3, display = FALSE ) ) ) - -test_that( 'Functional boxplot for multivariate data - simple - 2', - expect_error( fbplot( mfD, adjust = list( N_trials = 2 ), display = FALSE ) ) ) - -# 2 ) - -set.seed( 1618033 ) - -P = 1e2 -N = 1e2 -L = 3 - -time_grid = seq( 0, 1, length.out = 1e2 ) - -C1 = exp_cov_function( time_grid, alpha = 0.3, beta = 0.4 ) -C2 = exp_cov_function( time_grid, alpha = 0.3, beta = 0.4 ) -C3 = exp_cov_function( time_grid, alpha = 0.3, beta = 0.4 ) - -Data = generate_gauss_mfdata( N, L, - centerline = matrix( sin( 2 * pi * time_grid ), - nrow = 3, ncol = P, - byrow = TRUE ), - correlations = rep( 0.5, 3 ), - listCov = list( C1, C2, C3 ) ) - -mfD = mfData( time_grid, Data ) - -test_that( 'Functional boxplot for multivariate data - simple - 3', - expect_silent( fbplot( mfD, Fvalue = 2.5, display = FALSE ) ) ) - diff --git a/tests/testthat/test_multiMBD.R b/tests/testthat/test_multiMBD.R deleted file mode 100644 index 7f9a3de..0000000 --- a/tests/testthat/test_multiMBD.R +++ /dev/null @@ -1,88 +0,0 @@ - -# TESTING MULTIVARIATE DEPTHS --------------------------------------------- - -N = 1e2 -P = 1e3 - -time_grid = seq( 0, 10, length.out = P ) - -Cov = exp_cov_function( time_grid, alpha = 0.2, beta = 0.8 ) - -Data_1 = generate_gauss_fdata( N, centerline = rep( 0, P ), Cov = Cov ) - -Data_2 = generate_gauss_fdata( N, centerline = rep( 0, P ), Cov = Cov ) - -# Multivariate modified band depths - -test_that( 'Multivariate Modified Band Depths - with management of ties', - expect_equal( multiMBD( list( Data_1, Data_2 ), weights = 'uniform', - manage_ties = TRUE ) - - ( MBD( Data_1, manage_ties = TRUE ) + - MBD( Data_2, manage_ties = TRUE ) ) / 2, - rep( 0, N ) ) - ) - -test_that( 'Multivariate Modified Band Depths - without management of ties', - expect_equal( multiMBD( list( Data_1, Data_2 ), weights = 'uniform', - manage_ties = FALSE ) - - ( MBD( Data_1, manage_ties = FALSE ) + - MBD( Data_2, manage_ties = FALSE ) ) / 2, - rep( 0, N ) ) -) - -test_that( 'Multivariate Modified Band Depths - nonuniform weights', - expect_equal( multiMBD( list( Data_1, Data_2 ), - weights = c( 1/3, 2/3 ), - manage_ties = FALSE ) - - ( 1 / 3 * MBD( Data_1, manage_ties = FALSE ) + - 2 / 3 * MBD( Data_2, manage_ties = FALSE ) ), - rep( 0, N ) ) -) - -test_that( 'Multivariate Modified Band Depths - wrong weights', - expect_error( multiMBD( list( Data_1, Data_2 ), - weights = c( 1/2, 1 ) ) ) -) - -test_that( 'Multivariate Modified Band Depths - wrong weights (bis)', - expect_error( multiMBD( list( Data_1, Data_2 ), - weights = 'unif' ) ) -) - - -# Multivariate band depths - -test_that( 'Multivariate Band Depths - with management of ties', - expect_equal( multiBD( list( Data_1, Data_2 ), - weights = 'uniform' ) - - ( BD( Data_1 ) + - BD( Data_2 ) ) / 2, - rep( 0, N ) ) -) - -test_that( 'Multivariate Band Depths - without management of ties', - expect_equal( multiBD( list( Data_1, Data_2 ), - weights = 'uniform' ) - - ( BD( Data_1 ) + - BD( Data_2 ) ) / 2, - rep( 0, N ) ) -) - -test_that( 'Multivariate Band Depths - nonuniform weights', - expect_equal( multiBD( list( Data_1, Data_2 ), - weights = c( 1/3, 2/3 ) ) - - ( 1 / 3 * BD( Data_1 ) + - 2 / 3 * BD( Data_2 ) ), - rep( 0, N ) ) -) - -test_that( 'Multivariate Band Depths - wrong weights', - expect_error( multiBD( list( Data_1, Data_2 ), - weights = c( 1/2, 1 ) ) ) -) - -test_that( 'Multivariate Band Depths - wrong weights (bis)', - expect_error( multiBD( list( Data_1, Data_2 ), - weights = 'unif' ) ) -) - diff --git