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AUC: Threshold Independent Performance Measures for

Encoding UTF-8. NeedsCompilation no. Repository CRAN. Date/Publication 2022-04-04 15:40:17 UTC. R topics documented: AUC-package .



pROC: Display and Analyze ROC Curves

03-Sept-2021 Confidence intervals can be computed for (p)AUC or ROC curves. ... “pROC: an open-source package for R and S+ to analyze and.



NonCompart: Noncompartmental Analysis for Pharmacokinetic Data

16-Jul-2022 URL https://cran.r-project.org/package=NonCompart ... either of "Linear" or "Log" to indicate the way to calculate AUC and AUMC. Details.



Package timeROC

18-Dec-2019 Package 'timeROC'. December 18 2019. Type Package. Title Time-Dependent ROC Curve and AUC for Censored Survival Data. Version 0.4.



Package pkr

4) Interval(partial) AUCs with 'linear' or 'log' interpolation method. * Reference: Gabrielsson J Weiner D. URL https://cran.r-project.org/package=pkr.



survAUC: Estimators of Prediction Accuracy for Time-to-Event Data

20-May-2022 time-dependent true/false positive rates and AUC curves from a ... R topics documented: AUC.cd ... is not implemented in R package survAUC.



MESS: Miscellaneous Esoteric Statistical Scripts

Package 'MESS'. June 20 2022 R topics documented: auc . ... For area under a spline interpolation



discAUC: Linear and Non-Linear AUC for Discounting Data

02-Jun-2021 Package 'discAUC' ... The package can calculate all three versions of AUC [and includes a new version: IHS(AUC)] ... R topics documented:.





Package auRoc

License GPL. NeedsCompilation no. LazyData true. Repository CRAN. Date/Publication 2020-04-04 00:30:08 UTC. R topics documented: auc.nonpara.kernel .



Package AUC - The Comprehensive R Archive Network

AUC-package Threshold independent performance measures for probabilistic classi- fiers Summary and plotting functions for threshold independent performance measures for probabilistic classifiers This package includes functions to compute the area under the curve (function ) of selected mea- auc

Package 'AUC"

October 12, 2022

TypePackage

TitleThreshold Independent Performance Measures for Probabilistic

Classifiers

Version0.3.2

Date2022-04-04

AuthorMichel Ballings and Dirk Van den Poel

MaintainerMichel Ballings DescriptionVarious functions to compute the area under the curve of selected measures: The area un- der the sensitivity curve (AUSEC), the area under the specificity curve (AUSPC), the area un- der the accuracy curve (AUACC), and the area under the receiver operating characteris- tic curve (AUROC). Support for visualization and partial areas is included.

LicenseGPL (>= 2)

ByteCompiletrue

RoxygenNote7.1.2

EncodingUTF-8

NeedsCompilationno

RepositoryCRAN

Date/Publication2022-04-04 15:40:17 UTC

Rtopics documented:

AUC-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 auc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 AUCNews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 churn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 plot.AUC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 roc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 specificity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Index11

1

2AUC-packageAUC-packageThreshold independent performance measures for probabilistic classi-

fiers.Description Summary and plotting functions for threshold independent performance measures for probabilistic classifiers.

Details

This package includes functions to compute the area under the curve (functionauc) of selected mea- sures: The area under the sensitivity curve (AUSEC) (functionsensitivity), the area under the specificity curve (AUSPC) (functionspecificity), the area under the accuracy curve (AUACC) (functionaccuracy), and the area under the receiver operating characteristic curve (AUROC) (func- tionroc). The curves can also be visualized using the functionplot. Support for partial areas is provided. Auxiliary code in this package is adapted from theROCRpackage. The measures available in this package are not available in the ROCR package or vice versa (except for the AUROC). As for the AUROC, we adapted theROCRcode to increase computational speed (so it can be used more effectively in objective functions). As a result less funtionality is offered (e.g., averaging cross validation runs). Please use theROCRpackage for that purposes.

Author(s)

Michel Ballings and Dirk Van den Poel, Maintainer:

References

Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for Probabilistic

Classifcation Algorithms, Forthcoming.

See Also

Examples

data(churn) auc(roc(churn$predictions,churn$labels)) accuracy3 plot(roc(churn$predictions,churn$labels))accuracyCompute the accuracy curve.Description This function computes the accuracy curve required for theaucfunction and theplotfunction. Usage accuracy(predictions, labels, perc.rank = TRUE)

Arguments

predictionsA numeric vector of classification probabilities (confidences, scores) of the pos- itive event. labelsA factor of observed class labels (responses) with the only allowed values {0,1}. perc.rankA logical. If TRUE (default) the percentile rank of the predictions is used. Value

A list containing the following elements:

cutoffsA numeric vector of threshold values measureA numeric vector of accuracy values corresponding to the threshold values

Author(s)

Authors: Michel Ballings and Dirk Van den Poel, Maintainer:

References

Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for Probabilistic

Classifcation Algorithms, Forthcoming.

