View source: R/kfolds2Pressind.R
kfolds2Pressind | R Documentation |
This function computes individual PRESS for k-fold cross validated partial least squares regression models.
kfolds2Pressind(pls_kfolds)
pls_kfolds |
a k-fold cross validated partial least squares regression model |
list |
Individual Press vs number of components for the first group partition |
list() |
... |
list |
Individual Press vs number of components for the last group partition |
Use cv.plsR
to create k-fold cross validated partial
least squares regression models.
Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
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
kfolds2coeff
, kfolds2Press
,
kfolds2Mclassedind
and kfolds2Mclassed
to
extract and transforms results from k-fold cross validation.
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
kfolds2Pressind(cv.plsR(object=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),
nt=6,K=12,NK=1))
kfolds2Pressind(cv.plsR(object=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),
nt=6,K=6,NK=1))
rm(list=c("XCornell","yCornell"))
data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
kfolds2Pressind(cv.plsR(object=ypine,dataX=Xpine,nt=10,NK=1,verbose=FALSE))
kfolds2Pressind(cv.plsR(object=ypine,dataX=Xpine,nt=10,NK=2,verbose=FALSE))
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
kfolds2Pressind(cv.plsR(object=ypine,dataX=XpineNAX21,nt=10,NK=1,verbose=FALSE))
kfolds2Pressind(cv.plsR(object=ypine,dataX=XpineNAX21,nt=10,NK=2,verbose=FALSE))
rm(list=c("Xpine","XpineNAX21","ypine"))
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