plot.AUC: Plot the sensitivity, specificity, accuracy and roc curves.

In AUC: Threshold Independent Performance Measures for Probabilistic Classifiers

Plot 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

x	an object produced by one of the functions sensitivity, specificity, accuracy, or roc
y	Not used.
...	Arguments to be passed to methods, such as graphical parameters. See ?plot
type	Type of plot. Default is line plot.
add	Logical. If TRUE the curve is added to an existing plot. If FALSE a new plot is created.
min	a numeric value between 0 and 1, denoting the cutoff that defines the start of the area under the curve
max	a 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: [e-mail]

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)

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

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

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

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

AUC documentation built on April 5, 2022, 1:15 a.m.