See Also

Examples

data(churn) accuracy(churn$predictions,churn$labels)

4aucaucCompute the area under the curve of a given performance measure.Description

This function computes the area under the sensitivity curve (AUSEC), the area under the speci- ficity curve (AUSPC), the area under the accuracy curve (AUACC), or the area under the receiver operating characteristic curve (AUROC). Usage auc(x, min = 0, max = 1)

Arguments

orroc mina numeric value between 0 and 1, denoting the cutoff that defines the start of the area under the curve maxa numeric value between 0 and 1, denoting the cutoff that defines the end of the area under the curve Value A numeric value between zero and one denoting the area under the curve

Author(s)

Authors: Michel Ballings and Dirk Van den Poel, Maintainer:

References

Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for Probabilistic

Classifcation Algorithms, Forthcoming.

See Also

Examples

data(churn)

AUCNews5

auc(roc(churn$predictions,churn$labels))AUCNewsDisplay the NEWS fileDescription

AUCNewsshows the NEWS file of the AUC package.

Usage

AUCNews()

Value

None.churnChurn dataDescription

churncontains three variables: the churn predictions (probabilities) of two models, and observed churn Usage data(churn)

Format

A data frame with 1302 observations, and 3 variables:predictions,predictions2,churn.

Author(s)

Authors: Michel Ballings and Dirk Van den Poel, Maintainer:

References

Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for Probabilistic

Classifcation Algorithms, Forthcoming.

Examples

data(churn) str(churn)

6plot.AUCplot.AUCPlot the sensitivity, specificity, accuracy and roc curves.Description

This function plots the (partial) sensitivity, specificity, accuracy and roc curves. Usage ## S3 method for class?AUC? plot(x, y = NULL, ..., type = "l", add = FALSE, min = 0, max = 1)

Arguments

orroc yNot used. ...Arguments to be passed to methods, such as graphical parameters. See ?plot typeType of plot. Default is line plot. addLogical. If TRUE the curve is added to an existing plot. If FALSE a new plot is created. mina numeric value between 0 and 1, denoting the cutoff that defines the start of the area under the curve maxa numeric value between 0 and 1, denoting the cutoff that defines the end of the area under the curve

Author(s)

Authors: Michel Ballings and Dirk Van den Poel, Maintainer:

References

Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for Probabilistic

Classifcation Algorithms, Forthcoming.

See Also

Examples

data(churn) roc7

plot(roc(churn$predictions,churn$labels))rocCompute the receiver operating characteristic (ROC) curve.Description

This function computes the receiver operating characteristic (ROC) curve required for theaucfunc- tion and theplotfunction. Usage roc(predictions, labels)

Arguments

predictionsA numeric vector of classification probabilities (confidences, scores) of the pos- itive event. labelsA factor of observed class labels (responses) with the only allowed values {0,1}. Value

A list containing the following elements:

cutoffsA numeric vector of threshold values fprA numeric vector of false positive rates corresponding to the threshold values tprA numeric vector of true positive rates corresponding to the threshold values

Author(s)

Authors: Michel Ballings and Dirk Van den Poel, Maintainer:

References

Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for Probabilistic

Classifcation Algorithms, Forthcoming.

See Also

8sensitivity

Examples

data(churn) roc(churn$predictions,churn$labels)sensitivityCompute the sensitivity curve.Description This function computes the sensitivity curve required for theaucfunction and theplotfunction. Usage sensitivity(predictions, labels, perc.rank = TRUE)

Arguments

predictionsA numeric vector of classification probabilities (confidences, scores) of the pos- itive event. labelsA factor of observed class labels (responses) with the only allowed values {0,1}. perc.rankA logical. If TRUE (default) the percentile rank of the predictions is used. Value

A list containing the following elements:

cutoffsA numeric vector of threshold values measureA numeric vector of sensitivity values corresponding to the threshold values

Author(s)

Authors: Michel Ballings and Dirk Van den Poel, Maintainer:

References

Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for Probabilistic

Classifcation Algorithms, Forthcoming.

See Also

specificity9

Examples

data(churn) sensitivity(churn$predictions,churn$labels)specificityCompute the specificity curve.Description This function computes the specificity curve required for theaucfunction and theplotfunction. Usage specificity(predictions, labels, perc.rank = TRUE)

Arguments

predictionsA numeric vector of classification probabilities (confidences, scores) of the pos- itive event. labelsA factor of observed class labels (responses) with the only allowed values {0,1}. perc.rankA logical. If TRUE (default) the percentile rank of the predictions is used. Value

A list containing the following elements:

cutoffsA numeric vector of threshold values measureA numeric vector of specificity values corresponding to the threshold values

Author(s)

Authors: Michel Ballings and Dirk Van den Poel, Maintainer:

References

Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for Probabilistic

Classifcation Algorithms, Forthcoming.

See Also

10specificity

Examples

data(churn) Index datasets churn,5 accuracy,2,3 ,3 ,4,6-9

AUC(AUC-package),2

auc,2-4,4 ,6-9

AUC-package,2

AUCNews,5

churn,5 plot,2-4,6-9 plot.AUC,6 roc,2-4,6,7 ,7 ,8,9 sensitivity,2-4,6-8,8 ,9 specificity,2-4,6-9,9 11quotesdbs_dbs22.pdfusesText_28
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