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ROCR

An R package for visualizing classifier performance (Sing/Sander/Beerenwinkel/Lengauer [2005] Bioinformatics)

https://github.com/ipa-tys/rocr

Science Score: 23.0%

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
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    DOI references
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    Academic publication links
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    Committers with academic emails
    2 of 9 committers (22.2%) from academic institutions
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    Scientific vocabulary similarity
    Low similarity (13.4%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

An R package for visualizing classifier performance (Sing/Sander/Beerenwinkel/Lengauer [2005] Bioinformatics)

Basic Info
Statistics
  • Stars: 39
  • Watchers: 2
  • Forks: 11
  • Open Issues: 3
  • Releases: 0
Created about 14 years ago · Last pushed over 2 years ago
Metadata Files
Readme

README.md

ROCR

CRAN Status codecov <!-- badges: end -->

visualizing classifier performance in R, with only 3 commands

Please support our work by citing the ROCR article in your publications:

Sing T, Sander O, Beerenwinkel N, Lengauer T. [2005] ROCR: visualizing classifier performance in R. Bioinformatics 21(20):3940-1.

Free full text: http://bioinformatics.oxfordjournals.org/content/21/20/3940.full


ROCR was originally developed at the Max Planck Institute for Informatics

Introduction

ROCR (with obvious pronounciation) is an R package for evaluating and visualizing classifier performance. It is...

  • ...easy to use: adds only three new commands to R.
  • ...flexible: integrates tightly with R's built-in graphics facilities.
  • ...powerful: Currently, 28 performance measures are implemented, which can be freely combined to form parametric curves such as ROC curves, precision/recall curves, or lift curves. Many options such as curve averaging (for cross-validation or bootstrap), augmenting the averaged curves by standard error bar or boxplots, labeling cutoffs to the curve, or coloring curves according to cutoff.

Performance measures that ROCR knows:

Accuracy, error rate, true positive rate, false positive rate, true negative rate, false negative rate, sensitivity, specificity, recall, positive predictive value, negative predictive value, precision, fallout, miss, phi correlation coefficient, Matthews correlation coefficient, mutual information, chi square statistic, odds ratio, lift value, precision/recall F measure, ROC convex hull, area under the ROC curve, precision/recall break-even point, calibration error, mean cross-entropy, root mean squared error, SAR measure, expected cost, explicit cost.

ROCR features:

ROC curves, precision/recall plots, lift charts, cost curves, custom curves by freely selecting one performance measure for the x axis and one for the y axis, handling of data from cross-validation or bootstrapping, curve averaging (vertically, horizontally, or by threshold), standard error bars, box plots, curves that are color-coded by cutoff, printing threshold values on the curve, tight integration with Rs plotting facilities (making it easy to adjust plots or to combine multiple plots), fully customizable, easy to use (only 3 commands).

Installation of ROCR

The most straightforward way to install and use ROCR is to install it from CRAN by starting R and using the install.packages function:

install.packages("ROCR")

Alternatively you can install it from command line using the tar ball like this:

R CMD INSTALL ROCR_*.tar.gz

Getting started

from within R ...

library(ROCR) demo(ROCR) help(package=ROCR)

Examples

Using ROCR's 3 commands to produce a simple ROC plot:

pred <- prediction(predictions, labels) perf <- performance(pred, measure = "tpr", x.measure = "fpr") plot(perf, col=rainbow(10))

Documentation

  • The Reference Manual found here
  • Slide deck for a tutorial talk (feel free to re-use for teaching, but please give appropriate credits and write us an email) [PPT]
  • A few pointers to the literature on classifier evaluation

Contact

Questions, comments, and suggestions are very welcome. Open an issue on GitHub and we can discuss. We are also interested in seeing how ROCR is used in publications. Thus, if you have prepared a paper using ROCR we'd be happy to know.

Owner

  • Name: Tobias Sing
  • Login: ipa-tys
  • Kind: user

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 98
  • Total Committers: 9
  • Avg Commits per committer: 10.889
  • Development Distribution Score (DDS): 0.224
Past Year
  • Commits: 16
  • Committers: 1
  • Avg Commits per committer: 16.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
FelixErnst f****t@o****m 76
Tobias Sing l****e@g****m 8
Tobias Sing t****g@m****e 5
Tobias Sing t****g@g****m 4
Untom u****r@b****t 1
Mehera Emrich 7****h 1
tys t****s@u****n 1
Tobias Sing t****s@i****e 1
Billy Aung Myint r@u****m 1
Committer Domains (Top 20 + Academic)
use-r.com: 1 ipa.fhg.de: 1 ubuntu.ubuntu-domain: 1 bioinf.jku.at: 1 mpi-sb.mpg.de: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 13
  • Total pull requests: 4
  • Average time to close issues: 12 months
  • Average time to close pull requests: over 4 years
  • Total issue authors: 13
  • Total pull request authors: 3
  • Average comments per issue: 1.85
  • Average comments per pull request: 2.25
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Xdai8923 (1)
  • drdataking (1)
  • tjebo (1)
  • middeleast20 (1)
  • fuehrding (1)
  • olapuentesantana (1)
  • asantucci (1)
  • wiebkedrews (1)
  • higgi13425 (1)
  • CHENGHAO-WANG (1)
  • GautamBuddha (1)
  • eipi10 (1)
  • HywelMJ (1)
Pull Request Authors
  • untom (2)
  • meemrich (1)
  • burkovae (1)
Top Labels
Issue Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 59,128 last-month
  • Total docker downloads: 345,209
  • Total dependent packages: 93
  • Total dependent repositories: 400
  • Total versions: 8
  • Total maintainers: 1
cran.r-project.org: ROCR

Visualizing the Performance of Scoring Classifiers

  • Versions: 8
  • Dependent Packages: 93
  • Dependent Repositories: 400
  • Downloads: 59,128 Last month
  • Docker Downloads: 345,209
Rankings
Dependent repos count: 0.8%
Dependent packages count: 1.0%
Downloads: 1.7%
Forks count: 5.6%
Average: 5.8%
Stargazers count: 8.6%
Docker downloads count: 17.3%
Maintainers (1)
felix.gm.ernst@outlook.com
Last synced: 10 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.6 depends
  • gplots * imports
  • grDevices * imports
  • graphics * imports
  • methods * imports
  • stats * imports
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests
.github/workflows/R-CMD-check.yaml actions
  • actions/cache v1 composite
  • actions/checkout v2 composite
  • actions/upload-artifact master composite
  • r-lib/actions/setup-pandoc master composite
  • r-lib/actions/setup-r master composite
.github/workflows/pkgdown.yaml actions
  • actions/cache v1 composite
  • actions/checkout v2 composite
  • r-lib/actions/setup-pandoc master composite
  • r-lib/actions/setup-r master composite