Compute 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 specificity 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

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: Michel.Ballings@UGent.be

References

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

See Also

sensitivity, specificity, accuracy, roc, auc, plot

Examples

data(churn)

auc(sensitivity(churn$predictions,churn$labels))

auc(specificity(churn$predictions,churn$labels))

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

auc(roc(churn$predictions,churn$labels))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(AUC)
AUC 0.3.0
Type AUCNews() to see the change log and ?AUC to get an overview.
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AUC/auc.Rd_%03d_medium.png", width=480, height=480)
> ### Name: auc
> ### Title: Compute the area under the curve of a given performance measure.
> ### Aliases: auc
> 
> ### ** Examples
> 
> data(churn)
> 
> auc(sensitivity(churn$predictions,churn$labels))
[1] 0.8026259
> 
> auc(specificity(churn$predictions,churn$labels))
[1] 0.4591936
> 
> auc(accuracy(churn$predictions,churn$labels))
[1] 0.5034279
> 
> auc(roc(churn$predictions,churn$labels))
[1] 0.8439201
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>