a/tests/testthat/test_outliergram.R b/tests/testthat/test_outliergram.R deleted file mode 100644 index 3f31842..0000000 --- a/tests/testthat/test_outliergram.R +++ /dev/null @@ -1,75 +0,0 @@ - -# TESTING OUTLIERGRAM ----------------------------------------------------- - -set.seed( 1618 ) - -N = 200 -N_outliers = 4 -P = 200 - -grid = seq( 0, 1, length.out = P ) - - -Cov = exp_cov_function( grid, alpha = 0.2, beta = 0.8 ) - -Data = generate_gauss_fdata( N, - centerline = sin( 4 * pi * grid ), - Cov = Cov ) - - -Data_out = array( 0, dim = c( N_outliers, P ) ) - -Data_out[ 1, ] = generate_gauss_fdata( 1, - sin( 4 * pi * grid + pi / 2 ), - Cov = Cov ) - -Data_out[ 2, ] = generate_gauss_fdata( 1, - sin( 4 * pi * grid - pi / 2 ), - Cov = Cov ) - -Data_out[ 3, ] = generate_gauss_fdata( 1, - sin( 4 * pi * grid + pi/ 3 ), - Cov = Cov ) - -Data_out[ 4, ] = generate_gauss_fdata( 1, - sin( 4 * pi * grid - pi / 3), - Cov = Cov ) -Data = rbind( Data, Data_out ) - -fD = fData( grid, Data ) - - -test_that( 'Outliergram - general', - expect_silent( outliergram( fD, display = FALSE ) ) ) - -test_that( 'Outliergram - no adjustment', - expect_equal( - outliergram( fD, display = FALSE )$ID_outliers - , - c( 31, 78, 117, 122, 152, 183, 201, 202, 203, 204 ) ) ) - -test_that( 'Outliergram - no adjustment 2', - expect_equal( - outliergram( fD, Fvalue = 2.5, - display = FALSE )$ID_outliers, - c( 78, 117, 183, 201, 202, 203, 204 ) ) ) - - -test_that( 'Outliergram - with adjustment',{ - testthat::skip_on_cran() - expect_equal( - outliergram( fD, - adjust = list( N_trials = 10, - trial_size = 5 * nrow( Data ), - TPR = 0.01, - VERBOSE = FALSE ), - display = FALSE )$ID_outliers, - c( 78, 117, 183, 201, 202, 203, 204 ) ) }) - -test_that( 'Outliergram - warning generation', - expect_warning( outliergram( fD, - adjust = list( N_trials = 1, trial_size = N, - foo = 'bar', baz = 'qux' ), - display = FALSE ) ) ) - - diff --git a/tests/testthat/test_restyling.R b/tests/testthat/test_restyling.R deleted file mode 100644 index e5de57e..0000000 --- a/tests/testthat/test_restyling.R +++ /dev/null @@ -1,124 +0,0 @@ - - - -# RESTYLING UNIVARIATE DEPTHS --------------------------------------------- - -###### BD - -N = 1e2 - -P = 1e2 - - -grid = seq( 0, 1, length.out = 1e2 ) - -centerline = sin( 2 * pi * grid ) -Cov = exp_cov_function( grid, alpha = 0.2, beta = 0.3 ) - -Data = generate_gauss_fdata( N, - centerline, - Cov ) -fD = fData( grid, Data ) - -test_that( 'Restyling test on BD ', - expect_identical( BD( fD ), - BD( Data ) ) ) - -###### MBD - -N = 1e2 - -P = 1e2 - - -grid = seq( 0, 1, length.out = 1e2 ) - -centerline = sin( 2 * pi * grid ) -Cov = exp_cov_function( grid, alpha = 0.2, beta = 0.3 ) - -Data = generate_gauss_fdata( N, - centerline, - Cov ) -fD = fData( grid, Data ) - -test_that( 'Restyling test on MBD - 1 ', - expect_identical( MBD( fD, manage_ties = TRUE ), - MBD( Data, manage_ties = TRUE ) ) ) - -test_that( 'Restyling test on MBD - 2 ', - expect_identical( MBD( fD, manage_ties = FALSE ), - MBD( Data, manage_ties = FALSE ) ) ) - -###### RELATIVE BD and RELATIVE MBD - -N = 1e2 - -P = 1e2 - -grid = seq( 0, 1, length.out = 1e2 ) - -centerline_1 = sin( 2 * pi * grid ) -centerline_2 = sin( 2 * pi * grid ) + 0.5 - -Cov = exp_cov_function( grid, alpha = 0.2, beta = 0.3 ) - -Data_reference = generate_gauss_fdata( N, - centerline_1, - Cov ) -Data_target = generate_gauss_fdata( N, - centerline_2, - Cov ) -fD_target = fData( grid, Data_target ) -fD_reference = fData( grid, Data_reference ) - -test_that( 'Restyling test on BD ', - expect_identical( BD_relative( fD_target, fD_reference ), - BD_relative( Data_target, Data_reference ) ) ) -test_that( 'Restyling test on MBD ', - expect_identical( MBD_relative( fD_target, fD_reference ), - MBD_relative( Data_target, Data_reference ) ) ) - - -# RESTYLING INDEXES ------------------------------------------------------- - -#### EI and MEI -N = 1e2 - -P = 1e2 - -grid = seq( 0, 1, length.out = 1e2 ) - -centerline = sin( 2 * pi * grid ) - -Cov = exp_cov_function( grid, alpha = 0.2, beta = 0.3 ) - -Data = generate_gauss_fdata( N, - centerline, - Cov ) -fD = fData( grid, Data ) - -test_that( 'Restyling test on EI', - expect_identical( EI( fD ), - EI( Data ) ) ) - -test_that( 'Restyling test on MEI', - expect_identical( MEI( fD ), - MEI( Data ) ) ) - -#### HI and MHI -test_that( 'Restyling test on EI', - expect_identical( HI( fD ), - HI( Data ) ) ) - -test_that( 'Restyling test on MHI', - expect_identical( MHI( fD ), - MHI( Data ) ) ) - -#### HRD and MHRD -test_that( 'Restyling test on EI', - expect_identical( HRD( fD ), - HRD( Data ) ) ) - -test_that( 'Restyling test on MEI', - expect_identical( MHRD( fD ), - MHRD( Data ) ) ) diff --git a/tests/testthat/test_simulation.R b/tests/testthat/test_simulation.R deleted file mode 100644 index b891982..0000000 --- a/tests/testthat/test_simulation.R +++ /dev/null @@ -1,106 +0,0 @@ - - - -# TESTING THE SIMULATION OF UNIVARIATE FUNCTIONAL DATA -------------------- - -N = 30 -P = 1e2 - -t0 = 0 -tP = 1 - -time_grid = seq( t0, tP, length.out = P ) - -C = exp_cov_function( time_grid, alpha = 0.1, beta = 0.2 ) - -CholC = chol( C ) - -centerline = sin( 2 * pi * time_grid ) - -test_that( 'Generation of gaussian univariate functional data - 1', - expect_silent( generate_gauss_fdata( N, - centerline, - Cov = C ) - ) ) - - -test_that( 'Generation of gaussian univariate functional data - 1', - expect_silent( generate_gauss_fdata( N, - centerline, - CholCov = CholC ) - ) ) - - - -# TESTING THE SIMULATION OF MULTIVARIATE FUNCTIONAL DATA ------------------ - -N = 30 -P = 1e2 -L = 3 - -t0 = 0 -tP = 1 - -time_grid = seq( t0, tP, length.out = P ) - -C1 = exp_cov_function( time_grid, alpha = 0.1, beta = 0.2 ) -C2 = exp_cov_function( time_grid, alpha = 0.2, beta = 0.5 ) -C3 = exp_cov_function( time_grid, alpha = 0.3, beta = 1 ) - -CholC1 = chol( C1 ) -CholC2 = chol( C2 ) -CholC3 = chol( C3 ) - -centerline = matrix( c( sin( 2 * pi * time_grid ), - sqrt( time_grid ), - 10 * ( time_grid - 0.5 ) * time_grid ), - nrow = 3, byrow = TRUE ) - -test_that( 'Generation of gaussian multivarite functional data - 1', - expect_silent( generate_gauss_mfdata( N, L, - centerline, - correlations = c( 0.5, 0.5, - 0.5 ), - listCov = list( C1, C2, C3 ) ) - ) ) - -test_that( 'Generation of gaussian multivarite functional data - 2', - expect_silent( generate_gauss_mfdata( N, L, - centerline, - correlations = c( 0.5, 0.5, - 0.5 ), - listCholCov = list( CholC1, - CholC2, - CholC3 ) ) - ) ) - -test_that( 'Generation of gaussian multivarite functional data - 3', - expect_error( generate_gauss_mfdata( N, L, - centerline, - correlations = c( 0.5, 0.5, - 0.5 ), - listCholCov = list( CholC1[-1,], - CholC2[-1,], - CholC3[-1,] - ) ) ) ) - -test_that( 'Generation of gaussian multivarite functional data - 4', - expect_error( generate_gauss_mfdata( N, L, - centerline[-1,], - correlations = c( 0.5, 0.5, - 0.5 ), - listCov = c( C1, C2, C3 ), - listCholCov = list( CholC1[-1,], - CholC2[-1,], - CholC3[-1,] - ) ) ) ) - -test_that( 