PRROC-package: Compute and plot PR and ROC curves and the areas under the...

In PRROC: Precision-Recall and ROC Curves for Weighted and Unweighted Data

Description

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.

Details

Package:	PRROC
Type:	Package
Version:	1.3
Date:	2017-04-21
License:	GPL-3

Author(s)

Jan Grau and Jens Keilwagen

Maintainer: Jan Grau <[e-mail]>

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

# 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);

Example output

  Precision-recall curve

    Area under curve (Integral):
     0.7351774 

    Area under curve (Davis & Goadrich):
     0.735175 

    Curve not computed ( can be done by using curve=TRUE )

  ROC curve

    Area under curve:
     0.740395 

    Curve not computed ( can be done by using curve=TRUE )

PRROC documentation built on May 1, 2019, 9:19 p.m.