Merge branch 'master' of https://github.com/boost-R/FDboost

Author: davidruegamer

R/bootstrapCIs.R

@@ -103,23 +103,26 @@
 #' 
 #' ## now speed things up by defining the inner resampling
 #' ## function with parallelization based on mclapply (does not work on Windows)
+#' isWindows <- Sys.info()['sysname']=="Windows"
 #' 
 #' my_inner_fun <- function(object){ 
 #' cvrisk(object, folds = cvLong(id = object$id, weights = 
 #' model.weights(object), 
 #' B = 10 # 10-fold for inner resampling
-#' ), mc.cores = 10) # use ten cores
+#' ), mc.cores = if(isWindows) 1 else 10) # use ten cores
 #' }
 #' 
 #' \donttest{
-#' bootCIs <- bootstrapCI(m1, resampling_fun_inner = my_inner_fun)
+#' bootCIs <- bootstrapCI(m1, resampling_fun_inner = my_inner_fun, 
+#'                        B_outer = 5) # small B_outer to speed up
 #' }
 #' 
 #' ## We can also use the ... argument to parallelize the applyFolds
 #' ## function in the outer resampling 
 #' 
 #' \donttest{
-#' bootCIs <- bootstrapCI(m1, mc.cores = 30)
+#' bootCIs <- bootstrapCI(m1, mc.cores = if(isWindows) 1 else 30, 
+#'                        B_inner = 5, B_outer = 3)
 #' }
 #' 
 #' ## Now let's parallelize the outer resampling and use 
@@ -128,17 +131,24 @@
 #' my_inner_fun <- function(object){ 
 #' cvrisk(object, folds = cvLong(id = object$id, weights = 
 #' model.weights(object), type = "kfold", # use CV
-#' B = 10, # 10-fold for inner resampling
+#' B = 5, # 5-fold for inner resampling
 #' ),
-#' mc.cores = 10) # use ten cores
+#' mc.cores = if(isWindows) 1 else 5) # use five cores
 #' }
 #' 
 #' # use applyFolds for outer function to avoid messing up weights
 #' my_outer_fun <- function(object, fun){
 #' applyFolds(object = object,
 #' folds = cv(rep(1, length(unique(object$id))), 
-#' type = "bootstrap", B = 100), fun = fun,
-#' mc.cores = 10) # parallelize on 10 cores
+#' type = "bootstrap", B = 10), fun = fun,
+#' mc.cores = if(isWindows) 1 else 10) # parallelize on 10 cores
+#' }
+#' 
+#' \donttest{
+#' bootCIs <- bootstrapCI(m1, resampling_fun_inner = my_inner_fun,
+#'                        resampling_fun_outer = my_outer_fun,
+#'                        B_inner = 5, B_outer = 10) 
+# -> provide B_inner & B_outer only for enabling default plot
 #' }
 #' 
 #' ######## Example for scalar-on-function-regression with bsignal() 
@@ -165,15 +175,10 @@
 #' 
 #' \donttest{
 #' # takes some time, because of defaults: B_outer = 100, B_inner = 25
-#' bootCIs <- bootstrapCI(mod2)
+#' bootCIs <- bootstrapCI(mod2, B_outer = 10, B_inner = 5)
+#'            # in practice, rather set B_outer = 1000
 #' }
 #' 
-#' ## run with a larger number of outer bootstrap samples
-#' ## and only 10-fold for validation of each outer fold
-#' ## WARNING: This may take very long!
-#' \donttest{
-#' bootCIs <- bootstrapCI(mod2, B_outer = 1000, B_inner = 10)
-#' }
 #' 
 #' @export
 bootstrapCI <- function(object, which = NULL,