plsRglm (version 0.3.3)

plsRglm-package: Partial least squares Regression for generalized linear models

Description

This packages provides Partial least squares Regression for generalized linear models and kfold crossvalidation of such models using various criteria. It works either for complete datasets or datasets with missing values.

Details

ll{ Package: plsRglm Version: 0.1.2 Date: 2009-09-06 Requires: Enhances: Suggests: MASS, mvtnorm Depends: R (>= 2.4.0) License: GPL-3 Encoding: latin1 URL: http://www-irma.u-strasbg.fr/~fbertran/ Classification/MSC: 62J12, 62J99 Packaged: Tue Sep 8 01:22:14 2009; Administrateur Built: R 2.8.0; ; 2009-09-08 01:22:15; windows } Index: AICpls ~~function to do ... ~~ AICpls2 ~~function to do ... ~~ aze ~~ data name/kind ... ~~ aze_compl ~~ data name/kind ... ~~ bordeaux ~~ data name/kind ... ~~ CorMat ~~ data name/kind ... ~~ Cornell ~~ data name/kind ... ~~ dicho ~~function to do ... ~~ fowlkes ~~ data name/kind ... ~~ kfolds2Chisq ~~function to do ... ~~ kfolds2Chisqind ~~function to do ... ~~ kfolds2coeff ~~function to do ... ~~ kfolds2CVinfos_glm ~~function to do ... ~~ kfolds2CVinfos_lm ~~function to do ... ~~ kfolds2CVinfos_v1 ~~function to do ... ~~ kfolds2CVinfos_v2 ~~function to do ... ~~ kfolds2Press ~~function to do ... ~~ kfolds2Pressind ~~function to do ... ~~ loglikpls ~~function to do ... ~~ loglikpls2 ~~function to do ... ~~ pine ~~ data name/kind ... ~~ pine_full ~~ data name/kind ... ~~ pine_sup ~~ data name/kind ... ~~ PLS_glm ~~function to do ... ~~ PLS_glm_kfoldcv ~~function to do ... ~~ PLS_glm_wvc ~~function to do ... ~~ PLS_lm ~~function to do ... ~~ PLS_lm_kfoldcv ~~function to do ... ~~ PLS_lm_wvc ~~function to do ... ~~ PLS_v1 ~~function to do ... ~~ PLS_v1_kfoldcv ~~function to do ... ~~ PLS_v1_wvc ~~function to do ... ~~ PLS_v2 ~~function to do ... ~~ PLS_v2_kfoldcv ~~function to do ... ~~ PLS_v2_wvc ~~function to do ... ~~ plsR ~~function to do ... ~~ plsRglm ~~function to do ... ~~ plsRglmmodel.default ~~function to do ... ~~ plsRmodel.default ~~function to do ... ~~ print.plsRglmmodel ~~function to do ... ~~ print.plsRmodel ~~function to do ... ~~ print.summary.plsRglmmodel ~~function to do ... ~~ print.summary.plsRmodel ~~function to do ... ~~ simul_data_complete ~~function to do ... ~~ simul_data_UniYX ~~function to do ... ~~ simul_data_UniYX_old ~~function to do ... ~~ simul_data_YX ~~function to do ... ~~ summary.plsRglmmodel ~~function to do ... ~~ summary.plsRmodel ~~function to do ... ~~ XbordeauxNA ~~ data name/kind ... ~~ XpineNAX21 ~~ data name/kind ... ~~ ~~ An overview of how to use the package, including the most important functions ~~

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

Examples

data(pine)
ypine <- pine[,11]
Xpine <- pine[,1:10]
(Pinscaled <- as.data.frame(cbind(scale(log(ypine)),scale(as.matrix(Xpine)))))
colnames(Pinscaled)[1] <- "yy"

modpls <- plsR(log(ypine),Xpine,10)
modpls$Std.Coeffs