R/plsrf_x.r

Defines functions plsrf_x

Documented in plsrf_x

###    Class prediction based on PLS dimension reduction (without pre-validation) and random forests 
### 	with microarray data only
###
### 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_x<-function(Xlearn,Zlearn=NULL,Ylearn,Xtest,Ztest=NULL,ncomp=0:3,ordered=NULL,nbgene=NULL,...)
{
ncomp<-ncomp[ncomp!=0]
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))
 {
 data.learn<-data.frame(XXlearn[,1:ncomp[i]],y=factor(Ylearn))
 data.test<-data.frame(XXtest[,1:ncomp[i]])
 names(data.learn)<-c(sapply(as.list(1:ncomp[i]),FUN=paste,".comp"),"y")
 names(data.test)<-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)

newdata<-data.frame(data.test[,1:bestncomp])
names(newdata)<-sapply(as.list(1:bestncomp),FUN=paste,".comp")

prediction<-as.numeric(predict(object=output.forest,newdata=newdata))-1

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

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MAclinical documentation built on May 2, 2019, 9:30 a.m.