I like ROCs – they allow assessing (for example) the quality of a test without needing to decide on the threshold between "normal" and "pathologic" (yet). They even boil it down to a single number: AUC (area-under-curve). They are known since World War II, where they were developed for Radar interpretation, and Swets wrote a pertinent seminal paper in 1973. In 1993 I used them for analysis but did not place them into the paper – strange in hindsight –, rather showed a combined sensitivity-specificity figure. It took me until 2001 to publish ROC curves (for Pattern-ERG) to detect glaucoma. I've applied ROCs often since, and always programmed the algorithm myself (initially in Excel, then Igor Pro, and in the last few years in R). Until I found this paper “pROC: an open-source package for R and S+ to analyze and compare ROC curves” by Xavier Robin et al.

http://dx.doi.org/10.1186/1471-2105-12-77

They also offer “pROC” as an R package on the standard R site, great, thanks! One of its many advantages are confidence intervals for the AUCs, significance tests between several discriminators, and confidence areas around the traces – my own bootstrapping took much longer than theirs. If you want to follow the bootstrapping in the console, select something like the following option:

options(pROCProgress=list(name="text", width=NA, char=".", style=3))