sensitivity: Calculate the sensitivity for a given logit model

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

Calculate the sensitivity for a given logit model

Description

Calculate the sensitivity for a given logit model

Usage

sensitivity(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 predcitions, 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 sensitivity of the given binary response actuals and predicted probability scores, which is, the number of observations with the event AND predicted to have the event divided by the nummber of observations with the event.

glossa documentation built on Oct. 15, 2024, 5:08 p.m.