bootsPLS (version 1.1.2)

variable.selection: Tune the number of variables on each component

Description

Performs a testing procedure to choose the number of variable on each component

Usage

variable.selection(object,ncomp,alpha,limit,showProgress=TRUE)

Arguments

object

a `bootsPLS' object', as obtained from bootsPLS

ncomp

How many component?

alpha

Level of the test

limit

Vector of maximal number of genes to include on each component

showProgress

Logical. If TRUE, shows the progress of the algorithm.

Value

A 'variable.selection' object is returned for which plot is available.

pval

pvalue obtained from the testing procedure.

opt

Number of components chosen by the procedure

signature

Variables chosen on each of the ncomp components

object

input `bootsPLS' object

alpha

input level of the test

Details

The testing procedure ranks the variables by decreasing frequency in object$frequency, for each component. Random subsamplings are constructed, a spls.hybrid is fitted on the internal learning set and a prediction is made on the internal test set. The testing procedure evaluates the gain in classification accuracy over the random subsamplings when a new variable is added from a decreasing frequency. This is done by on-sided t-test of level alpha. See the reference below for more details on the multiple testing procedure.

References

Rohart et al. (2016). A Molecular Classification of Human Mesenchymal Stromal Cells. PeerJ, DOI 10.7717/peerj.1845

See Also

spls.hybrid, plot, fit.model, prediction

Examples

# NOT RUN {
data(MSC)
X=MSC$X
Y=MSC$Y
dim(X)
table(Y)

boot=bootsPLS(X=X,Y=Y,ncomp=3,many=5,kCV=5)
out=variable.selection(boot,ncomp=2)

out2=variable.selection(boot,ncomp=2,limit=c(40,40))

# }