plsRglm (version 1.5.1)

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

Description

This function extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares glm models for both formula or classic specifications of the model.

Usage

kfolds2CVinfos_glm(pls_kfolds, MClassed = FALSE, verbose = TRUE)

Value

list

table of fit statistics for first group partition

list()

...

list

table of fit statistics for last group partition

Arguments

pls_kfolds

an object computed using cv.plsRglm

MClassed

should number of miss classed be computed ?

verbose

should infos be displayed ?

Author

Frédéric Bertrand

https://fbertran.github.io/homepage/

Details

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

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, kfolds2Press, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross-validation.

Examples

# \donttest{
data(Cornell)
summary(cv.plsRglm(Y~.,data=Cornell,
nt=6,K=12,NK=1,keepfolds=FALSE,keepdataY=TRUE,modele="pls",verbose=FALSE),MClassed=TRUE)


data(aze_compl)
summary(cv.plsR(y~.,data=aze_compl,nt=10,K=8,modele="pls",verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,modele="pls",verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,
modele="pls-glm-family",
family=gaussian(),verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,
modele="pls-glm-logistic",
verbose=FALSE),MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,
modele="pls-glm-family",
family=binomial(),verbose=FALSE),
MClassed=TRUE,verbose=FALSE)


if(require(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),],verbose=FALSE)

summary(cv.plsRglm(factor(Group,ordered=TRUE)~.,data=hyptis[,-c(7,8)],nt=4,K=10,
random=TRUE,modele="pls-glm-polr",keepcoeffs=TRUE,verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
}
# }