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

Description Usage Arguments Value Note Author(s) References See Also Examples

View source: R/kfolds2Mclassedind.R

Description

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

Usage

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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
frederic.bertrand@math.unistra.fr
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

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

plsRglm documentation built on March 16, 2021, 1:08 a.m.