plsRglm (version 1.2.5)

PLS_glm_wvc: Light version of PLS\_glm for cross validation purposes

Description

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

Usage

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

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-Gamma", "pls-glm-gaussian", "pls-glm-inverse.gaussian", "pls-glm-logistic", "pls-glm-poisson", "pls-glm-polr"). Use "modele=pls-glm-family" to enable the family option.

family

a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See family for details of family functions.) To use the family option, please set modele="pls-glm-family". User defined families can also be defined. See details.

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 \(10^{-12}\)

weights

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

method

logistic, probit, complementary log-log or cauchit (corresponding to a Cauchy latent variable).

verbose

should info messages be displayed ?

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_glm_kfoldcv_formula in order to perform cross-validation either on complete or incomplete datasets.

There are seven different predefined models with predefined link functions available :

"pls"

ordinary pls models

"pls-glm-Gamma"

glm gaussian with inverse link pls models

"pls-glm-gaussian"

glm gaussian with identity link pls models

"pls-glm-inverse-gamma"

glm binomial with square inverse link pls models

"pls-glm-logistic"

glm binomial with logit link pls models

"pls-glm-poisson"

glm poisson with log link pls models

"pls-glm-polr"

glm polr with logit link pls models

Using the "family=" option and setting "modele=pls-glm-family" allows changing the family and link function the same way as for the glm function. As a consequence user-specified families can also be used.

The gaussian family

accepts the links (as names) identity, log and inverse.

The binomial family

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

The Gamma family

accepts the links inverse, identity and log.

The poisson family

accepts the links log, identity, and sqrt.

The inverse.gaussian family

accepts the links 1/mu^2, inverse, identity and log.

The quasi family

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

The function power

can be used to create a power link function.

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 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

PLS_glm for more detailed results, PLS_glm_kfoldcv for cross-validating models and PLS_lm_wvc for the same function dedicated to plsR models

Examples

# NOT RUN {
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_glm_wvc(dataY=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",
dataPredictY=XCornell[1,])
PLS_glm_wvc(dataY=yCornell,dataX=XCornell,nt=3,modele="pls-glm-family",
family=gaussian(),dataPredictY=XCornell[1,], verbose=FALSE)
PLS_glm_wvc(dataY=yCornell[-1],dataX=XCornell[-1,],nt=3,modele="pls-glm-gaussian",
dataPredictY=XCornell[1,], verbose=FALSE)
PLS_glm_wvc(dataY=yCornell[-1],dataX=XCornell[-1,],nt=3,modele="pls-glm-family",
family=gaussian(),dataPredictY=XCornell[1,], verbose=FALSE)
rm("XCornell","yCornell")

# }
# NOT RUN {
## With an incomplete dataset (X[1,2] is NA)
data(pine)
ypine <- pine[,11]
data(XpineNAX21)
PLS_glm_wvc(dataY=ypine,dataX=XpineNAX21,nt=10,modele="pls-glm-gaussian")
rm("XpineNAX21","ypine")

data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
PLS_glm_wvc(ypine,Xpine,10,modele="pls", verbose=FALSE)
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-Gamma", verbose=FALSE)
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-family",family=Gamma(), verbose=FALSE)
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-gaussian", verbose=FALSE)
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-family",family=gaussian(log), verbose=FALSE)
PLS_glm_wvc(round(ypine),Xpine,10,modele="pls-glm-poisson", verbose=FALSE)
PLS_glm_wvc(round(ypine),Xpine,10,modele="pls-glm-family",family=poisson(log), verbose=FALSE)
rm(list=c("pine","ypine","Xpine"))


data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_glm_wvc(yCornell,XCornell,10,modele="pls-glm-inverse.gaussian", verbose=FALSE)
PLS_glm_wvc(yCornell,XCornell,10,modele="pls-glm-family",
family=inverse.gaussian(), verbose=FALSE)
rm(list=c("XCornell","yCornell"))


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

data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
PLS_glm(yaze_compl,Xaze_compl,10,modele="pls-glm-logistic",typeVC="none", verbose=FALSE)$InfCrit
PLS_glm_wvc(yaze_compl,Xaze_compl,10,modele="pls-glm-logistic", keepcoeffs=TRUE, verbose=FALSE)
rm("Xaze_compl","yaze_compl")
# }