update docu for CRAN

Author: davidruegamer

DESCRIPTION

@@ -58,7 +58,7 @@ Collate:
     'methods.R'
     'stabsel.R'
     'utilityFunctions.R'
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
 Encoding: UTF-8
 BugReports: https://github.com/boost-R/FDboost/issues
 URL: https://github.com/boost-R/FDboost

R/FDboost.R

@@ -106,7 +106,7 @@
 #' With \code{FDboost} the following covariate effects can be estimated by specifying 
 #' the following effects in the \code{formula}
 #' (similar to function \code{\link[refund]{pffr}} 
-#' in R-package \code{\link[refund:refund-package]{refund}}). 
+#' in R-package refund. 
 #' The \code{timeformula} is used to expand the effects in \code{t}-direction. 
 #' \itemize{
 #' \item Linear functional effect of scalar (numeric or factor) covariate \eqn{z} that varies 

R/bootstrapCIs.R

@@ -101,15 +101,10 @@
 #' plot(bootCIs, ask = FALSE)
 #' }
 #' 
-#' ## 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$id, weights = 
-#' model.weights(object), 
-#' B = 10 # 10-fold for inner resampling
-#' ), mc.cores = if(isWindows) 1 else 10) # use ten cores
+#' model.weights(object), B = 2) # 10-fold for inner resampling
+#' ) 
 #' }
 #' 
 #' \donttest{
@@ -121,8 +116,7 @@
 #' ## function in the outer resampling 
 #' 
 #' \donttest{
-#' bootCIs <- bootstrapCI(m1, mc.cores = if(isWindows) 1 else 30, 
-#'                        B_inner = 5, B_outer = 3)
+#' bootCIs <- bootstrapCI(m1, B_inner = 5, B_outer = 3)
 #' }
 #' 
 #' ## Now let's parallelize the outer resampling and use 
@@ -132,16 +126,14 @@
 #' cvrisk(object, folds = cvLong(id = object$id, weights = 
 #' model.weights(object), type = "kfold", # use CV
 #' B = 5, # 5-fold for inner resampling
-#' ),
-#' mc.cores = if(isWindows) 1 else 5) # use five cores
+#' )) # 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 = 10), fun = fun,
-#' mc.cores = if(isWindows) 1 else 10) # parallelize on 10 cores
+#' type = "bootstrap", B = 10), fun = fun) # parallelize on 10 cores
 #' }
 #' 
 #' \donttest{

R/crossvalidation.R

@@ -646,7 +646,7 @@ applyFolds <- function(object, folds = cv(rep(1, length(unique(object$id))), typ
 #' mod <- mod[75]
 #' 
 #'   #### create folds for 3-fold bootstrap: one weight for each curve
-#'   set.seed(123)
+#'   set.seed(124)
 #'   folds_bs <- cv(weights = rep(1, mod$ydim[1]), type = "bootstrap", B = 3)
 #' 
 #'   ## compute out-of-bag risk on the 3 folds for 1 to 75 boosting iterations  
@@ -670,7 +670,7 @@ applyFolds <- function(object, folds = cv(rep(1, length(unique(object$id))), typ
 #'   ## plot the estimated coefficients per fold
 #'   ## more meaningful for higher number of folds, e.g., B = 100 
 #'   par(mfrow = c(2,2))
-#'   plotPredCoef(cvr2, terms = FALSE, which = 2)
+#'   plotPredCoef(cvr2, terms = FALSE, which = 1)
 #'   plotPredCoef(cvr2, terms = FALSE, which = 3)
 #'   
 #'   ## compute out-of-bag risk and predictions for leaving-one-curve-out cross-validation