plsRglm (version 0.7.4)

PLS_lm_kfoldcv_formula: Partial least squares regression models with kfold cross validation

Description

This function implements kfold cross validation on complete or incomplete datasets for partial least squares regression models (formula specification of the model).

Usage

PLS_lm_kfoldcv_formula(formula,data=NULL,nt=2,limQ2set=.0975,modele="pls", K=nrow(dataX), NK=1, grouplist=NULL, random=FALSE, scaleX=TRUE, scaleY=NULL, keepcoeffs=FALSE, keepfolds=FALSE, keepdataY=TRUE, keepMclassed=FALSE, tol_Xi=10^(-12), weights,subset,contrasts=NULL)

Arguments

formula

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken fro

nt

number of components to be extracted

limQ2set

limit value for the Q2

modele

name of the PLS model to be fitted, only ("pls" available for this fonction.

K

number of groups

NK

number of times the group division is made

grouplist

to specify the members of the K groups

random

should the K groups be made randomly

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

shall the coefficients for each model be returned

keepfolds

shall the groups' composition be returned

keepdataY

shall the observed value of the response for each one of the predicted value be returned

keepMclassed

shall the number of miss classed be returned

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 $10^{-12}$

weights

an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

contrasts

an optional list. See the contrasts.arg of model.matrix.default.

Value

• results_kfolds
  list of NK. Each element of the list sums up the results for a group division: [object Object],[object Object],[object Object]
• folds
  list of NK. Each element of the list sums up the results for a group division: [object Object],[object Object],[object Object]
• dataY_kfolds
  list of NK. Each element of the list sums up the results for a group division: [object Object],[object Object],[object Object]
• call
  the call of the function

Details

Predicts 1 group with the K-1 other groups. Leave one out cross validation is thus obtained for K==nrow(dataX). A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed. A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second. The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this pass a terms object as the formula. Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations.

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

kfolds2coeff, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind, kfolds2Mclassed and kfolds2CVinfos_lm to extract and transform results from kfold cross validation.

Examples

data(Cornell)
PLS_lm_kfoldcv_formula(Y~.,Cornell,nt=3,K=12,keepfolds=TRUE)
PLS_lm_kfoldcv_formula(Y~.,Cornell,nt=3,K=12,keepfolds=FALSE)
PLS_lm_kfoldcv_formula(Y~.,Cornell,nt=3,K=6,NK=2,random=FALSE,keepfolds=TRUE)
PLS_lm_kfoldcv_formula(Y~.,Cornell,nt=3,K=6,NK=2,random=TRUE,keepfolds=TRUE)
PLS_lm_kfoldcv_formula(Y~.,Cornell,nt=3,keepcoeffs=TRUE,keepfolds=TRUE)
PLS_lm_kfoldcv_formula(Y~.,Cornell,nt=3,keepcoeffs=TRUE,keepfolds=FALSE)

bbb <- PLS_lm_kfoldcv_formula(Y~scale(as.matrix(Cornell))[,-8],Cornell,nt=6,K=12,NK=1)
bbb2 <- PLS_lm_kfoldcv_formula(Y~scale(as.matrix(Cornell))[,-8],Cornell,nt=6,K=6,NK=1)
kfolds2CVinfos_lm(bbb)
kfolds2CVinfos_lm(bbb2)
PLS_lm_formula(Y~.,Cornell,6,typeVC="standard")$CVinfos
rm(list=c("bbb","bbb2"))


data(pine)
bbb <- PLS_lm_kfoldcv_formula(log(x11)~.,pine,nt=10,K=12,NK=1)
bbb2 <- PLS_lm_kfoldcv_formula(log(x11)~.,pine,nt=10,K=6,NK=1)
kfolds2CVinfos_lm(bbb)
kfolds2CVinfos_lm(bbb2)
PLS_lm_formula(log(x11)~.,pine,10,typeVC="standard")$CVinfos

pineNAX21 <- pine
pineNAX21[1,2] <- NA
bbbNA <- PLS_lm_kfoldcv_formula(log(x11)~.,pineNAX21,nt=10,K=12,NK=1)
bbbNA2 <- PLS_lm_kfoldcv_formula(log(x11)~.,pineNAX21,nt=10,K=6,NK=1)
kfolds2CVinfos_lm(bbbNA)
kfolds2CVinfos_lm(bbbNA2)
PLS_lm_formula(log(x11)~.,pineNAX21,10,typeVC="standard")$CVinfos
rm(list=c("bbb","bbb2","bbbNA","bbbNA2"))