| wilcox.split {WilcoxCV} | R Documentation |
The function wilcox.split computes the Wilcoxon rank sum statistic for all niter CV or MCCV iterations defined by the matrix split.
wilcox.split(x,y,split,algo="new")
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. |
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.
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. |
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.
wilcox.test, generate.split, generate.cv, wilcox.selection.split
# 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")