ROCR: Visualizing the Performance of Scoring Classifiers
Version 1.0-7

ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parameterized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parameterization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters.

Browse man pages Browse package API and functions Browse package files

AuthorTobias Sing, Oliver Sander, Niko Beerenwinkel, Thomas Lengauer
Date of publication2015-03-26 17:12:17
MaintainerTobias Sing <tobias.sing@gmail.com>
LicenseGPL (>= 2)
Version1.0-7
URL http://rocr.bioinf.mpi-sb.mpg.de/
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("ROCR")

Man pages

performance: Function to create performance objects
performance-class: Class "performance"
plot-methods: Plot method for performance objects
prediction: Function to create prediction objects
prediction-class: Class "prediction"
ROCR.hiv: Data set: Support vector machines and neural networks applied...
ROCR.simple: Data set: Simple artificial prediction data for use with ROCR
ROCR.xval: Data set: Artificial cross-validation data for use with ROCR

Functions

ROCR.hiv Man page
ROCR.simple Man page
ROCR.xval Man page
combine.performance.objects Source code
compute.unnormalized.roc.curve Source code
construct.linefunct Source code
define.environments Source code
downsample Source code
farg Source code
garg Source code
get.arglist Source code
ntersection.point Source code
performance Man page Source code
performance-class Man page
performance.accuracy Source code
performance.auc Source code
performance.calibration.error Source code
performance.chisq Source code
performance.cost Source code
performance.error.rate Source code
performance.expected.cost Source code
performance.f Source code
performance.false.negative.rate Source code
performance.false.positive.rate Source code
performance.lift Source code
performance.mean.cross.entropy Source code
performance.mutual.information Source code
performance.negative.predictive.value Source code
performance.odds.ratio Source code
performance.phi Source code
performance.plot.canvas Source code
performance.plot.horizontal.avg Source code
performance.plot.no.avg Source code
performance.plot.threshold.avg Source code
performance.plot.vertical.avg Source code
performance.positive.predictive.value Source code
performance.precision.recall.break.even.point Source code
performance.prediction.conditioned.fallout Source code
performance.prediction.conditioned.miss Source code
performance.rate.of.negative.predictions Source code
performance.rate.of.positive.predictions Source code
performance.rocconvexhull Source code
performance.root.mean.squared.error Source code
performance.sar Source code
performance.true.negative.rate Source code
performance.true.positive.rate Source code
plot,performance,missing-method Man page
plot,performance-method Man page
plot-methods Man page
plot.performance Man page Source code
prediction Man page Source code
prediction-class Man page
sarg Source code
select.args Source code
select.prefix Source code
slice.run Source code

Files

inst
inst/CITATION
NAMESPACE
demo
demo/00Index
demo/ROCR.R
INSTALL
NEWS
data
data/ROCR.simple.rda
data/ROCR.xval.rda
data/datalist
data/ROCR.hiv.rda
R
R/performance_measures.R
R/ROCR_aux.R
R/prediction.R
R/performance_plots.R
R/zzz.R
R/performance.R
MD5
README
DESCRIPTION
man
man/ROCR.simple.Rd
man/plot-methods.Rd
man/prediction.Rd
man/performance.Rd
man/performance-class.Rd
man/prediction-class.Rd
man/ROCR.xval.Rd
man/ROCR.hiv.Rd
tools
tools/README.unittests
tools/unittests
tools/unittests/XXXrunit.ROCR.aux.RXXX
tools/unittests/runit.aux.r
tools/unittests/testsuite.ROCR.R
tools/unittests/runit.consistency.r
tools/unittests/runit.simple.r
ROCR documentation built on May 19, 2017, 7:33 a.m.