A new update of pROC is now available on CRAN: version 1.10.0.

ggplot2 support (Experimental)

A new function was introduced: ggroc. Given a roc object, or a (optionally named) list of roc objects, it returns a ggplot object, that can then be printed, with optional aesthetics, themes etc. Here is a basic example:

library(pROC)
# Create a basic roc object
data(aSAH)
rocobj <- roc(aSAH$outcome, aSAH$s100b)
rocobj2 <- roc(aSAH$outcome, aSAH$wfns)

library(ggplot2)
# Multiple curves:
gg2 <- ggroc(list(s100b=rocobj, wfns=rocobj2))
gg2

Basic ggplot with two ROC curves.

The usual ggplot syntax applies, so you can add themes, labels, etc. Note the aes argument, which control the aesthetics for geom_line to map to the different ROC curves supplied. Here we use "linetype" instead of the default color:

# with additional aesthetics:
gg2b <- ggroc(list(s100b=rocobj, wfns=rocobj2), aes="linetype", color="red")
# You can then your own theme, etc.
gg2b + theme_minimal() + ggtitle("My ROC curve")

Basic ggplot with two ROC curves.

This functionality is currently experimental and subject to change. Please report bugs and feedback on pROC's GitHub issue tracker.

Precision and recall in coords

The coords function supports two new ret values: "precision" and "recall":

library(pROC)
# Create a basic roc object
data(aSAH)
rocobj <- roc(aSAH$outcome, aSAH$s100b)
coords(rocobj, "best", ret = c("threshold", "sensitivity", "specificity", "precision", "recall"))
  threshold sensitivity specificity   precision      recall 
  0.2050000   0.6341463   0.8055556   0.6500000   0.6341463

It makes it very easy to get a Precision-Recall (PR) plot:

plot(precision ~ recall, t(coords(rocobj, "all", ret = c("recall", "precision"))), type="l", main = "PR plot of S100B")

A simple PR plot.

Automated testing

Several functions are now covered with tests (powered by the testthat package) to ensure correct behavior. This allowed me to find and fix a few glitches. It will also make it easier to refactor code in the future.

The tests are automatically run by R CMD check. Additional tests that are too slow to be enabled by defauld can be activated with the RUN_SLOW_TESTS environment variable.

export RUN_SLOW_TESTS=true
R CMD check pROC

Test results can be seen on Travis CI, and the coverage of the tests can be seen on Codecov. Currently 30% of the code is tested. This includes most functionality, with the exception of bootstrapping and smoothing which I plan to implement in the future.

Obtaining the update

To update your installation, simply type:

install.packages("pROC")

Here is the full changelog:

• Basic ggplot2 support (one and multiple ROC curves)
• Implement precision and recall for coords
• Fix: properly handle NAs in cases when passing cases/controls to roc (thanks Thomas König for the report)
• Fix various minor bugs detected with new unit tests