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  • $\begingroup$ Hi Dave, thanks again for your time. The protocol I am working in already provides a way in which the AUC is extracted with 'true positive' and 'false positive' on the respective axis. I guess I am just struggling to combine those three AUCs that I have. $\endgroup$ Commented Sep 23, 2021 at 16:46
  • $\begingroup$ If all you have is the plot and no ability to extract the sensitivity and specificity values, I think you're stuck unless you want to go through and examine by hand/eyeball what the coordinates are of the points on your three ROC curves. However, I would be flabbergasted if your software didn't give the points needed to construct the ROC curves the way that pROC::roc does. // If you have the probability values predicted by your model, you can construct the ROC curves yourself, either in pROC or by hand, picking a sequence of thresholds like seq(0, 1, 0.001). $\endgroup$ Commented Sep 23, 2021 at 20:34
  • $\begingroup$ If I have the curves, is it possible just to combine them in ggplot2? Is that a thing? $\endgroup$ Commented Sep 23, 2021 at 20:56
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    $\begingroup$ Do you mean that you have graphs that you can see or that you have the points that make up those curves (vectors, data frames, lists...something like that)? I doubt ggplot2 can combine multiple graphs made in a different package if you lack the raw data. // Why not use software like pROC that can do what you want to do? $\endgroup$ Commented Sep 23, 2021 at 20:57
  • $\begingroup$ Yeah, pROC seems like the optimal plan. But tbh, I just have three sets of commands which print out an AUC curve for me, for three models. It seems unecessarily difficult to combine these in pROC somehow with the code I have unfortunately. Yet, I know it can be done, because the authors routinely evaluate their models against others with multiple AUC plots $\endgroup$ Commented Sep 23, 2021 at 21:01