kfolds2Press: Computes PRESS for k-fold cross validated partial least...

In plsRglm: Partial Least Squares Regression for Generalized Linear Models

Computes PRESS for k-fold cross validated partial least squares regression models.

Description

This function computes PRESS for k-fold cross validated partial least squares regression models.

Usage

kfolds2Press(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression model

Value

list	Press vs number of components for the first group partition
list()	...
list	Press vs number of components for the last group partition

Note

Use cv.plsR to create k-fold cross validated partial least squares regression models.

Author(s)

Frédéric Bertrand
[e-mail]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparing the linear and the logistic PLS regression with qualitative predictors: application to allelotyping data. Journal de la Societe Francaise de Statistique, 151(2), pages 1-18. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/47

See Also

kfolds2coeff, kfolds2Pressind, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross validation.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
kfolds2Press(cv.plsR(object=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),
nt=6,K=12,NK=1,verbose=FALSE))
kfolds2Press(cv.plsR(object=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),
nt=6,K=6,NK=1,verbose=FALSE))
rm(list=c("XCornell","yCornell"))


data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
kfolds2Press(cv.plsR(object=ypine,dataX=Xpine,nt=10,NK=1,verbose=FALSE))
kfolds2Press(cv.plsR(object=ypine,dataX=Xpine,nt=10,NK=2,verbose=FALSE))

XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
kfolds2Press(cv.plsR(object=ypine,dataX=XpineNAX21,nt=10,NK=1,verbose=FALSE))
kfolds2Press(cv.plsR(object=ypine,dataX=XpineNAX21,nt=10,NK=2,verbose=FALSE))
rm(list=c("Xpine","XpineNAX21","ypine"))

plsRglm documentation built on March 31, 2023, 11:10 p.m.