plsRglm (version 0.3.3)

PLS_v2_wvc: Light version of PLS_v2 for cross validation purposes

Description

Light version of PLS_v2 for cross validation purposes either on complete or incomplete datasets.

Usage

PLS_v2_wvc(dataY, dataX, nt = 2, dataPredictY = dataX, modele = "pls", family = NULL, scaleX = TRUE, scaleY = NULL, keepcoeffs = FALSE, keepstd.coeffs=FALSE, tol_Xi = 10^(-12))

Arguments

dataY

response (training) dataset

dataX

predictor(s) (training) dataset

nt

number of components to be extracted

dataPredictY

predictor(s) (testing) dataset

modele

name of the PLS glm model to be fitted ("pls", "pls-glm-gaussian", "pls-glm-logistic", "pls-glm-polr").

family

for the moment the family argumlent is ignored and set thanks to the value of modele

scaleX

scale the predictor(s) : must be set to TRUE for modele="pls" and should be for glms pls.

scaleY

scale the response : Yes/No. Ignored since non always possible for glm responses.

keepcoeffs

whether the coefficients of the linear fit on link scale of unstandardized eXplanatory variables should be returned or not.

keepstd.coeffs

whether the coefficients of the linear fit on link scale of standardized eXplanatory variables should be returned or not.

tol_Xi

minimal value for Norm2(Xi) and $\mathrm{det}(pp' \times pp)$ if there is any missing value in the dataX. It defaults to $10^{-12}$

Value

• valsPredict
  nrow(dataPredictY) * nt matrix of the predicted values
• coeffs
  If the coefficients of the eXplanatory variables were requested: i.e. keepcoeffs=TRUE. ncol(dataX) * 1 matrix of the coefficients of the the eXplanatory variables

Details

This function is called by PLS_v2_kfoldcv in order to perform cross validation either on complete or incomplete datasets.

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

PLS_v2 for more detailed results, PLS_v2_kfoldcv for cross validating models and PLS_v1_wvc for the same function dedicated to plsR models

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_v2_wvc(dataY=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",dataPredictY=XCornell[1,])
PLS_v2_wvc(dataY=yCornell[-1],dataX=XCornell[-1,],nt=3,modele="pls-glm-gaussian",dataPredictY=XCornell[1,])
rm("XCornell","yCornell")

## With an incomplete dataset (X[1,2] is NA)
data(pine)
ypine <- pine[,11]
data(XpineNAX21)
PLS_v2_wvc(dataY=ypine,dataX=XpineNAX21,nt=10,modele="pls-glm-gaussian")
rm("XpineNAX21","ypine")


data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
PLS_v2(yaze_compl,Xaze_compl,10,modele="pls-glm-logistic",typeVC="none")$InfCrit
PLS_v2_wvc(yaze_compl,Xaze_compl,10,modele="pls-glm-logistic", keepcoeffs=TRUE)
rm("Xaze_compl","yaze_compl")