fit.lspcr.glm: Fitting a LS-PCR model for logistic regression
In lsplsGlm: Classification using LS-PLS for Logistic Regression

Description Usage Arguments Details Value Author(s) See Also Examples

Fits a model the combination of two methods: Ordinary Least Square (OLS) and Principal Component Regression (PCR) to fit both clinical and gene expression data.

1	fit.lspcr.glm(Y,X,D,ncomp)

`Y`	a vector of length `n` giving the classes of the `n` observations. The classes must be coded as 1 or 0.
`X`	a data matrix (`nxp`) of genes. NAs and Inf are not allowed. Each row corresponds to an observation and each column to a gene.
`D`	a data matrix (`nxq`) of clinical data. NAs and Inf are not allowed. Each row corresponds to an observation and each column to a clinical variable.
`ncomp`	a positive integer. `ncomp` is the number of selected components.

This function combines two methods, the first one is the Principal Components Regression on genetic data to reduce the dimension using prcomp from {stats} package. The second one is the logistic regression on the concatenation of the ncomp first selected axes and clinical data (D) to explain Y. To do that we use glm from {stats} package.

`coefficients`	coefficients of logistic regression.
`cvg`	the 0-1 value indicating convergence of the algorithm (1 for convergence, 0 otherwise).
`projection`	projection matrix used to convert `X` to scores.

Caroline Bazzoli, Thomas Bouleau, Sophie Lambert-Lacroix

cv.lspcr.glm,predict.lspcr.glm.

 
#Data
data(BreastCancer)
#Vector of response
Y<-BreastCancer$Y
#Genetic data
X<-BreastCancer$X
#Clinical data
D<-BreastCancer$D
#Apply fit.lspcr.glm with ncomp=5 using the 76 first patients
fit<-fit.lspcr.glm(Y=Y[1:76],X=X[1:76,],D=D[1:76,],ncomp=5)

#using projection to predict class of 2 last patients
newX<-X[77:78,]
newD<-D[77:78,]

#New Score matrix
newScores<-newX%*%fit$projection

#prediction
newEta=cbind(rep(1,dim(newD)[1]),newD,newScores)%*%fit$coefficients
newPi=1/(1+exp(-newEta))
newY=as.numeric(newEta>0)