plot.variable.selection: Plot the results of the testing procedure to determine the...

In bootsPLS: Bootstrap Subsamplings of Sparse Partial Least Squares - Discriminant Analysis for Classification and Signature Identification

Description

Plot the results of the testing procedure to determine the number of variables to select on each component

Usage

## S3 method for class 'variable.selection'
plot(x,pch,col,type,lty,...)

Arguments

x	Object of class "variable.selection".
pch	A vector of length ncomp (argument of bootsPLS). See par for details.
col	A vector of length ncomp (argument of bootsPLS). See par for details.
type	A vector of length ncomp (argument of bootsPLS). See par for details.
lty	A vector of length ncomp (argument of bootsPLS). See par for details.
...	not used

Details

This function plots the p-value of the one sided t-test with respect to the frequency of each variable. See variable.selection and 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

bootsPLS, variable.selection

Examples

## Not run: 
data(MSC)
X=MSC$X
Y=MSC$Y


boot=bootsPLS(X=X,Y=Y,ncomp=3,many=5,kCV=5)
out=variable.selection(boot,ncomp=2,limit=c(40,40))
plot(out)



## End(Not run)

bootsPLS documentation built on May 2, 2019, 2:44 a.m.