pls: Weighted PLS gaussian regression
In lsplsGlm: Classification using LS-PLS for Logistic Regression

Description Usage Arguments Details Value Author(s) Examples

Performs a weighted Partial Least Square gaussian regression.

1	pls(Y,X,W = diag(rep(1, length(Y))),ncomp)

`Y`	a vector of length `n` giving the classes of the `n` observations. `Y` contains continuous values.
`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.
`W`	weight matrix, if `W` is the identity matrix then the function will perform a standard PLS regression.
`ncomp`	a positive integer. `ncomp` is the number of PLS components.

This function performs a weighted PLS gaussian regression. It takes as input a vector of response, a data matrix about genes, a number of component and a weight matrix. If weight matrix is the identity matrix then it performs a standard PLS regression.

`coefficients`	an array of regression coefficients (`(p+1)xncomp`).
`projection`	the projection matrix, used to convert `X` to scores.
`scores`	the scores matrix `(nxncomp)` of PLS regression.
`intercept`	the constant of the model.

Caroline Bazzoli, Thomas Bouleau, Sophie Lambert-Lacroix

#X simulation
meanX<-sample(1:300,50)
sdeX<-sample(50:150,50)
X<-matrix(nrow=60,ncol=50)
for (i in 1:50){
  X[,i]<-rnorm(60,meanX[i],sdeX[i])
}

#Y simulation
Y<-rnorm(60,30,10)

# Learning sample
index<-sample(1:length(Y),round(2*length(Y)/3))
XL<-X[index,]
YL<-Y[index]

#fit the model
fit<-pls(Y=YL,X=XL,ncomp=3)

#Testing sample
newX=X[-index,]

#predictions with the constant of the model
a.coefficients<-rbind(fit$intercept,fit$coefficients)

#predictions
newY=cbind(rep(1,dim(newX)[1]),newX)%*%a.coefficients