correcting all warning and notes

Changes: i) include a globals R script to host foreach loop index for the bayesweight and bayesweight_cen

Only one note left
checking for non-standard things in the check directory ... NOTE
  Found the following files/directories:
    'censoring_model.txt' 'treatment_model.txt'

0 errors ✔ | 0 warnings ✔ | 1 note ✖

.Rbuildignore

@@ -7,3 +7,4 @@
 ^Meta$
 ^docs$
 ^README\.Rmd$
+^doc$

DESCRIPTION

@@ -18,7 +18,10 @@ Depends:
     R (>= 4.2.0)
 Suggests: 
     rmarkdown,
-    testthat (>= 3.0.0)
+    testthat (>= 3.0.0),
+    knitr, 
+	  DiagrammeR, 
+	  DT 
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
@@ -29,12 +32,10 @@ Imports:
 	  foreach,
 	  parallel,
 	  R2jags,
-	  knitr,
 	  coda (>= 0.19-4),
 	  stats,
 	  grDevices,
-	  graphics,
-	  knitr
+	  graphics
 NeedsCompilation: no
 Config/testthat/edition: 3
 URL: https://kuan-liu-lab.github.io/bayesmsm/

NAMESPACE

@@ -7,9 +7,7 @@ export(plot_APO)
 export(plot_ATE)
 export(plot_est_box)
 export(summary_bayesmsm)
-import(MCMCpack)
 import(doParallel)
-import(foreach)
 import(parallel)
 importFrom(MCMCpack,rdirichlet)
 importFrom(R2jags,jags)

R/bayesmsm.R

@@ -30,7 +30,6 @@
 #' @importFrom foreach "%dopar%"
 #' @import doParallel
 #' @import parallel
-#' @import MCMCpack
 #' @importFrom MCMCpack rdirichlet
 #' @importFrom stats as.formula density optim quantile sd terms var
 #'

R/bayesweight.R

@@ -18,7 +18,6 @@
 #' @importFrom coda mcmc as.mcmc geweke.diag
 #' @import parallel
 #' @import doParallel
-#' @import foreach
 #' @importFrom foreach "%dopar%"
 #' @importFrom stats as.formula terms var
 #'
@@ -52,51 +51,52 @@ bayesweight <- function(trtmodel.list,
                         seed = 890123,
                         parallel = TRUE){
 
-  # Load all the required R packages;
-  # require(foreach)
-  # require(doParallel)
-  # require(MCMCpack)
-  # require(parallel)
-  # require(R2jags)
-  # require(coda)
-  # if (!require(R2jags)){
-  #   install.packages("R2jags",repos="http://cran.r-project.org")
-  #   library(R2jags)
-  # }
-  # if (!require(coda)){
-  #   install.packages("coda",repos="http://cran.r-project.org")
-  #   library(coda)
-  # }
-  # if (!require(parallel)){
-  #   install.packages("parallel",repos="http://cran.r-project.org")
-  #   library(parallel)
-  # }
-
 
   create_marginal_treatment_models <- function(trtmodel.list) {
-    # Initialize the list for the marginal treatment models
-    trtmodel.list_s <- list()
 
-    # Loop through each model in the original list
-    for (i in seq_along(trtmodel.list)) {
+    # Use lapply to iterate over indices
+    trtmodel.list_s <- lapply(seq_along(trtmodel.list), function(index) {
       # Extract the response variable (treatment variable) from each model
-      response_var <- all.vars(trtmodel.list[[i]])[1]  # assuming the response is the first variable on the LHS
+      response_var <- all.vars(trtmodel.list[[index]])[1]  # assuming the response is the first variable on the LHS
 
       # Create the marginal model formula
-      if (i == 1) {
+      if (index == 1) {
         # The first treatment model does not depend on any previous treatments
         formula_s <- as.formula(paste(response_var, "~ 1"))
       } else {
         # Subsequent treatment models depend on all previous treatments
-        previous_treatments <- sapply(seq_len(i-1), function(j) {
+        previous_treatments <- sapply(seq_len(index - 1), function(j) {
           all.vars(trtmodel.list[[j]])[1]
         })
         formula_s <- as.formula(paste(response_var, "~", paste(previous_treatments, collapse = " + ")))
       }
 
