plsRglm (version 0.7.4)

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 allows for missing data in the eXplanatory variables. Bootstrap confidence intervals constructions are also available.

Details

ll{ Package: plsRglm Version: 0.6.3 Date: 2011-01-08 Depends: R (>= 2.4.0) Imports: mvtnorm, boot Enhances: pls Suggests: MASS License: GPL-3 Encoding: latin1 URL: http://www-irma.u-strasbg.fr/~fbertran/ Classification/MSC: 62J12, 62J99 Packaged: 2011-01-08 14:47:17 UTC; Bertrand Built: R 2.11.1; ; 2011-01-08 14:47:17 UTC; windows } Index: AICpls AIC functions for plsR models aze Microsat Dataset aze_compl As aze without missing values bootpls Non-parametric Bootstrap for PLS models bootplsglm Non-parametric Bootstrap for PLS generalized linear models bordeaux Quality of wine dataset boxplots.bootpls Boxplot bootstrap distributions coefs.plsR Coefficients for bootstrap computations coefs.plsRglm Coefficients for bootstrap computations confints.bootpls Bootstrap confidence intervals CorMat Correlation matrix for simulating plsR datasets Cornell Cornell dataset dicho Dichotomization fowlkes Fowlkes dataset kfolds2Chisq Computes Predicted Chisquare for kfold cross validated partial least squares regression models. kfolds2Chisqind Computes individual Predicted Chisquare for kfold cross validated partial least squares regression models. kfolds2coeff Extracts coefficients from kfold cross validated partial least squares regression models kfolds2CVinfos_glm Extracts and computes information criteria and fits statistics for kfold cross validated partial least squares glm models kfolds2CVinfos_v2 Extracts and computes information criteria and fits statistics for kfold cross validated partial least squares glm models kfolds2Mclassed Number of missclassified individuals for kfold cross validated partial least squares regression models. kfolds2Mclassedind Number of missclassified individuals per group for kfold cross validated partial least squares regression models. kfolds2Press Computes PRESS for kfold cross validated partial least squares regression models. kfolds2Pressind Computes individual PRESS for kfold cross validated partial least squares regression models. loglikpls loglikelihood functions for plsR models permcoefs.plsR Coefficients computation for permutation bootstrap permcoefs.plsRglm Coefficients computation for permutation bootstrap pine Pine dataset pine_full Full pine dataset pine_sup Supplementary data for pine dataset plots.confints.bootpls Plot bootstrap confidence intervals PLS_glm Partial least squares Regression generalized linear models PLS_glm_formula Partial least squares Regression generalized linear models PLS_glm_kfoldcv Partial least squares regression glm models with kfold cross validation PLS_glm_kfoldcv_formula Partial least squares regression glm models with kfold cross validation PLS_glm_wvc Light version of PLS\_glm for cross validation purposes PLS_lm Partial least squares Regression models with leave one out cross validation PLS_lm_formula Partial least squares Regression models with leave one out cross validation PLS_lm_kfoldcv Partial least squares regression models with kfold cross validation PLS_lm_kfoldcv_formula Partial least squares regression models with kfold cross validation PLS_lm_wvc Light version of PLS\_lm for cross validation purposes PLS_v1 Partial least squares Regression models with leave one out cross validation PLS_v1_kfoldcv Partial least squares regression models with kfold cross validation PLS_v1_wvc Light version of PLS\_v1 for cross validation purposes PLS_v2 Partial least squares Regression generalized linear models PLS_v2_kfoldcv Partial least squares regression glm models with kfold cross validation PLS_v2_wvc Light version of PLS\_v2 for cross validation purposes plsR Partial least squares Regression models with leave one out cross validation plsRglm Partial least squares Regression generalized linear models plsRglm-package Partial least squares Regression for generalized linear models print.plsRglmmodel Print method for plsRglm models print.plsRmodel Print method for plsR models print.summary.plsRglmmodel Print method for summaries of plsRglm models print.summary.plsRmodel Print method for summaries of plsR models simul_data_complete Data generating detailed process for multivariate plsR models simul_data_UniYX Data generating function for univariate plsR models simul_data_YX Data generating function for multivariate plsR models summary.plsRglmmodel Summary method for plsRglm models summary.plsRmodel Summary method for plsR models tilt.bootpls Tilted bootstrap for PLS models tilt.bootplsglm Tilted bootstrap for PLS models XbordeauxNA Missing data analysis for the quality of wine dataset XpineNAX21 Missing data analysis for the pine dataset

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

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