planarPlot-methods {MLInterfaces} | R Documentation |
show the classification boundaries on the plane dictated by two genes in an exprSet
library(ALL) data(ALL) # # restrict to BCR/ABL or NEG # bio <- which( ALL$mol.biol %in% c("BCR/ABL", "NEG")) # # restrict to B-cell # isb <- grep("^B", as.character(ALL$BT)) kp <- intersect(bio,isb) all2 <- ALL[,kp] # # sample 2 genes at random # set.seed(1234) ng <- nrow(exprs(all2)) pick <- sample(1:ng, size=2, replace=FALSE) library(hgu95av2) gg <- all2[pick,] sym <- unlist(mget(geneNames(gg), hgu95av2SYMBOL)) geneNames(gg) <- sym class <- as.character(all2$mol.biol) gg@phenoData@pData$class <- factor(class) cl1 <- which( class == "NEG" ) cl2 <- which( class != "NEG" ) # # create balanced training sample # trainInds <- c( sample(cl1, size=floor(length(cl1)/2) ), sample(cl2, size=floor(length(cl2)/2)) ) # # run rpart # tgg <- rpartB( gg, "class", trainInds, minsplit=4 ) opar <- par(no.readonly=TRUE) par(mfrow=c(2,2)) planarPlot( tgg, gg, "class" ) title("rpart") # # run nnet # ngg <- nnetB( gg, "class", trainInds, size=8 ) planarPlot( ngg, gg, "class" ) title("nnet") # # run knn # kgg <- knnB( gg, "class", trainInds, k=3, l=1 ) planarPlot( kgg, gg, "class" ) title("3-nn") # # run svm # sgg <- svmB( gg, "class", trainInds ) planarPlot( sgg, gg, "class" ) title("svm") par(opar)