-      # Append the new formula to the list
-      trtmodel.list_s[[i]] <- formula_s
-    }
+      return(formula_s)
+    })
+
+    # # Initialize the list for the marginal treatment models
+    # trtmodel.list_s <- list()
+    #
+    # # Loop through each model in the original list
+    # for (i in seq_along(trtmodel.list)) {
+    #   # Extract the response variable (treatment variable) from each model
+    #   response_var <- all.vars(trtmodel.list[[i]])[1]  # assuming the response is the first variable on the LHS
+    #
+    #   # Create the marginal model formula
+    #   if (i == 1) {
+    #     # The first treatment model does not depend on any previous treatments
+    #     formula_s <- as.formula(paste(response_var, "~ 1"))
+    #   } else {
+    #     # Subsequent treatment models depend on all previous treatments
+    #     previous_treatments <- sapply(seq_len(length(trtmodel.list) - 1), function(j) {
+    #       all.vars(trtmodel.list[[j]])[1]
+    #     })
+    #     formula_s <- as.formula(paste(response_var, "~", paste(previous_treatments, collapse = " + ")))
+    #   }
+    #
+    #   # Append the new formula to the list
+    #   trtmodel.list_s[[i]] <- formula_s
+    # }
 
     return(trtmodel.list_s)
   }
@@ -237,9 +237,6 @@ bayesweight <- function(trtmodel.list,
       # This is where you'd actually generate the JAGS model code
     }
 
-    # Example writing the model to a file
-    cat(model_string, file = "treatment_model.txt")
-
     return(unique(all_parameters))
   }
 
@@ -288,38 +285,36 @@ bayesweight <- function(trtmodel.list,
     # Run JAGS model in parallel
     cl <- parallel::makeCluster(n.chains)
     doParallel::registerDoParallel(cl)
-    # Run JAGS model in parallel;
-    # cl <- makeCluster(n.chains)
-    # registerDoParallel(cl)
 
     # Ensure the cluster is stopped when the function exits, even in case of error
-    on.exit({
-      if (!is.null(cl)) {
-        parallel::stopCluster(cl)
-        foreach::registerDoSEQ()  # Reset to sequential processing correctly
-      }
-    }, add = TRUE)
+    # on.exit({
+    #   if (!is.null(cl)) {
+    #     parallel::stopCluster(cl)
+    #     foreach::registerDoSEQ()  # Reset to sequential processing correctly
+    #   }
+    # }, add = TRUE)
 
     jags.model.wd <- paste(getwd(), '/treatment_model.txt',sep='')
 
-    posterior <- foreach::foreach(i=1:n.chains, .packages=c('R2jags'),
+    posterior <- foreach::foreach(chain_idx=1:n.chains, .packages=c('R2jags'),
                          .combine='rbind') %dopar%{
 
+                           set.seed(seed+chain_idx) #define seed;
                            jagsfit <- R2jags::jags(data = jags.data,
                                            parameters.to.save = jags.params,
                                            model.file = jags.model.wd,
                                            n.chains = 1,
                                            n.iter = n.iter,
                                            n.burnin = n.burnin,
-                                           n.thin = n.thin,
-                                           jags.seed = seed+i)
+                                           n.thin = n.thin)
                            # Combine MCMC output from multiple chains
                            out.mcmc <- as.mcmc(jagsfit)
                            return(do.call(rbind, lapply(out.mcmc, as.matrix)))
 
                          }
-    # parallel::stopCluster(cl)
-    # doParallel::registerDoSEQ()
+
+    parallel::stopCluster(cl)
+
 
   } else if (parallel == FALSE) {
 

R/bayesweight_cen.R

@@ -17,7 +17,6 @@
 #' @importFrom coda mcmc as.mcmc geweke.diag
 #' @import parallel
 #' @import doParallel
-#' @import foreach
 #' @importFrom foreach "%dopar%"
 #' @importFrom stats as.formula terms var
 #'
@@ -64,30 +63,51 @@ bayesweight_cen <- function(trtmodel.list = list(A1 ~ L11 + L21,
 
 
   create_marginal_treatment_models <- function(trtmodel.list) {
-    # Initialize the list for the marginal treatment models
-    trtmodel.list_s <- list()
 
