# How to calculate a ROC curve? What kind of inputs do you need for that?

I have a microarray data which I ran through a continuous covariate (say "X"). I did this using 4 different methods.

For the results obtained from each of the 4 methods, I have the following from each of the 4:

1. p-value,
2. FDR significant value,
3. "Bonferonni significance" (saying "true" or "false")
4. "Holm significance" (saying "true" or "false")

I wish to obtain an ROC curve showing lines for sensitivity and specificity from each of the 4 methods.

I kind of know what is an ROC curve, but even after reading through a few links, I dont feel clear of the concept of what kind of input it needs to create it. So I apologise if this seems to be a dummy question.

So my questions are:

1. Can a ROC curve be created from the 4 values (p value, FDR, Bonferoni & Holm) which I have mentioned above? Or is it that I have to calculate the FP and FN first?

2. Can you suggest me an easy to use package which could do this for me, by giving me a ROC curve with lines of 4 methods using the input above?

Your help appreciated. Thanking you in advance.

• you need to be able to compute FP/FN that depends on a threshold (say a p-value), then just plot ROC by changing the threshold. If you only need the area under the ROC, take a look at Wilcoxon rank-sum test. Feb 5, 2013 at 14:07
• Welcome to cross validated! As your question is on the proper site now, have a look at the listed "Related" questions. Do they answer your question? You may also want to check the warnings about model comparison on the basis of ROC that have been written here many times. Feb 5, 2013 at 14:56

As for an R package, try ROCR; if you only want AUROC or just something simple (RORC may feel overwhelming and is in general pretty slow) yet less functional, use colAUC from caTools.