I am using ROC curves and full AUC values to compare different models, using simulated data. Now I think I am confused with the interpretations of ROC curves and AUC values. Please see the figure below (sorry it is partial from screen shots...)
There are three models compared, and I know that the model shown in green should preform best of all. However, as you can see, the green curve is superior to the other two before the FPR reaching around 0.2. This cut-off of 0.2 is quite interesting: it is the percentage of differentially expressed genes that I specify in my simulation (i.e. 20% of the observations are simulated to be positives).
My concern are:
given that people in reality will seldom choose a FPR cut-off of 0.5 or higher, why people would prefer a ROC curve with FPR ranging from 0 to 1 and use the full AUC value (i.e. calculate the entire area under the ROC curve) instead of just reporting the area made from, say, 0 to 0.25 or to 0.5? Is that called "partial AUC"?
in the figure below, what can we say about the performances of the three models? The AUC values are: green (0.805), red (0.815), blue (0.768). The red curve turns out to be superior, but as you see, the superiority is only reflected after FPR > 0.2. Thanks :)