R help - Mar 2011 - use of ROCR package (ROC curve / AUC value) in a specific case versus integral calculation

Hello,

I would like to use the ROCR package to draw ROC curves and compute AUC 
values.

However, in the specific context of my application, the true positive 
rates and false positive rates are already provided by some upstream method.

Of course, I can draw a ROC plot with the following command :

plot(x=FPrate, y=TPrate, "o", xlab="false positive rate",
ylab="true
positive rate", xlim=c(0, 1), ylim=c(0, 1)

but this will bot compute the AUC value.

There are two possibilities :
Either it is possible to use the above parameters - FPrate and TPrate 
vectors- to run the performance function and I would like to know how,


or it is not possible and I have to compute the area under the curve but 
I cannot find on the Web how to perform this, through an R package, 
using the two vectors above, if possible (I would rather not implement 
an integration algorithm).

I thank you in advance for your answer.

Best regards,

Christine Sinoquet

Christine SINOQUET wrote:
> Hello,
>
> I would like to use the ROCR package to draw ROC curves and compute AUC
> values.
>
> However, in the specific context of my application, the true positive
> rates and false positive rates are already provided by some upstream
> method.
>
> Of course, I can draw a ROC plot with the following command :
>
> plot(x=FPrate, y=TPrate, "o", xlab="false positive
rate", ylab="true
> positive rate", xlim=c(0, 1), ylim=c(0, 1)
>
> but this will bot compute the AUC value.
>
> There are two possibilities :
> Either it is possible to use the above parameters - FPrate and TPrate
> vectors- to run the performance function and I would like to know how,
>
>
> or it is not possible and I have to compute the area under the curve but
> I cannot find on the Web how to perform this, through an R package,
> using the two vectors above, if possible (I would rather not implement
> an integration algorithm).
>
> I thank you in advance for your answer.
My answer doesn't use R...

For some applications, when the classifier performance is quite good,
integrating the ROC curve
isn't a good idea, as the curve is very abrupt.

Another approach is to use the fact that the (1-AUC) represents the probability
of taking at random
one positive and one negative event and don't geting  them in the correct
order (by their score).
This is related to the Wilcoxon distribution.

So, if you have a set of negative and positive events and the "scores"
assigned to them, you can
sort them by their scores and enumerate the number of couples out of order you
can get.

Hope this help.

Jos?-Marcio


-- 
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  Jose Marcio MARTINS DA CRUZ           http://j-chkmail.ensmp.fr
  Ecole des Mines de Paris
  60, bd Saint Michel                      75272 - PARIS CEDEX 06
  mailto:Jose-Marcio.Martins at mines-paristech.fr