Hi.

Thnx a lot. I will try that.

nitin

On 1/24/07, Tobias Sing <tobias.sing@mpi-sb.mpg.de> wrote:
>
> Maybe ROCR might help you.
> You can visualize the prec/rec-trade-off across the range of all cutoffs:
> assuming your numerical predictions are in scores and the true class
> labels are in classes:
> pred <- prediction( scores, classes )
> perf <- performance(pred, 'rec','prec')
> plot(perf)
>
> HTH,
>   Tobias
>
> On 1/24/07, nitin jindal <nitin.jindal@gmail.com> wrote:
> > Hi,
> >
> > I am using logistic regression model named lrm(Design)
> >
> > Rite now I was using Area Under Curve (AUC) for testing my model. But,
> now I
> > have to calculate precision/recall of the model on test cases.
> > For lrm, precision and recal would be simply defined with the help of 2
> > terms below:
> > True Positive (TP) - Number of test cases where class 1 is given
> probability
> > >= 0.5.
> > False Negative (FP) - Number of test cases where class 0 is given
> > probability >= 0.5.
> >
> > Precision = TP / (TP + FP)
> > Recall = TP / ( Number of Positive Samples in test data)
> >
> > Any help is appreciated.
> >
> > I an write a long code with for loops and all, but is there any inbuild
> > function or just few commands that would do the task.
> >
> > regards,
> > Nitin
> >
> >        [[alternative HTML version deleted]]
> >
> > ______________________________________________
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> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
>
> --
> Tobias Sing
> Computational Biology and Applied Algorithmics
> Max Planck Institute for Informatics
> Saarbrucken, Germany
> Phone: +49 681 9325 315
> Fax: +49 681 9325 399
> http://www.tobiassing.net
>

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