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

## Description

Performs a weighted Partial Least Square gaussian regression.

## Usage

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

## Arguments

 `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.

## Details

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.

## Value

 `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.

## Author(s)

Caroline Bazzoli, Thomas Bouleau, Sophie Lambert-Lacroix

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ```#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)),newX)%*%a.coefficients ```

lsplsGlm documentation built on May 2, 2019, 12:36 p.m.