'Generation of gaussian multivarite functional data - 5', - expect_error( generate_gauss_mfdata( N, L, - centerline[,-1], - correlations = c( 0.5, 0.5 ), - listCov = c( C1, C2, C3 ), - listCholCov = list( CholC1, - CholC2, - CholC3 ) ) - ) ) diff --git a/vignettes/references.bib b/vignettes/references.bib index 8b3dfde..3ebfe3a 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -1,12 +1,23 @@ %% This BibTeX bibliography file was created using BibDesk. %% http://bibdesk.sourceforge.net/ -%% Created for Nicholas Tarabelloni at 2016-05-09 12:21:20 +0200 - - -%% Saved with string encoding Unicode (UTF-8) - - +%% Created for Nicholas Tarabelloni at 2016-05-09 12:21:20 +0200 + + +%% Saved with string encoding Unicode (UTF-8) + +@article{Ieva2019, + author = {Francesca Ieva and Anna Maria Paganoni and Juan Romo and + Nicholas Tarabelloni}, + title = {{roahd Package: Robust Analysis of High Dimensional Data}}, + year = {2019}, + journal = {{The R Journal}}, + doi = {10.32614/RJ-2019-032}, + url = {https://doi.org/10.32614/RJ-2019-032}, + pages = {291--307}, + volume = {11}, + number = {2} +} @techreport{spearman_2015, Address = {http://EconPapers.repec.org/RePEc:cte:wsrepe:ws133329}, diff --git a/vignettes/Roahd.Rmd b/vignettes/roahd.Rmd similarity index 91% rename from vignettes/Roahd.Rmd rename to vignettes/roahd.Rmd index 33e3d5e..75f7017 100644 --- a/vignettes/Roahd.Rmd +++ b/vignettes/roahd.Rmd @@ -18,7 +18,7 @@ vignette: > Package **roahd** (**RO**bust **A**nalysis of **H**igh dimensional **D**ata) is an `R` package meant to gather recently proposed statistical methods to deal -with the robust analysis of functional data. +with the robust analysis of functional data [@Ieva2019]. The package contains an implementation of quantitative methods, based on functional depths and indices, on top of which are built some graphical methods @@ -125,7 +125,7 @@ plot( mfD, lwd = 2, main = 'Multivariate FD', xlab = 'time', ylab = list( 'Values 1', 'Values 2' )) ``` -The fact that `mfData` components are `fData` obejcts is indeed conceptually +The fact that `mfData` components are `fData` objects is indeed conceptually very natural, but also allows for a seamless application of `S3` methods meant for `fData` on multivariate functional data components, making the exploration and manipulation of multivariate datasets rather easy: @@ -165,15 +165,17 @@ fD[ 2, 10 : 20, as_fData = FALSE ] fD[ , 10, as_fData = FALSE ] -# As default behaviour the subset is returned in fData form -par( mfrow = c(1,2) ) -plot( fD, main = 'Original dataset', lwd = 2 ) -plot( fD[ , 1 : 20 ], main = 'Zooming in', lwd = 2 ) +# As default behavior the subset is returned in fData form +oldpar <- par(mfrow = c(1, 1)) +par(mfrow = c(1, 2)) +plot(fD, main = "Original dataset", lwd = 2) +plot(fD[, 1:20], main = "Zooming in", lwd = 2) +par(oldpar) ``` ## Algebra -An algebra of `fData` obejcts is also implemented, making it easy to sum, +An algebra of `fData` objects is also implemented, making it easy to sum, subtract, multiply and divide these objects by meaningful and compliant structures. @@ -213,10 +215,10 @@ fD / ( 1 : N ) ``` ## Visualization -`fData` and `mfData` objects can be visualised thanks to specific `S3` plotting +`fData` and `mfData` objects can be visualized thanks to specific `S3` plotting methods, `plot.fData` and `plot.mfData`. -The graphical parameters of these functions have been suitably customised in order +The graphical parameters of these functions have been