R/pls.lda.R

In plsgenomics: PLS Analyses for Genomics

### pls.lda.R  (2005-04-06)
###
###     Classification with PLS Dimension Reduction and Linear Discriminan Analysis
###
### Copyright 2004-04 Anne-Laure Boulesteix and Korbinian Strimmer
###
### 
###
###
### This file is part of the `plsgenomics' library for R and related languages.
### It is made available under the terms of the GNU General Public
### License, version 2, or at your option, any later version,
### incorporated herein by reference.
### 
### This program is distributed in the hope that it will be
### useful, but WITHOUT ANY WARRANTY; without even the implied
### warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
### PURPOSE.  See the GNU General Public License for more
### details.
### 
### You should have received a copy of the GNU General Public
### License along with this program; if not, write to the Free
### Software Foundation, Inc., 59 Temple Place - Suite 330, Boston,
### MA 02111-1307, USA

pls.lda<-function(Xtrain, Ytrain, Xtest=NULL, ncomp, nruncv=0, alpha=2/3, priors=NULL)
{
     ntrain<-nrow(Xtrain)
     Ytrain<-as.factor(Ytrain)
     
     if (is.vector(Xtest))
     {
          Xtest<-matrix(Xtest,1,length(Xtest))
     }
     if (is.null(Xtest))
     {
          Xtest<-Xtrain
     }
     if (nruncv==0&length(ncomp)>1) 
          stop("Since length(ncomp)>1, nruncv must be >0")
     
     if (nruncv>0)
     {
          ncomp<-pls.lda.cv(Xtrain,Ytrain,ncomp=ncomp,nruncv=nruncv,alpha=alpha,priors=priors)
     }
     
     pls.out<-pls.regression(Xtrain=Xtrain,Ytrain=transformy(Ytrain),Xtest=NULL,ncomp=ncomp)
     
     Ztrain<-as.data.frame(matrix(pls.out$T,ntrain,ncomp))
     Ztrain$y<-Ytrain
     Ztest<-as.data.frame(scale(Xtest,center=pls.out$meanX,scale=FALSE)%*%pls.out$R)
     if (is.null(priors))
     {
          lda.out <- lda(formula = y ~ ., data = Ztrain)
     }
     else
     {
          lda.out <- lda(formula = y ~ ., data = Ztrain, prior = priors)
     }
     
     
     pred.lda.out <- predict(object=lda.out,newdata=Ztest)
     predclass <- pred.lda.out$class
     
     return(list(predclass=predclass,ncomp=ncomp, pls.out=pls.out, lda.out=lda.out, pred.lda.out=pred.lda.out))
}

############################

transformy<-function(y)
{
     y<-as.numeric(y)
     K<-max(y)
     if (K>2)
     {
          Y<-matrix(0,length(y),K)
          for (k in 1:K)
          {
               Y[,k]<-as.numeric(y==k)
               Y[,k]<-Y[,k]-mean(Y[,k])
          }
     }
     else
     {
          Y<-matrix(y-mean(y),length(y),1)
     }
     
     Y
}