getFprTpr: Compute specificity and sensitivity

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

Compute specificity and sensitivity

Description

Compute specificity and sensitivity

Usage

getFprTpr(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

A list with two elements: fpr (false positive rate) and tpr (true positive rate).

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