######################################### ### Anne-Laure Boulesteix, 11.12.2009 ### ######################################### library(e1071) library(limma) library(randomForest) library(CMA) ########################### ### Load prostate data #### ########################### load("prostata.RData") Y<-as.numeric(prostate[,1])-1 X<-prostate[,-1] X<-as.matrix(X) ############################ ###### learning sets ####### ############################ set.seed(111) lset <- GenerateLearningsets(y=Y, method = "CV", fold=5, strat =TRUE,niter=1) ########################## ########## niter ######### ########################## niter<-20 ###################################### ########## Defining matrices ######### ###################################### methodnames<-c("knn1nb20","knn1nb50","knn1nb100","knn1nb200","knn1nb500", "knn3nb20","knn3nb50","knn3nb100","knn3nb200","knn3nb500", "knn5nb20","knn5nb50","knn5nb100","knn5nb200","knn5nb500", "ldanb10","ldanb20", "fdanb10","fdanb20", "dldanb20","dldanb50","dldanb100","dldanb200","dldanb500", "plslda2nb20","plslda2nb50","plslda2nb100","plslda2nb200","plslda2nb500", "plslda3nb20","plslda3nb50","plslda3nb100","plslda3nb200","plslda3nb500", "nnetnb20","nnetnb50","nnetnb100","nnetnb200","nnetnb500" ) methodnames_all<-c("pam","L2","svm","rfp","rf2p","rf3p","rf4p") prostateCV_t<-matrix(0,39,niter) prostateCV_w<-matrix(0,39,niter) prostateCV_l<-matrix(0,39,niter) prostateCV_all<-matrix(0,7,niter) rownames(prostateCV_t)<-methodnames rownames(prostateCV_w)<-methodnames rownames(prostateCV_l)<-methodnames rownames(prostateCV_all)<-methodnames_all ####################################### ############ Main analysis ############ ####################################### permute<-function(y,my.seed) { y<-as.factor(y) set.seed(my.seed) n<-tapply(y,y,length) n01<-round(n[1]/sum(n)*n[2]) n00<-n[1]-n01 n11<-n[2]-n01 n10<-n[1]-n00 newy<-numeric(sum(n)) newy[y==levels(y)[1]][sample(n[1],n01)]<-1 newy[y==levels(y)[2]][sample(n[2],n11)]<-1 return(factor(newy)) } for (i in 1:20) { y<-permute(Y,i) #################################################### ##### t-test as preliminary variable selection ##### #################################################### selttest<-GeneSelection(X, y, learningsets = lset, method="t.test") j<-1 ###### knn k=1 ###### prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel = selttest, classifier = knnCMA, k = 1, nbgene = 20))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 1, nbgene = 50))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 1, nbgene = 100))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 1, nbgene = 200))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 1, nbgene = 500))@score) ### knn k=3 ### j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel = selttest, classifier = knnCMA, k = 3, nbgene = 20))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 3, nbgene = 50))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 3, nbgene = 100))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 3, nbgene = 200))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 3, nbgene = 500))@score) ### knn k=5 ### j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel = selttest, classifier = knnCMA, k = 5, nbgene = 20))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 5, nbgene = 50))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 5, nbgene = 100))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 5, nbgene = 200))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = knnCMA, k = 5, nbgene = 500))@score) ### lda ### j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = ldaCMA, nbgene = 10))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = ldaCMA, nbgene = 20))@score) ### fda ### j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = fdaCMA, nbgene = 10))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = fdaCMA, nbgene = 20))@score) ### dlda ### j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = dldaCMA, nbgene = 20))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = dldaCMA, nbgene = 50))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = dldaCMA, nbgene = 100))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = dldaCMA, nbgene = 200))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = dldaCMA, nbgene = 500))@score) ### pls+lda ncomp=2 ### j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = pls_ldaCMA, comp=2, nbgene = 20))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = pls_ldaCMA, comp=2, nbgene = 50))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = pls_ldaCMA, comp=2, nbgene = 100))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = pls_ldaCMA, comp=2, nbgene = 200))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = pls_ldaCMA, comp=2, nbgene = 500))@score) ### pls+lda ncomp=3 ### j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = pls_ldaCMA, comp=3, nbgene = 20))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = pls_ldaCMA, comp=3, nbgene = 50))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = pls_ldaCMA, comp=3, nbgene = 100))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = pls_ldaCMA, comp=3, nbgene = 200))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = pls_ldaCMA, comp=3, nbgene = 500))@score) ### nnet ### j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = nnetCMA, nbgene=20))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = nnetCMA, nbgene=50))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = nnetCMA, nbgene=100))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = nnetCMA, nbgene=200))@score) j<-j+1 prostateCV_t[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selttest, classifier = nnetCMA, nbgene=500))@score) ########################################################### ##### Wilcoxon-test as preliminary variable selection ##### ########################################################### selwtest<-GeneSelection(X, y, learningsets = lset, method="wilcox.test") ###### KNN ############ ### knn k=1 ### j<-1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel = selwtest, classifier = knnCMA, k = 1, nbgene = 20))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 1, nbgene = 50))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 1, nbgene = 100))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 1, nbgene = 200))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 1, nbgene = 500))@score) ### knn k=3 ### j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel = selwtest, classifier = knnCMA, k = 3, nbgene = 20))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 3, nbgene = 50))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 3, nbgene = 100))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 3, nbgene = 200))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 3, nbgene = 500))@score) ### knn k=5 ### j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel = selwtest, classifier = knnCMA, k = 5, nbgene = 20))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 5, nbgene = 50))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 5, nbgene = 100))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 5, nbgene = 200))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = knnCMA, k = 5, nbgene = 500))@score) ### fda ### j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = ldaCMA, nbgene = 10))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = ldaCMA, nbgene = 20))@score) ### lda ### j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = fdaCMA, nbgene = 10))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = fdaCMA, nbgene = 20))@score) ### dlda ### j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = dldaCMA, nbgene = 20))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = dldaCMA, nbgene = 50))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = dldaCMA, nbgene = 100))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = dldaCMA, nbgene = 200))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = dldaCMA, nbgene = 500))@score) ### pls+lda ncomp=2 ### j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = pls_ldaCMA, comp=2, nbgene = 20))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = pls_ldaCMA, comp=2, nbgene = 50))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = pls_ldaCMA, comp=2, nbgene = 100))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = pls_ldaCMA, comp=2, nbgene = 200))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = pls_ldaCMA, comp=2, nbgene = 500))@score) ### pls+lda ncomp=3 ### j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = pls_ldaCMA, comp=3, nbgene = 20))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = pls_ldaCMA, comp=3, nbgene = 50))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = pls_ldaCMA, comp=3, nbgene = 100))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = pls_ldaCMA, comp=3, nbgene = 200))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = pls_ldaCMA, comp=3, nbgene = 500))@score) ### neural networks ### j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = nnetCMA, nbgene=20))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = nnetCMA, nbgene=50))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = nnetCMA, nbgene=100))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = nnetCMA, nbgene=200))@score) j<-j+1 prostateCV_w[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=selwtest, classifier = nnetCMA, nbgene=500))@score) #################################################### ##### limma as preliminary variable selection ###### #################################################### sellimma<-GeneSelection(X, y, learningsets = lset, method="limma") ###### KNN ############ ### knn k=1 ### j<-1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel = sellimma, classifier = knnCMA, k = 1, nbgene = 20))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 1, nbgene = 50))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 1, nbgene = 100))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 1, nbgene = 200))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 1, nbgene = 500))@score) ### knn k=3 ### j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel = sellimma, classifier = knnCMA, k = 3, nbgene = 20))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 3, nbgene = 50))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 3, nbgene = 100))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 3, nbgene = 200))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 3, nbgene = 500))@score) ### knn k=5 ### j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel = sellimma, classifier = knnCMA, k = 5, nbgene = 20))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 5, nbgene = 50))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 5, nbgene = 100))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 5, nbgene = 200))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = knnCMA, k = 5, nbgene = 500))@score) ### lda ### j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = ldaCMA, nbgene = 10))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = ldaCMA, nbgene = 20))@score) ### fda ### j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = fdaCMA, nbgene = 10))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = fdaCMA, nbgene = 20))@score) ### dlda ### j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = dldaCMA, nbgene = 20))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = dldaCMA, nbgene = 50))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = dldaCMA, nbgene = 100))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = dldaCMA, nbgene = 200))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = dldaCMA, nbgene = 500))@score) ### pls+lda ncomp=2 ### j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = pls_ldaCMA, comp=2, nbgene = 20))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = pls_ldaCMA, comp=2, nbgene = 50))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = pls_ldaCMA, comp=2, nbgene = 100))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = pls_ldaCMA, comp=2, nbgene = 200))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = pls_ldaCMA, comp=2, nbgene = 500))@score) ### pls+lda ncomp=3 ### j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = pls_ldaCMA, comp=3, nbgene = 20))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = pls_ldaCMA, comp=3, nbgene = 50))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = pls_ldaCMA, comp=3, nbgene = 100))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = pls_ldaCMA, comp=3, nbgene = 200))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = pls_ldaCMA, comp=3, nbgene = 500))@score) ### neural networks ### j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = nnetCMA, nbgene=20))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = nnetCMA, nbgene=50))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = nnetCMA, nbgene=100))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = nnetCMA, nbgene=200))@score) j<-j+1 prostateCV_l[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, genesel=sellimma, classifier = nnetCMA, nbgene=500))@score) ################################ ## Without variable selection ## ################################ j<-1 ### PAM ### tunescda<-tune(X, y, learningsets = lset, classifier = scdaCMA) prostateCV_all[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, tuneres=tunescda,classifier = scdaCMA))@score) ### PLR ### j<-j+1 tuneplr<-tune(X, y, learningsets = lset, classifier = plrCMA) prostateCV_all[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, tuneres=tuneplr,classifier = plrCMA))@score) ### svm linear ### j<-j+1 tunesvm<-tune(X, y, learningsets = lset, classifier = svmCMA) prostateCV_all[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, tuneres=tunesvm,classifier = svmCMA))@score) ### rf sqrt(p) ### j<-j+1 p<-ncol(X) prostateCV_all[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, classifier = rfCMA,mtry=sqrt(p),ntree=1000))@score) ### rf 2sqrt(p) ### j<-j+1 prostateCV_all[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, classifier = rfCMA,mtry=2*sqrt(p),ntree=1000))@score) ### rf 3sqrt(p) ### j<-j+1 prostateCV_all[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, classifier = rfCMA,mtry=3*sqrt(p),ntree=1000))@score) ### rf 4sqrt(p) ### j<-j+1 prostateCV_all[j,i] <- mean(evaluation(classification(X, y, learningsets = lset, classifier = rfCMA,mtry=4*sqrt(p),ntree=1000))@score) save(prostateCV_all,file="prostateCV_all.RData") save(prostateCV_t,file="prostateCV_t.RData") save(prostateCV_w,file="prostateCV_w.RData") save(prostateCV_l,file="prostateCV_l.RData") }