I am doing some ROC analysis in Matlab. The function perfcurve is excellent. However, it assumes that larger values of the score indicate stronger evidence for a positive state. This is often exactly what you want if you are analysing the output of a classifier model.
In my case, I want to use raw data where smaller values indicate stronger evidence for a positive state. Reading through the documentation, I could not find a way to change the test direction for the perfcurve function.
Does anyone know if it is possible to do this? Or other similar functions that allow doing this?
Why don't you simply run your analysis on the dummy variable Y, defined as Y=-X+max(X)
Otherwise, the AUC you are looking for is 1-AUC coming from perfcurve
Francesco - Yes, using Y=-X+max(X) [or even just Y=-X] does the trick. But I was wondering if perfcurve is able to do this by itself, without the need to manipulate the inputs, or the outputs.
Peng - If I change the value of posclass I can get the correct value for AUC, but the values for the ROC plot are (somewhat) swapped around. Of course, this can be fixed manually, but again I don't find it ideal.
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