Should fix applyFolds issue for irregular data (#24 (comment))

Author: Almond-S

R/FDboost.R

@@ -398,6 +398,19 @@
 #' ## plot(mod4)
 #' ## plotPredicted(mod4, lwdPred = 2)
 #' 
+#' \donttest{
+#'   ## Find optimal mstop, small grid/low B for a fast example
+#'   set.seed(123)
+#'   folds4 <- cv(rep(1, length(unique(mod4$id))), B = 3)
+#'   appl4 <- applyFolds(mod4, folds = folds4, grid = 1:50)
+#'   ## val4 <- validateFDboost(mod4, folds = folds4, grid = 1:50)
+#' 
+#'   set.seed(123)
+#'   folds4long <- cvLong(id = mod4$id, weights = model.weights(mod4), B = 3)
+#'   cvm4 <- cvrisk(mod4, folds = folds4long, grid = 1:50)
+#'   mstop(cvm4)
+#' }
+#' 
 #' ## Be careful if you want to predict newdata with irregular response,  
 #' ## as the argument index is not considered in the prediction of newdata. 
 #' ## Thus, all covariates have to be repeated according to the number of observations 

R/utilityFunctions.R

@@ -1182,6 +1182,8 @@ reweightData <- function(data, argvals, vars,
       i <- i + 1
     }
     idvars_new <- c(factor(my_temp_idvars))
+    # regain 1:n ids format expected by FDboost 
+    idvars_new <- as.numeric(idvars_new)
     ## check whether id variable of hmatrix-object and id variable of long variables are equal
     if(!is.null(idvars_new_hmatrix)){
       if(!all(idvars_new == idvars_new_hmatrix))