> 1. I have tried to understand how to extract area-under-curve value by > looking at the ROCR document and googling. Still I am not sure if I am doing > the right thing. Here is my code, is "auc1" the auc value? > " > pred1 <- prediction(resp1,label1) > > perf1 <- performance(pred1,"tpr","fpr") > plot( perf1, type="l",col=1 ) > > auc1 <- performance(pred1,"auc") > auc1 <- a...@y.values[[2]] > "
If you have only one set of predictions and matching class labels, it would be in a...@y.values[[1]]. If you have multiple sets (as from cross-validation or bootstrapping), the AUCs would be in a...@y.values[[1]], a...@y.values[[2]], etc. You can collect all of them for example by unlist(p...@y.values). Btw, you can use str(auc1) to see the structure of objects. > 2. I have to compare two models that have very close ROCs. I'd like to have a > more distinguishable plot of the ROCs. So is it possible to have a logarithm > FP axis which might probably separate them well? Or zoom in the part close to > the leftup corner of ROC plot? Or any other ways to make the ROCs more > separate? To "zoom in" to a specific part: plot(perf1, xlim=c(0,0.2), ylim=c(0.7,1)) plot(perf2, add=TRUE, lty=2, col='red') If you want logarithmic axes (though I wouldn't personally do this for a ROC plot), you can set up an empty canvas and add ROC curves to it: plot(1,1, log='x', xlim=c(0.001,1), ylim=c(0,1), type='n') plot(perf, add=TRUE) You can adjust all components of the performance plots. See ?plot.performance and the examples in this slide deck: http://rocr.bioinf.mpi-sb.mpg.de/ROCR_Talk_Tobias_Sing.ppt Hope that helps, Tobias ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