suitably customized in order to enhance the visualization of functions. In particular, elements are plotted by default with continuous lines and an ad-hoc palette that helps differentiating them. As default x- and y-axis labels, as well as titles, are dropped so that @@ -248,7 +250,7 @@ data values (e.g. in the form of `fData$values` output). prior to computing depths, and therefore a suitable computing strategy must be used. The implementation of `MBD` exploits the recommendations of [@Sun_Genton_2012], -but extends them in order to accomodate for the possible presence of ties. +but extends them in order to accommodate for the possible presence of ties. ```{r BD_MBD_1, eval = FALSE } @@ -276,7 +278,7 @@ MHRD( fD$values ) ``` -A generalisation of MBD to multivariate functional data, implementing the ideas +A generalization of MBD to multivariate functional data, implementing the ideas of @ieva_paganoni_2013, is also available through the functions `multiBD` and `multiMBD`. These functions accept either a `mfData` object, specifying the multivariate functional dataset whose depths have to be computed, or a list of @@ -352,12 +354,16 @@ the dots arguments). median_fData( fD, type = 'MBD' ) median_fData( fD, type = 'MHRD' ) - par( mfrow = c(1,2) ) - plot( fD, main = 'Mean', lwd = 2 ) - plot( mean( fD ), add = TRUE, lwd = 2, col = 'black', lty = 2 ) + oldpar <- par(mfrow = c(1, 1)) + par(mfrow = c(1, 2)) - plot( fD, main = 'Median', lwd = 2 ) - plot( median_fData( fD, type = 'MBD' ), add = TRUE, lwd = 2, lty = 2, col = 'black' ) + plot(fD, main = "Mean", lwd = 2) + plot(mean(fD), add = TRUE, lwd = 2, col = "black", lty = 2) + + plot(fD, main = "Median", lwd = 2) + plot(median_fData(fD, type = "MBD"), add = TRUE, lwd = 2, lty = 2, col = "black") + + par(oldpar) ``` ## Covariance and cross-covariance functions @@ -381,7 +387,7 @@ covariances of the components. When `X` is a univariate functional dataset, and if `Y` is `NULL`, `cov_fun` returns the sample covariance function of the functional -dataset, defined over the tensorised grid where `X` is defined. +dataset, defined over the tensorized grid where `X` is defined. If `Y` is a univariate functional dataset (in form of `fData` object), the method returns the cross-covariance function of `X` and `Y`. @@ -406,12 +412,12 @@ between `X`'s and `Y`'s components. ``` Each covariance function estimate (the elements of the list in the multivariate -case, too) is returned as an istance of the `S3` class `Cov`, that stores the +case, too) is returned as an instance of the `S3` class `Cov`, that stores the values of the covariance matrix as well as the grid parameters. -`plot.Cov`, an `S3` specialisation of `plot` is available as plotting method for +`plot.Cov`, an `S3` specialization of `plot` is available as plotting method for `Cov` objects. It is built around `graphics::image`, hence all the additional -parameters of `image` can be used to customise it. +parameters of `image` can be used to customize it. ```{r plot_cov, eval = FALSE, collapse = TRUE, fig.align = 'center', fig.height = 4, fig.width = 4} plot( C1, main = 'Covariance function', xlab = 'time', ylab = 'time' ) @@ -419,13 +425,13 @@ parameters of `image` can be used to customise it. ## Indexes `roahd` collects the implementation of some useful indexes that can be used to -describe and summarise functional datasets. +describe and summarize functional datasets. `EI` and `MEI` implement the **Epigraph Index** and the **Modified Epigraph Index**, while `HI` and `MHI` implement the **Hypograph Index** and the **Modified Hypograph Index** [see @lopezpintado_romo_2011; @arribasgil_romo_2014]. These indexes can be used to sort data in a top-down and bottom-up -fashion, and are used to define the HRD/MHRD and to build the outliegram. +fashion, and are used to define the HRD/MHRD and to build the outliergram. These `S3` methods can be called on univariate functional datasets, provided either in form of a `fData` object or a 2D matrix of values. @@ -449,13 +455,13 @@ either in form of a `fData` object or a 2D matrix of values. When dealing with multivariate functional data, in particular in case of **bivariate** data, it is possible to compute correlation coefficients between data components -that generalise the Kendall's tau and Spearman's coefficients [@kendall_2015; @spearman_2015]. +that generalize the Kendall's tau and Spearman's coefficients [@kendall_2015; @spearman_2015]. The function `cor_kendall( mfD, ordering = 'max' )` allows to compute the Kendall's tau correlation coefficient between components of a bivariate dataset. The function accepts a `mfData` object and a criterion to perform the ordering of functional data (this ordering is used to determine the concordances and -discordacnes between pairs in the definition of the coefficient). +discordances between pairs in the definition of the coefficient). Two criteria are available so far, that directly reflect those proposed in the reference paper: `max`, for the ordering between maxima of functions, @@ -482,7 +488,7 @@ and `area`, for the ordering between area-under-the-curve of functions. ``` The function `cor_spearman( mfD, ordering = 'MEI', ... )` can be used to compute -the Spearman correlation coefficient for a bivariate `mfData` object, uning the +the Spearman correlation coefficient for a bivariate `mfData` object, tuning the ordering policy specified by `ordering` (defaulting to `MEI`) to rank univariate components and then compute the correlation coefficient. Besides `MEI`, also `MHI` can be used to rank univariate components. @@ -496,10 +502,10 @@ can be used to rank univariate components. # Simulation of functional data `roahd` contains also some functions that can be used to simulate artificial data sets of functional data, both univariate and multivariate. These are used in the -adjustment procedure of the outliegram and functional boxplot, but can also be +adjustment procedure of the outliergram and functional boxplot, but can also be used to help the development of new methodologies and help their testing. -Artificial univariate data are obtained simulating realisations of a gaussian +Artificial univariate data are obtained simulating realizations of a gaussian process over a discrete grid with a specific covariance function and center (e.g. mean or median). Given a covariance function, $C(s,t)$ and a centerline $m(t)$, the model @@ -522,9 +528,9 @@ data makes use of the Cholesky factor of `Cov`, hence by providing its Cholesky factor, if already present in the caller's scope, can save computing time. A built-in function can be used to generate exponential-like covariance functions, -namely `exp_cov_function( grid, alpha, beta )`, generating the discretised +namely `exp_cov_function( grid, alpha, beta )`, generating the discretized version of a covariance of the form $C(s,t) = \alpha e^{-\beta | s - t | }$ -over a lattice given by the tensorisation of grid in `grid`. +over a lattice given by the tensorization of grid in `grid`. A