# Calculating AUPR in R [closed]

It is easy to find a package calculating area under ROC, but is there a package that calculates the area under precision-recall curve?

• ROCR, pROC - are really nice! Sep 5, 2011 at 15:37
• They certainly are, yet AFAIK neither can calculate the area under precision-recall curve.
– user88
Sep 5, 2011 at 16:57

Assuming you already have a vector of probabilities (called probs) computed with your model and the true class labels are in your data frame as df$label (0 and 1) this code should work: install.packages("PRROC") require(PRROC) fg <- probs[df$$label == 1] bg <- probs[df$$label == 0] # ROC Curve roc <- roc.curve(scores.class0 = fg, scores.class1 = bg, curve = T) plot(roc) # PR Curve pr <- pr.curve(scores.class0 = fg, scores.class1 = bg, curve = T) plot(pr)  PS: The only disconcerting thing is you use scores.class0 = fg when fg is computed for label 1 and not 0. Here are the example ROC and PR curves with the areas under them: The bars on the right are the threshold probabilities at which a point on the curve is obtained. Note that for a random classifier, ROC AUC will be close to 0.5 irrespective of the class imbalance. However, the PR AUC is tricky (see What is "baseline" in precision recall curve). • Pls refer to the PRROC documentation linked in the answer above. – arun Oct 12, 2018 at 9:34 AUPRC() is a function in the PerfMeas package which is much better than the pr.curve() function in PRROC package when the data is very large. pr.curve() is a nightmare and takes forever to finish when you have vectors with millions of entries. PerfMeas takes seconds in comparison. PRROC is written in R and PerfMeas is written in C. A little googling returns one bioc package, qpgraph (qpPrecisionRecall), and a cran one, minet (auc.pr). I have no experience with them, though. Both have been devised to deal with biological networks. • This minet looked nice, but it needs to have some external adapter to make appropriate input from general data :-( – user88 May 8, 2011 at 9:09 Once you've got a precision recall curve from qpPrecisionRecall, e.g.: pr <- qpPrecisionRecall(measurements, goldstandard)  you can calculate its AUC by doing this: f <- approxfun(pr[, 1:2]) auc <- integrate(f, 0, 1)$value

the help page of qpPrecisionRecall gives you details on what data structure expects in its arguments.