# [R] ROCR: auc and logarithm plot

Tobias Sing tobias.sing at gmail.com
Tue May 12 11:54:00 CEST 2009

```> 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 <- auc1 at y.values[[2]]
> "

If you have only one set of predictions and matching class labels, it
would be in auc1 at y.values[[1]].
If you have multiple sets (as from cross-validation or bootstrapping),
the AUCs would be in auc1 at y.values[[1]], auc1 at y.values[[2]], etc.
You can collect all of them for example by unlist(perf at 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))

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')