Hi! First I want to say thanks for the material that you sent to me, it was of great help! And now I have anothers questions: reading the material, I figured out that my classifier is a discrete one, so the ROC curve for it is just a point. Is ROCR able to plot this point for me? Or ROCR just works for scoring classifiers? And besides, a confusion matrix could be a better way to evaluate this discrete classifier than a ROC point? I'm sorry for doing so many questions at a time, but my individual search is not getting me much far.
Thanks for the help, Regina Beretta Mazaro. > Date: Thu, 19 Mar 2009 10:14:00 +0100 > Subject: Re: [R] Prediction-class ROCR > From: tobias.s...@gmail.com > To: rberet...@hotmail.com > CC: r-help@r-project.org > > Regina, > > to get a simple ROC curve, use the following sequence of commands: > pred <- prediction(predictions, labels) > perf <- performance(pred, "tpr", "fpr") > plot(perf) > In the first line, 'predictions' are the raw predictions (usually > numerical) of your classifier, and labels (as you correctly guessed) > the true (binary) classes of your items. The true positive and false > positive rates _at various cutoffs_ are then calculated from the raw > predictions. The purpose of ROCR is to obtain these (and other) rates > --- if you already have them, I don't understand from your email what > else you want. > > Just in case you are uncertain about the overall framework of > classification, take a look at this tutorial: > Fawcett, T. (2003): ROC graphs: Notes and practical considerations > for data mining researchers > http://www.hpl.hp.com/techreports/2003/HPL-2003-4.pdf > > The following slide deck also contains a brief introduction of the > framework, as well as usage examples of ROCR: > http://rocr.bioinf.mpi-sb.mpg.de/ROCR_Talk_Tobias_Sing.ppt > > Hope that helps, > Tobias > > > > On Thu, Mar 19, 2009 at 3:01 AM, Regina Beretta Mazaro > <rberet...@hotmail.com> wrote: > > > > > > > > > > Hi, > > > > I'm involved in a bioinformatics project at my university, and we're doing > > a comparison paper between some methods of classification of nc-RNA. I've > > been encharged of ploting the ROC curves' graphs. But I'm new on working > > with R and I'm having some difficulty with the prediction-class. I don't > > get where the values of ROCR.simple$predictions, for example, came from > > ($labels I understand that represents the real classisfication of that > > item). And I just have the values for true positive, false positive, true > > negative and false positive, obtained from the methods tests. So, I can't > > plot a graph with my own values. How can I convert these values that I have > > into $predictions-type needed to run ROCR? Is there any function that does > > this? Or I have to redo the tests using another kind of measuring? If > > someone could help me, I'll be very grateful. > > Regina Beretta Mazaro. > > _________________________________________________________________ > > > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > 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. > > _________________________________________________________________ [[alternative HTML version deleted]] ______________________________________________ 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.
