# Compute and plot PR and ROC curves and the areas under the curves for weighted and unweighted data This package computes the areas under the precision-recall (PR) and receiver operating characteristics (ROC) curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. package ## Details ||| |:--|:--| |Package:|PRROC| |Type:|Package| |Version:|1.3| |Date:|2017-04-21| |License:|[GPL-3](https://www.r-project.org/Licenses/GPL-3)| ## Author(s) Jan Grau and Jens Keilwagen Maintainer: Jan Grau ## References J. Davis and M. Goadrich. The relationship between precision-recall and ROC curves. In **Proceedings of the 23rd International Conference on Machine Learning**, pages 233--240, New York, NY, USA, 2006. ACM. T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters (27) 8, 861-874, 2006. J. Keilwagen, I. Grosse, and J. Grau. Area under precision-recall curves for weighted and unweighted data, PLOS ONE (9) 3, 2014. J. Grau, I. Grosse, and J. Keilwagen. PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R. Bioinformatics, 31(15):2595-2597, 2015. ## See Also `pr.curve` `roc.curve` `plot.PRROC` `print.PRROC` ## Examples ```r # create artificial scores as random numbers x <- rnorm( 1000 ); y <- rnorm( 1000, -1 ); # compute area under PR curve pr <- pr.curve( x, y ); print( pr ); # compute area under ROC curve roc <- roc.curve( x, y ); print( roc ); # compute PR curve and area under curve pr <- pr.curve( x, y, curve = TRUE ); # plot curve plot(pr); # compute ROC curve and area under curve roc <- roc.curve( x, y, curve = TRUE ); # plot curve plot(roc); # create artificial weights x.weights <- runif( 1000 ); y.weights <- runif( 1000 ); # compute PR curve and area under curve pr <- pr.curve( x, y, x.weights, y.weights, curve = TRUE ); # plot curve plot(pr); # compute ROC curve and area under curve roc <- roc.curve( x, y, x.weights, y.weights, curve = TRUE ); # plot curve plot(roc); ```

PRROC-package