

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.sing@gmail.com
> To: rberettam@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
> <rberettam@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.
> > _________________________________________________________________
> >
> >
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> >
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> > and provide commented, minimal, self-contained, reproducible code.
> >

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