plsRglm-package

Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <arXiv:1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

References

A short paper that sums up some of features of the package is available on https://arxiv.org/, Frédéric Bertrand and Myriam Maumy-Bertrand (2018), "plsRglm: Partial least squares linear and generalized linear regression for processing incomplete datasets by cross-validation and bootstrap techniques with R", *arxiv*, https://arxiv.org/abs/1810.01005, https://github.com/fbertran/plsRglm/ et https://fbertran.github.io/plsRglm/

Examples

set.seed(314)
library(plsRglm)
data(Cornell)
cv.modpls<-cv.plsR(Y~.,data=Cornell,nt=6,K=6)
#> NK: 1 
#> Number of groups : 6 
#> 1 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 2 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 3 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 4 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 5 components could thus be extracted
#> ****________________________________________________****
#> 
#> 5 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 6 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
res.cv.modpls<-cvtable(summary(cv.modpls))
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> 
#> NK: 1
#> 
#> CV Q2 criterion:
#> 0 1 
#> 0 1 
#> 
#> CV Press criterion:
#> 1 2 3 
#> 0 0 1