comprehensive example is the following: @@ -558,8 +564,8 @@ simulate; `L`, the number of components of the multivariate functional data; `centerline`, a matrix containing (by rows) the centerline for each component; `correlations`, a vector of length `1/2 * L * ( L - 1 )`, containing all the correlation coefficients among the components; either `listCov` or -`listCholCov`, a list containing either the discretised covariance functions -over the tensorised grid where functional data will be defined), or their +`listCholCov`, a list containing either the discretized covariance functions +over the tensorized grid where functional data will be defined), or their Cholesky factor. A comprehensive example is the following: @@ -591,7 +597,7 @@ allows for the detection of *amplitude* outliers in univariate and multivariate functional datasets [see @Sun_Genton_2011]. `fbplot` can be used to compute the set of indices of observations marking -outlying signals. If used in graphical way (default behaviour), it also +outlying signals. If used in graphical way (default behavior), it also plots the functional boxplot of the dataset under study The functional boxplot is obtained by ranking functions from the center of the @@ -601,7 +607,7 @@ function crossing these boundaries is flagged as outlier. The default value for `F` is `1.5`, otherwise it can be set with the argument `Fvalue`. The argument `Depths` can take either the name of the function to call in order -to compute the depths (default is `MBD`), or a vector containing the dephts +to compute the depths (default is `MBD`), or a vector containing the depth values for the provided dataset. An example is: @@ -650,8 +656,8 @@ The parameters to control the adjustment procedure can be passed through the argument `adjust`, whose default is `FALSE` and otherwise is a list with (some of) the fields: -* `N_trials`: the number of repetitions of the adujustment procedure based on -the simulation of a gaussisan population of functional data, each one producing +* `N_trials`: the number of repetitions of the adjustment procedure based on +the simulation of a gaussian population of functional data, each one producing an adjusted value of F, which will lead to the averaged adjusted value `Fvalue`. Default is 20; * `trial_size`: the number of elements in the gaussian population of functional @@ -661,21 +667,21 @@ Default is 8 * `Data$N`; in a dataset without amplitude outliers that have to be considered outliers. Default is `2 * pnorm( 4 * qnorm( 0.25 ) )`; * `F_min`: the minimum value of `Fvalue`, defining the left boundary for the -optimisation problem aimed at finding, for a given dataset of simulated gaussian +optimization problem aimed at finding, for a given dataset of simulated gaussian data associated to Data, the optimal value of `Fvalue`. Default is `0.5`; * `F_max`: the maximum value of `Fvalue`, defining the right boundary for the -optimisation problem aimed at finding, for a given dataset of simulated gaussian +optimization problem aimed at finding, for a given dataset of simulated gaussian data associated to Data, the optimal value of `Fvalue`. Default is `5`; -* `tol`: the tolerance to be used in the optimisation problem aimed at finding, +* `tol`: the tolerance to be used in the optimization problem aimed at finding, for a given dataset of simulated gaussian data associated to Data, the optimal value of `Fvalue`. Default is `1e-3`; -* `maxiter`: the maximum number of iterations to solve the optimisation