plsRglm (version 0.3.3)

PLS_lm_kfoldcv: Partial least squares regression models with kfold cross validation

Description

This function implements kfold cross validation on complete or incomplete datasets for partial least squares regression models

Usage

PLS_lm_kfoldcv(dataY, dataX, nt = 2, limQ2set = 0.0975, modele = "pls", K = nrow(dataX), NK = 1, grouplist = NULL, random = FALSE, scaleX = TRUE, scaleY = NULL, keepcoeffs = FALSE, keepfolds = FALSE, keepdataY = TRUE, keepMclassed=FALSE, tol_Xi = 10^(-12))

Arguments

dataY

response (training) dataset

dataX

predictor(s) (training) dataset

nt

number of components to be extracted

limQ2set

limit value for the Q2

modele

name of the PLS model to be fitted, only ("pls" available for this fonction.

K

number of groups

NK

number of times the group division is made

grouplist

to specify the members of the K groups

random

should the K groups be made randomly

scaleX

scale the predictor(s) : must be set to TRUE for modele="pls" and should be for glms pls.

scaleY

scale the response : Yes/No. Ignored since non always possible for glm responses.

keepcoeffs

shall the coefficients for each model be returned

keepfolds

shall the groups' composition be returned

keepdataY

shall the observed value of the response for each one of the predicted value be returned

keepMclassed

shall the number of miss classed be returned

tol_Xi

minimal value for Norm2(Xi) and $\mathrm{det}(pp' \times pp)$ if there is any missing value in the dataX. It defaults to $10^{-12}$

Value

• results_kfolds
  list of NK. Each element of the list sums up the results for a group division: [object Object],[object Object],[object Object]
• folds
  list of NK. Each element of the list sums up the results for a group division: [object Object],[object Object],[object Object]
• dataY_kfolds
  list of NK. Each element of the list sums up the results for a group division: [object Object],[object Object],[object Object]
• call
  the call of the function

Details

Predicts 1 group with the K-1 other groups. Leave one out cross validation is thus obtained for K==nrow(dataX).

References

Nicolas Meyer, Myriam Maumy-Bertrand et Fr�d�ric{Fr'ed'eric} Bertrand (2010). Comparaison de la r�gression{r'egression} PLS et de la r�gression{r'egression} logistique PLS : application aux donn�es{donn'ees} d'all�lotypage{d'all'elotypage}. Journal de la Soci�t� Fran�aise de Statistique, 151(2), pages 1-18. http://smf4.emath.fr/Publications/JSFdS/151_2/pdf/sfds_jsfds_151_2_1-18.pdf

See Also

kfolds2coeff, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind, kfolds2Mclassed and kfolds2CVinfos_lm to extract and transform results from kfold cross validation.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,K=12,keepfolds=TRUE)
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,K=12,keepfolds=FALSE)
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,K=6,NK=2,random=FALSE,keepfolds=TRUE)
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,K=6,NK=2,random=TRUE,keepfolds=TRUE)
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,keepcoeffs=TRUE,keepfolds=TRUE)
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,keepcoeffs=TRUE,keepfolds=FALSE)

bbb <- PLS_lm_kfoldcv(dataY=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),nt=6,K=12,NK=1)
bbb2 <- PLS_lm_kfoldcv(dataY=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),nt=6,K=6,NK=1)
kfolds2CVinfos_lm(bbb)
kfolds2CVinfos_lm(bbb2)
PLS_lm(yCornell,XCornell,6,typeVC="standard")$CVinfos
rm(list=c("XCornell","yCornell","bbb","bbb2"))


data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
bbb <- PLS_lm_kfoldcv(dataY=log(ypine),dataX=Xpine,nt=10,K=12,NK=1)
bbb2 <- PLS_lm_kfoldcv(dataY=log(ypine),dataX=Xpine,nt=10,K=6,NK=1)
kfolds2CVinfos_lm(bbb)
kfolds2CVinfos_lm(bbb2)
PLS_lm(log(ypine),Xpine,10,typeVC="standard")$CVinfos

XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
bbbNA <- PLS_lm_kfoldcv(dataY=log(ypine),dataX=XpineNAX21,nt=10,K=12,NK=1)
bbbNA2 <- PLS_lm_kfoldcv(dataY=log(ypine),dataX=XpineNAX21,nt=10,K=6,NK=1)
kfolds2CVinfos_lm(bbbNA)
kfolds2CVinfos_lm(bbbNA2)
PLS_lm(log(ypine),XpineNAX21,10,typeVC="standard")$CVinfos
rm(list=c("Xpine","XpineNAX21","ypine","bbb","bbb2","bbbNA","bbbNA2"))