-    # Loop through each model in the original list
-    for (i in seq_along(trtmodel.list)) {
+    # Use lapply to iterate over indices
+    trtmodel.list_s <- lapply(seq_along(trtmodel.list), function(index) {
       # Extract the response variable (treatment variable) from each model
-      response_var <- all.vars(trtmodel.list[[i]])[1]  # assuming the response is the first variable on the LHS
+      response_var <- all.vars(trtmodel.list[[index]])[1]  # assuming the response is the first variable on the LHS
 
       # Create the marginal model formula
-      if (i == 1) {
+      if (index == 1) {
         # The first treatment model does not depend on any previous treatments
         formula_s <- as.formula(paste(response_var, "~ 1"))
       } else {
         # Subsequent treatment models depend on all previous treatments
-        previous_treatments <- sapply(seq_len(i-1), function(j) {
+        previous_treatments <- sapply(seq_len(index - 1), function(j) {
           all.vars(trtmodel.list[[j]])[1]
         })
         formula_s <- as.formula(paste(response_var, "~", paste(previous_treatments, collapse = " + ")))
       }
 
-      # Append the new formula to the list
-      trtmodel.list_s[[i]] <- formula_s
-    }
-
+      return(formula_s)
+    })
+
+    # # Initialize the list for the marginal treatment models
+    # trtmodel.list_s <- list()
+    #
+    # # Loop through each model in the original list
+    # for (i in seq_along(trtmodel.list)) {
+    #   # Extract the response variable (treatment variable) from each model
+    #   response_var <- all.vars(trtmodel.list[[i]])[1]  # assuming the response is the first variable on the LHS
+    #
+    #   # Create the marginal model formula
+    #   if (i == 1) {
+    #     # The first treatment model does not depend on any previous treatments
+    #     formula_s <- as.formula(paste(response_var, "~ 1"))
+    #   } else {
+    #     # Subsequent treatment models depend on all previous treatments
+    #     previous_treatments <- sapply(seq_len(length(trtmodel.list) - 1), function(j) {
+    #       all.vars(trtmodel.list[[j]])[1]
+    #     })
+    #     formula_s <- as.formula(paste(response_var, "~", paste(previous_treatments, collapse = " + ")))
+    #   }
+    #
+    #   # Append the new formula to the list
+    #   trtmodel.list_s[[i]] <- formula_s
+    # }
+    #
     return(trtmodel.list_s)
   }
 
@@ -283,28 +303,29 @@ bayesweight_cen <- function(trtmodel.list = list(A1 ~ L11 + L21,
       stop(paste("Parallel MCMC requires 1 core per chain. You have", available_cores, "cores. We recommend using", available_cores - 2, "cores."))
     }
     # Run JAGS model in parallel
-    library(doParallel)
-    cl <- makeCluster(n.chains)
-    registerDoParallel(cl)
+    cl <- parallel::makeCluster(n.chains)
+    doParallel::registerDoParallel(cl)
+
     jags.model.wd <- paste(getwd(), '/censoring_model.txt',sep='')
 
-    posterior <- foreach(i=1:n.chains, .packages=c('R2jags'),
+    posterior <- foreach::foreach(chain_idx=1:n.chains, .packages=c('R2jags'),
                          .combine='rbind') %dopar%{
 
+                           set.seed(seed+chain_idx) #define seed;
                            jagsfit <- jags(data = jags.data,
                                            parameters.to.save = jags.params,
                                            model.file = jags.model.wd,
                                            n.chains = 1,
                                            n.iter = n.iter,
                                            n.burnin = n.burnin,
-                                           n.thin = n.thin,
-                                           jags.seed = seed+i)
+                                           n.thin = n.thin)
                            # Combine MCMC output from multiple chains
                            out.mcmc <- as.mcmc(jagsfit)
                            return(do.call(rbind, lapply(out.mcmc, as.matrix)))
                          }
 
-    stopCluster(cl)
+    parallel::stopCluster(cl)
+
   } else if (parallel == FALSE) {
     if (n.chains != 1) {
       stop("Non-parallel MCMC requires exactly 1 chain.")

R/globals.R

@@ -0,0 +1,3 @@
+if (getRversion() >= "2.15.1") {
+  utils::globalVariables(c("chain_idx"))
+}