problem +* `maxiter`: the maximum number of iterations to solve the optimization problem aimed at finding, for a given dataset of simulated gaussian data associated to `Data`, the optimal value of `Fvalue`. Default is `100`; * `VERBOSE`: a parameter controlling the verbosity of the adjustment process; *Suggestion*: Try and select a sufficiently high value for `adjust$trial_size`, -in fact too small values (the default is `8 * adjust$N`) will result in the impossibility to carry out the optimisation since the TPR percentage is too small +in fact too small values (the default is `8 * adjust$N`) will result in the impossibility to carry out the optimization since the TPR percentage is too small compared to the sample size. ```{r fbplot_fData_adjust, eval = TRUE, collapse = TRUE, fig.align = 'center', fig.width = 7, fig.height = 4} @@ -726,33 +732,33 @@ Similarly to the functional boxplot, also the outliergram makes use of an `Fvalu constant controlling the check by which observations are flagged as outliers [see @arribasgil_romo_2014 for more details]. Such value can be provided as an argument (default is `1.5`), or can be determined by the function itself, through -an adjustment procedure similar to that of `fbplot.fData`. In particolar, whenever +an adjustment procedure similar to that of `fbplot.fData`. In particular, whenever `adjust` is not `FALSE`, `adjust` should be a list containing the fields controlling the adjustment: -* `N_trials` the number of repetitions of the adujustment -procedure based on the simulation of a gaussisan population of functional +* `N_trials` the number of repetitions of the adjustment +procedure based on the simulation of a gaussian population of functional data, each one producing an adjusted value of `Fvalue`, which will lead to the averaged adjusted value. Default is `20`; * `trial_size` the number of elements in the gaussian population of functional data that will be simulated at each repetition of the adjustment procedure. Default is `5 * fData$N`; -* `TPR` the True Positive Rate of outleirs, i.e. the proportion +* `TPR` the True Positive Rate of outliers, i.e. the proportion of observations in a dataset without shape outliers that have to be considered outliers. Default is `2 * pnorm( 4 * qnorm( 0.25 ) )`; * `F_min` the minimum value of `Fvalue`, defining the left -boundary for the optimisation problem aimed at finding, for a given dataset +boundary for the optimization problem aimed at finding, for a given dataset of simulated gaussian data associated to `fData`, the optimal value of `Fvalue`. Default is `0.5`; * `F_max` the maximum value of `Fvalue`, defining the right -boundary for the optimisation problem aimed at finding, for a given dataset +boundary for the optimization problem aimed at finding, for a given dataset of simulated gaussian data associated to `fData`, the optimal value of `Fvalue`. Default is `20`;} -* `tol` the tolerance to be used in the optimisation problem +* `tol` the tolerance to be used in the optimization problem aimed at finding, for a given dataset of simulated gaussian data associated to `fData`, the optimal value of `Fvalue`. Default is `1e-3`; * `maxiter` the maximum number of iterations to solve the -optimisation problem aimed at finding, for a given dataset of simulated +optimization problem aimed at finding, for a given dataset of simulated gaussian data associated to `fData`, the optimal value of `Fvalue`. Default is `100`; * `VERBOSE` a parameter controlling the verbosity of the