cv.plsR: Partial least squares regression models with k-fold...

In plsRglm: Partial Least Squares Regression for Generalized Linear Models

Partial least squares regression models with k-fold cross-validation

Description

This function implements k-fold cross-validation on complete or incomplete datasets for partial least squares regression models

Usage

cv.plsR(object, ...)
## Default S3 method:
cv.plsRmodel(object,dataX,nt=2,limQ2set=.0975,modele="pls", 
K=5, NK=1, grouplist=NULL, random=TRUE, scaleX=TRUE, 
scaleY=NULL, keepcoeffs=FALSE, keepfolds=FALSE, keepdataY=TRUE, 
keepMclassed=FALSE, tol_Xi=10^(-12), weights, verbose=TRUE,...)
## S3 method for class 'formula'
cv.plsRmodel(object,data=NULL,nt=2,limQ2set=.0975,modele="pls", 
K=5, NK=1, grouplist=NULL, random=TRUE, scaleX=TRUE, 
scaleY=NULL, keepcoeffs=FALSE, keepfolds=FALSE, keepdataY=TRUE, 
keepMclassed=FALSE, tol_Xi=10^(-12), weights,subset,contrasts=NULL, verbose=TRUE,...)
PLS_lm_kfoldcv(dataY, dataX, nt = 2, limQ2set = 0.0975, modele = "pls", 
K = 5, NK = 1, grouplist = NULL, random = TRUE, scaleX = TRUE, 
scaleY = NULL, keepcoeffs = FALSE, keepfolds = FALSE, keepdataY = TRUE, 
keepMclassed=FALSE, tol_Xi = 10^(-12), weights, verbose=TRUE)
PLS_lm_kfoldcv_formula(formula,data=NULL,nt=2,limQ2set=.0975,modele="pls", 
K=5, NK=1, grouplist=NULL, random=TRUE, scaleX=TRUE, 
scaleY=NULL, keepcoeffs=FALSE, keepfolds=FALSE, keepdataY=TRUE, 
keepMclassed=FALSE, tol_Xi=10^(-12), weights,subset,contrasts=NULL,verbose=TRUE)

Arguments

object	response (training) dataset or 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'.
dataY	response (training) dataset
dataX	predictor(s) (training) dataset
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 from environment(formula), typically the environment from which plsRglm is called.
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. Defaults to 5.
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. Defaults to TRUE
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.
verbose	should info messages be displayed ?
...	arguments to pass to cv.plsRmodel.default or to cv.plsRmodel.formula

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.

Value

An object of class "cv.plsRmodel".

results_kfolds	list of NK. Each element of the list sums up the results for a group division: list of K matrices of size about nrow(dataX)/K * nt with the predicted values for a growing number of components ... ... list of K matrices of size about nrow(dataX)/K * nt with the predicted values for a growing number of components
folds	list of NK. Each element of the list sums up the results for a group division: list of K vectors of length about nrow(dataX) with the numbers of the rows of dataX that were used as a training set ... ... list of K vectors of length about nrow(dataX) with the numbers of the rows of dataX that were used as a training set
dataY_kfolds	list of NK. Each element of the list sums up the results for a group division: list of K matrices of size about nrow(dataX)/K * 1 with the observed values of the response ... ... list of K matrices of size about nrow(dataX)/K * 1 with the observed values of the response
call	the call of the function

Note

Work for complete and incomplete datasets.

Author(s)

Frederic Bertrand
[e-mail]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frederic Bertrand (2010). Comparing the linear and the logistic PLS regression with qualitative predictors: application to allelotyping data. Journal de la Societe Francaise de Statistique, 151(2), pages 1-18. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/47

See Also

Summary method summary.cv.plsRmodel. kfolds2coeff, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind, kfolds2Mclassed and kfolds2CVinfos_lm to extract and transform results from k-fold cross-validation.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

#Leave one out CV (K=nrow(Cornell)) one time (NK=1)
bbb <- cv.plsR(object=yCornell,dataX=XCornell,nt=6,K=nrow(Cornell),NK=1)
bbb2 <- cv.plsR(Y~.,data=Cornell,nt=6,K=12,NK=1,verbose=FALSE)
(sum1<-summary(bbb2))

#6-fold CV (K=6) two times (NK=2)
#use random=TRUE to randomly create folds for repeated CV
bbb3 <- cv.plsR(object=yCornell,dataX=XCornell,nt=6,K=6,NK=2)
bbb4 <- cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=2,verbose=FALSE)
(sum3<-summary(bbb4))

cvtable(sum1)
cvtable(sum3)
rm(list=c("XCornell","yCornell","bbb","bbb2","bbb3","bbb4"))

plsRglm documentation built on March 31, 2023, 11:10 p.m.