Loop to plot multiple ROC curves in one unique plot using ROCR - r

Question

I am using ROCR package to generate ROC curves. I already have a loop to generate multiple ROC plots from multiple files. I have 30 files. But I would like to combine all 30 ROC curves in one plot (different colors if possible). I know that there are few posts about it, but I would like to use my own loop and modify it. Any suggestions?
My script:
library(ROCR)
labels <- read.table(file="c:/data1/input")
files <- list.files(path="c:/data2/", pattern="*.txt")
for(i in files){
predictions <- read.table(paste("c:/data2/",i,sep=""))
pred <- prediction(predictions, labels)
perf <- performance(pred,"tpr","fpr")
pdf(paste0("c:/data2/", i,"ROC.pdf"))
plot(perf)
dev.off()
}

Answer 1

When you call plot() on a prediction object, it generates a new plot. One way is to use lines() and to call the values directly. First I generate 30 models, since you did not provide your data:
library(gbm)
library(mlbench)
library(colorspace)
data(DNA)
Pal = qualitative_hcl(30)
dat = DNA[,-c(1:2)]
dat$Class = as.numeric(dat$Class == "ei")
idx = split(sample(nrow(dat)),1:nrow(dat) %% 30)
mdl = lapply(1:30,function(i){
mod = gbm(Class~.,data=dat[idx[[i]],])
predictions = predict(mod,dat[-idx[[i]],],n.trees=10,type="response")
labels = dat[-idx[[i]],"Class"]
list(labels=labels,predictions=predictions)
})
names(mdl) = paste("model",1:30)
Now we start a empty plot, iterate through the 30 models, essentially the part you need is lines(...) :
plot(NULL,xlim=c(0,1),ylim=c(0,1),
xlab="False positive rate",ylab="True positive rate")
for(i in 1:30){
predictions = mdl[[i]]$predictions
labels = mdl[[i]]$labels
pred = prediction(predictions, labels)
perf <- performance(pred,"tpr","fpr")
lines(perf#x.values[[1]],perf#y.values[[1]],col=Pal[i])
}
legend("bottomright",fill=Pal,names(mdl),ncol=5,cex=0.7)
For your example trying something like this:
files <- list.files(path="c:/data2/", pattern="*.txt")
mdl = lapply(files,read.table)
names(mdl) = gubs(".txt","",files)
plot(NULL,xlim=c(0,1),ylim=c(0,1),
xlab="False positive rate",ylab="True positive rate")
for(i in 1:30){
pred = prediction(mdl[[i]], labels)
perf <- performance(pred,"tpr","fpr")
lines(perf#x.values[[1]],perf#y.values[[1]],col=Pal[i])
}
legend("bottomright",fill=Pal,names(mdl),ncol=5,cex=0.7)