plsRglm (version 0.3.3)

kfolds2CVinfos_glm: Extracts and computes information criteria and fits statistics for kfold cross validated partial least squares glm models

Description

This function extracts and computes information criteria and fits statistics for kfold cross validated partial least squares glm models.

Usage

kfolds2CVinfos_glm(pls_kfolds, MClassed = FALSE)

Arguments

pls_kfolds

an object computed using PLS_v1_kfoldcv

MClassed

should number of miss classed be computed

Value

• list
  table of fit statistics for first group partition
• ...
  ...
• list
  table of fit statistics for last group partition

Details

The Mclassed option should only set to TRUE if the response is binary.

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 and kfolds2Mclassed to extract and transforms results from kfold cross validation.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
bbb <- PLS_glm_kfoldcv(dataY=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),nt=6,K=12,NK=1,keepfolds=FALSE,keepdataY=TRUE,modele="pls")
kfolds2CVinfos_glm(bbb)
rm(list=c("XCornell","yCornell","bbb"))


data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
bbb <- PLS_glm_kfoldcv(yaze_compl,Xaze_compl,nt=10,K=12,NK=1,keepfolds=FALSE,keepdataY=TRUE,modele="pls")
kfolds2CVinfos_glm(bbb,MClassed=TRUE)
bbb2 <- PLS_glm_kfoldcv(yaze_compl,Xaze_compl,nt=10,K=12,NK=1,keepfolds=FALSE,keepdataY=TRUE,modele="pls-glm-logistic")
kfolds2CVinfos_glm(bbb2,MClassed=TRUE)
rm(list=c("Xaze_compl","yaze_compl","bbb","bbb2"))


#install.packages("chemometrics")
library(chemometrics)
data(hyptis)
hyptis
yhyptis <- factor(hyptis$Group,ordered=TRUE)
Xhyptis <- as.data.frame(hyptis[,c(1:6)])
options(contrasts = c("contr.treatment", "contr.poly"))
modpls2 <- plsRglm(yhyptis,Xhyptis,6,modele="pls-glm-polr")
modpls2$Coeffsmodel_vals
modpls2$InfCrit
modpls2$Coeffs
modpls2$std.coeffs

table(yhyptis,predict(modpls2$FinalModel,type="class"))

modpls3 <- PLS_glm(yhyptis[-c(1,2,3)],Xhyptis[-c(1,2,3),],3,modele="pls-glm-polr",dataPredictY=Xhyptis[c(1,2,3),])
bbb <- PLS_glm_kfoldcv(yhyptis,Xhyptis,nt=4,K=10,random=TRUE,modele="pls-glm-polr",keepcoeffs=T)
kfolds2CVinfos_v2(bbb,MClassed=TRUE)