WilcoxCV (version 1.0-2)

wilcox.split: Wilcoxon rank sum statistic in cross-validation (CV) and Monte-Carlo cross-validation (MCCV)

Description

The function wilcox.split computes the Wilcoxon rank sum statistic for all niter CV or MCCV iterations defined by the matrix split.

Usage

wilcox.split(x,y,split,algo="new")

Arguments

x

a numeric vector of length n giving the expression levels of a gene for the n arrays.

y

a vector of length n giving the class membership for the n arrays. y can be either a factor or a numeric and must be coded as 0,1.

split

A niter x ntest matrix giving the indices of the ntest observations included in each of the niter test sets, as generated by the functions generate.split or generate.cv. The i-th row of split gives the indices of the observations included in the test data set for the i-th iteration.

algo

either "new" or "naive". If algo="new", the new fast method described in Boulesteix (2007) is used to compute the Wilcoxon rank statistic. If algo="naive", the Wilcoxon rank sum statistics are obtained by running the function wilcox.test niter times.

Value

A list with the following components:

wilcox.split

a numeric vector of length niter whose i-th component gives the Wilcoxon rank sum statistic obtained in the i-th iteration.

Details

The Wilcoxon rank sum statistic is defined as the sum of the X-ranks of the observations with y=0. The Wilcoxon rank sum test is equivalent to the Mann-Whitney test. It is implemented in the function wilcox.test.

In the context of cross-validation (CV) or Monte-Carlo cross-validation (MCCV), wilcox.selection.split computes the Wilcoxon rank sum statistic for each iteration. At each iteration, a subset of the n observations is excluded from the data set and considered as test data set. The indices of the observations considered as test set for each of the niter iterations are given in the niter x ntest matrix split.

References

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

See Also

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

Examples

# load WilcoxCV library
library(WilcoxCV)

# Generate data
x<-rnorm(100)
y<-sample(c(0,1),100,replace=TRUE)

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

# Compute the Wilcoxon rank sum statistic for the 50 iterations.
wilcox.split(x=x,y=y,split=my.split,algo="new")