kfolds2Mclassedind: Number of missclassified individuals per group for k-fold...

In fbertran/plsRglm: Partial Least Squares Regression for Generalized Linear Models

Number of missclassified individuals per group for k-fold cross validated partial least squares regression models.

Description

This function indicates the number of missclassified individuals per group for k-fold cross validated partial least squares regression models.

Usage

kfolds2Mclassedind(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression model used on binary data

Value

list	Number of missclassified individuals per group vs number of components for the first group partition
list()	...
list	Number of missclassified individuals per group vs number of components for the last group partition

Note

Use cv.plsR or cv.plsRglm to create k-fold cross validated partial least squares regression models or generalized linear ones.

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, kfolds2Press, kfolds2Pressind and kfolds2Mclassed to extract and transforms results from k-fold cross-validation.

Examples

data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
kfolds2Mclassedind(cv.plsR(object=yaze_compl,dataX=Xaze_compl,nt=10,K=8,NK=1,verbose=FALSE))
kfolds2Mclassedind(cv.plsR(object=yaze_compl,dataX=Xaze_compl,nt=10,K=8,NK=2,verbose=FALSE))
rm(list=c("Xaze_compl","yaze_compl"))

fbertran/plsRglm documentation built on March 23, 2023, 2:14 a.m.