plotROC: plotROC

In selva86/roc: Performance analysis and companion functions such as AUROC, IV, WOE calculation for binary classification models (logistic, discriminant etc)

Description

Plot the Receiver Operating Characteristics(ROC) Curve based on ggplot2

Usage

plotROC(actuals, predictedScores, Show.labels = F, returnSensitivityMat = F)

Arguments

actuals	The actual binary flags for the response variable. It can take a numeric vector containing values of either 1 or 0, where 1 represents the 'Good' or 'Events' while 0 represents 'Bad' or 'Non-Events'.
predictedScores	The prediction probability scores for each observation. If your classification model gives the 1/0 predcitions, convert it to a numeric vector of 1's and 0's.
Show.labels	Whether the probability scores should be printed at change points?. Defaults to False.
returnSensitivityMat	Whether the sensitivity matrix (a dataframe) should be returned. Defaults to FALSE.

Details

For a given actuals and predicted probability scores, A ROC curve is plotted using the ggplot2 framework along the the area under the curve.

Value

Plots the ROC curve

Author(s)

Selva Prabhakaran [e-mail]

Examples

data('ActualsAndScores')
plotROC(actuals=ActualsAndScores$Actuals, predictedScores=ActualsAndScores$PredictedScores)

selva86/roc documentation built on May 29, 2019, 5:55 p.m.