R/plsrf_xz.r

In MAclinical: Class prediction based on microarray data and clinical parameters

###    Class prediction based on PLS dimension reduction (without pre-validation) and random forests with microarray data and clinical parameters
###
### Copyright 2007-11 Anne-Laure Boulesteix 
###
### 
###
###
### This file is part of the `MAclinical' 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


plsrf_xz<-function(Xlearn,Zlearn,Ylearn,Xtest,Ztest,ncomp=0:3,ordered=NULL,nbgene=NULL,...)
{
Ylearn<-as.numeric(factor(Ylearn))-1
nlearn<-length(Ylearn)
p<-ncol(Xlearn)

if (is.null(ordered)&is.null(nbgene))
 {
 ordered<-1:p
 }

if (is.null(ordered)&!is.null(nbgene))
 {
 ordered<-order(abs(studentt.stat(X=Xlearn,L=Ylearn+1)),decreasing=TRUE)
 }

if (is.null(nbgene))
 {
 nbgene<-p
 }


output.pls<-pls.regression(Xlearn[,ordered[1:nbgene]],Ylearn+1,ncomp=max(ncomp))
XXlearn<-scale(Xlearn[,ordered[1:nbgene]],scale=FALSE,center=output.pls$meanX)%*%output.pls$R[,1:max(ncomp)]
XXtest<-matrix(scale(Xtest[,ordered[1:nbgene]],scale=FALSE,center=output.pls$meanX)%*%output.pls$R[,1:max(ncomp)],nrow=nrow(Xtest))
output.forest<-list()
OOB<-numeric(length(ncomp))


for (i in 1:length(ncomp))
  {
  if (ncomp[i]==0)
   {
   data.learn<-data.frame(Zlearn,y=factor(Ylearn))
   data.test<-data.frame(Ztest)
   names(data.learn)<-c(sapply(as.list(1:ncol(Zlearn)),FUN="paste",".Z"),"y")
   names(data.test)<-sapply(as.list(1:ncol(Zlearn)),FUN="paste",".Z")
   }
  else
   {
   data.learn<-data.frame(Zlearn,XXlearn[,1:ncomp[i]],y=factor(Ylearn))
   data.test<-data.frame(Ztest,XXtest[,1:ncomp[i]])
   names(data.learn)<-c(sapply(as.list(1:ncol(Zlearn)),FUN="paste",".Z"),sapply(as.list(1:ncomp[i]),FUN=paste,".comp"),"y")
   names(data.test)<-c(sapply(as.list(1:ncol(Zlearn)),FUN="paste",".Z"),sapply(as.list(1:ncomp[i]),FUN=paste,".comp"))
   }  

  output.forest[[i]]<-cforest(formula=y~.,data=data.learn,controls=cforest_control(ntree=200,mincriterion=qnorm(0.5),mtry=floor(sqrt(ncol(data.learn)-1)),replace=FALSE))
  OOB[i]<-sum(predict(output.forest[[i]],OOB=TRUE)!=Ylearn)/nlearn
  }

best<-which.min(OOB)
bestncomp<-ncomp[best]
output.forest<-output.forest[[best]]
importance<-varimp(output.forest)

prediction<-as.numeric(predict(object=output.forest,newdata=data.test[,1:(ncol(Zlearn)+bestncomp)]))-1

return(list(prediction=prediction,importance=importance,bestncomp=bestncomp,OOB=OOB))
}