generate.split: Generating random splittings into learning and test data sets
In WilcoxCV: Wilcoxon-based variable selection in cross-validation

Description Usage Arguments Details Value Author(s) References See Also Examples

The function generate.split generates niter random splittings into learning and test data sets for use in Monte-Carlo cross-validation (MCCV).

1	generate.split(niter,n,ntest)

`niter`	The number of iterations (number of splits into learning and split sets).
`n`	The total number of observations in the data set.
`ntest`	The number of observations in the test sets.

This function is meant for use in Monte-Carlo cross-validation (MCCV).

A niter x ntest matrix giving the indices of the observations included in the test sets. The i-th row gives the indices of the ntest observations included in the test set for the i-th MCCV iteration.

Anne-Laure Boulesteix (http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/index.html)

A. L. Boulesteix (2007). WilcoxCV: an R package for fast variable selection in cross-validation. Bioinformatics 23:1702-1704.

generate.cv,wilcox.split,wilcox.selection.split

# load WilcoxCV library
library(WilcoxCV)

# Generate 50 splits with ratio 2:1 for a data set including 90 observations
my.split<-generate.split(niter=50,n=90,ntest=30)