specificity: Calculate the specificity for a given logit model

In glossa: User-Friendly 'shiny' App for Bayesian Species Distribution Models

Calculate the specificity for a given logit model

Description

Calculate the specificity for a given logit model

Usage

specificity(actuals, predictedScores, threshold = 0.5)

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 predictions, convert it to a numeric vector of 1's and 0's.
threshold	If predicted value is above the threshold, it will be considered as an event (1), else it will be a non-event (0). Defaults to 0.5.

Details

This function was obtained from the InformationValue R package (https://github.com/selva86/InformationValue).

Value

The specificity of the given binary response actuals and predicted probability scores, which is, the number of observations without the event AND predicted to not have the event divided by the number of observations without the event.

glossa documentation built on June 8, 2025, 1:20 p.m.