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:.
cvAUC: Cross-Validated Area Under the ROC Curve Confidence
17-Jan-2022 tions AUC and cvAUC
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 ProbabilisticClassifiers
Version0.3.2
Date2022-04-04
AuthorMichel Ballings and Dirk Van den Poel
MaintainerMichel BallingsLicenseGPL (>= 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Index11
12AUC-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 ProbabilisticClassifcation 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. ValueA list containing the following elements:
cutoffsA numeric vector of threshold values measureA numeric vector of accuracy values corresponding to the threshold valuesAuthor(s)
Authors: Michel Ballings and Dirk Van den Poel, Maintainer:References
Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for ProbabilisticClassifcation 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 curveAuthor(s)
Authors: Michel Ballings and Dirk Van den Poel, Maintainer:References
Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for ProbabilisticClassifcation Algorithms, Forthcoming.
See Also
Examples
data(churn)AUCNews5
auc(roc(churn$predictions,churn$labels))AUCNewsDisplay the NEWS fileDescriptionAUCNewsshows the NEWS file of the AUC package.
UsageAUCNews()
ValueNone.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 ProbabilisticClassifcation 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 curveAuthor(s)
Authors: Michel Ballings and Dirk Van den Poel, Maintainer:References
Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for ProbabilisticClassifcation Algorithms, Forthcoming.
See Also
Examples
data(churn) roc7plot(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}. ValueA 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 valuesAuthor(s)
Authors: Michel Ballings and Dirk Van den Poel, Maintainer:References
Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for ProbabilisticClassifcation 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. ValueA list containing the following elements:
cutoffsA numeric vector of threshold values measureA numeric vector of sensitivity values corresponding to the threshold valuesAuthor(s)
Authors: Michel Ballings and Dirk Van den Poel, Maintainer:References
Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for ProbabilisticClassifcation Algorithms, Forthcoming.
See Also
specificity9Examples
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. ValueA list containing the following elements:
cutoffsA numeric vector of threshold values measureA numeric vector of specificity values corresponding to the threshold valuesAuthor(s)
Authors: Michel Ballings and Dirk Van den Poel, Maintainer:References
Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for ProbabilisticClassifcation Algorithms, Forthcoming.
See Also
10specificity
Examples
data(churn) Index datasets churn,5 accuracy,2,3 ,3 ,4,6-9AUC(AUC-package),2
auc,2-4,4 ,6-9AUC-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[PDF] Première connexion ? Base Elèves Premier Degré
[PDF] 1ere utilisation d une clé OTP - Lyon
[PDF] arena - palais des sports du pays d 'aix - SPLA PAYS D 'AIX
[PDF] aréna du pays d 'aix - Mairie d 'Aix-en-Provence
[PDF] https://extranetac-grenoblefr/arena
[PDF] plan d 'accès /access - Arena
[PDF] Mode opératoire - Académie de Toulouse
[PDF] Déclaration des services d enseignement - UFR ALLSH
[PDF] Cours pyramide et cône de révolution _prof
[PDF] Les différents types d 'arguments - Sciences Po
[PDF] Le port de l uniforme ? l école : débat Document Enseignant - Défis 71
[PDF] Le module, les arguments, l 'exponentielle imaginaire et leurs
[PDF] LES TECHNIQUES D ARGUMENTATION Il est - GEA IUT Evreux
[PDF] L 'argument de valeur dans le discours politique