Concordance: Concordance

In InformationValue: Performance Analysis and Companion Functions for Binary Classification Models

Description

Calculate concordance and discordance percentages for a logit model

Usage

Concordance(actuals, predictedScores)

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.

Details

Calculate the percentage of concordant and discordant pairs for a given logit model.

Value

a list containing percentage of concordant pairs, percentage discordant pairs, percentage ties and No. of pairs.

• Concordance The total proportion of pairs in concordance. A pair is said to be concordant when the predicted score of 'Good' (Event) is greater than that of the 'Bad'(Non-event)

• Discordance The total proportion of pairs that are discordant.

• Tied The proportion of pairs for which scores are tied.

• Pairs The total possible combinations of 'Good-Bad' pairs based on actual response (1/0) labels.

Author(s)

Selva Prabhakaran [e-mail]

Examples

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

Example output

$Concordance
[1] 0.8730796

$Discordance
[1] 0.1269204

$Tied
[1] 0

$Pairs
[1] 7225

InformationValue documentation built on May 1, 2019, 9:12 p.m.