ROCR: visualizing classifier performance in R

By Tobias Sing, Oliver Sander, Niko Beerenwinkel, Thomas Lengauer. Bioinform., 2005

3,113 citations recorded by the source corpus.

UNLABELLED ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R's powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage. AVAILABILITY http://rocr.bioinf.mpi-sb.mpg.de. ROCR can be used under the terms of the GNU General Public License. Running within R, it is platform-independent. CONTACT tobias.sing@mpi-sb.mpg.de.

Subfields: Artificial Intelligence, Biophysics

View Paper: https://www.semanticscholar.org/paper/140e28b488407e4c52bd3b201c44f1f3e693d241

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Citations in the corpus

Receiver operating characteristic curve: overview and practical use for clinicians (2022, methodology): “Unlike commercial software packages, the R program is a free, open-source software that includes all the functions for ROC curve analyses using packages such as ROCR [31], pROC [32], and OptimalCutpoints [22].”
Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults Implications for Primary Prevention (2019, methodology): “Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: visualizing classifier performance in R. Bioinformatics 2005;21:3940–1.”
An alignment confidence score capturing robustness to guide tree uncertainty. (2010, methodology): “A receiver operating characteristic (ROC) analysis (Green and Swets 1966; Fawcett 2006) was conducted using the R package ROCR (Sing et al. 2005) to evaluate the speciﬁcity and sensitivity of the GUIDANCE conﬁdence measure.”
PresenceAbsence: An R Package for Presence Absence Analysis (2008, methodology): “These include the packages ROCR (Sing et al. 2005) and the ROC (Carey and Redestig 2007), as well as the roc.area and roc.plot functions from veriﬁcation package (NCAR - Research Application Program 2007), the colAUC function from the caTools package (Tuszynski 2007), the ROC function from the Epi…”
CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model (2013, methodology): “The receiver operating characteristic (ROC) curve and precision–recall (PR) curve were generated using ROCR package (24).”
cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R (2020, methodology): “0-11 (Sing et al. 2005) and a custom function using roc from pROC 1.”
SWAN: Subset-quantile Within Array Normalization for Illumina Infinium HumanMethylation450 BeadChips (2012, methodology): “Additionally, for the ROC analysis, which was performed using the ROCR package [31], true negatives were defined as those CpGs found to have an absolute difference in b value <0.”
Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines (2014, methodology): “In the original study, receiver operating characteristic (ROC) curve [28,29] analysis reported an area under the curve (AUC) of 0.”
The long and winding road: identifying pig domestication through molar size and shape (2013, methodology): “Morphometric and statistical analyses were performed in R v 2.13.1 (R Development Core Team, 2011) using “Rmorph” (Baylac, 2012); the “ROCR” package (Sing et al., 2005) and newly designed functions (available under request).”