small touchups

Author: philchalmers

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: SimDesign
 Title: Structure for Organizing Monte Carlo Simulation Designs
-Version: 2.21.3
+Version: 2.21.4
 Authors@R: c(person("Phil", "Chalmers", email = "[email protected]", role = c("aut", "cre"),
                     comment = c(ORCID="0000-0001-5332-2810")),
              person("Matthew", "Sigal", role = c("ctb")),

R/analysis.R

@@ -31,7 +31,7 @@ Analysis <- function(Functions, condition, replications, fixed_objects, prepare
             write(prepare_Random.seed, file.path(save_results_out_rootdir, filename), sep = ' ')
         }
 
-        prep_result <- try(prepare(condition=condition, fixed_objects=fixed_objects), silent=FALSE)
+        prep_result <- try(prepare(condition=condition, fixed_objects=fixed_objects), silent=TRUE)
 
         if(is(prep_result, 'try-error')){
             # Capture seed on error (similar to mainsim)

R/runSimulation.R

@@ -218,7 +218,7 @@
 #'
 #'   \code{  fixed_objects$expensive_stuff <- readRDS(fname)}
 #'
-#'   \code{  return(fixed_objects)}
+#'   \code{  fixed_objects}
 #'
 #'   \code{\}}
 #'
@@ -834,10 +834,7 @@
 #' head(res[[1]]$results)
 #'
 #' # just first condition
-#' res <- SimResults(Final, which=1)
-#' head(res$results)
-#' dplyr::tibble(res$condition, res$results)
-#'
+#' SimResults(Final, which=1)
 #'
 #' # obtain empirical bootstrapped CIs during an initial run
 #' # the simulation was completed (necessarily requires save_results = TRUE)
@@ -968,19 +965,12 @@
 #'               generate=Generate, analyse=Analyse, summarise=Summarise,
 #'               parallel=TRUE, debug='analyse-2')
 #'
+#' }
+#'
 #'
 #' ####################################
-#' ## EXTRA: To run the simulation on a user-define cluster, use the following setup (not run)
-# library(doMPI)
-# cl <- startMPIcluster()
-# registerDoMPI(cl)
-# Final <- runSimulation(design=Design, replications=1000, MPI=TRUE,
-#                        generate=Generate, analyse=Analyse, summarise=Summarise)
-# saveRDS(Final, 'mysim.rds')
-# closeCluster(cl)
-# mpi.quit()
-#
-#
+#'
+#' \dontrun{
 #' ## Network linked via ssh (two way ssh key-paired connection must be
 #' ## possible between master and slave nodes)
 #' ##
@@ -1027,12 +1017,15 @@
 #' # reset future computing plan when complete (good practice)
 #' plan(sequential)
 #'
+#' }
+#'
 #' ####################################
 #'
 #' ###### Post-analysis: Analyze the results via functions like lm() or SimAnova(), and create
 #' ###### tables(dplyr) or plots (ggplot2) to help visualize the results.
 #' ###### This is where you get to be a data analyst!
 #'
+#'
 #' library(dplyr)
 #' res %>% summarise(mean(welch), mean(independent))
 #' res %>% group_by(standard_deviation_ratio, group_size_ratio) %>%
@@ -1135,9 +1128,7 @@
 #' results
 #'
 #' # Cleanup
-#' unlink('prepared_objects', recursive = TRUE)
-#'
-#' }
+#' SimClean(dirs='prepared_objects')
 #'
 #' }
 #'

man/runSimulation.Rd

@@ -83,17 +83,20 @@ test_that('prepare RNG management', {
         return(fixed_objects)
     }
 
-    res_fail <- tryCatch(
+    runSimulation(Design[2,], replications=1,
+                  prepare=prepare_error,
+                  generate=generate,
+                  analyse=analyse,
+                  summarise=summarise,
+                  verbose=FALSE)
+
+    expect_error(
         runSimulation(Design[2,], replications=1,
                       prepare=prepare_error,
                       generate=generate,
                       analyse=analyse,
                       summarise=summarise,
-                      verbose=FALSE),
-        error = function(e) e
-    )
-
-    expect_true(inherits(res_fail, 'error'))
+                      verbose=FALSE))
 
     # Test 5: load_seed_prepare with file path
     res5 <- runSimulation(Design, replications=2,