optimalCutoff: Compute the optimal probability cutoff score

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

Compute the optimal probability cutoff score

Description

Compute the optimal probability cutoff score

Usage

optimalCutoff(
  actuals,
  predictedScores,
  optimiseFor = "misclasserror",
  returnDiagnostics = FALSE
)

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.
optimiseFor	The maximization criterion for which probability cutoff score needs to be optimised. Can take either of following values: "Ones" or "Zeros" or "Both" or "misclasserror"(default). If "Ones" is used, 'optimalCutoff' will be chosen to maximise detection of "One's". If 'Both' is specified, the probability cut-off that gives maximum Youden's Index is chosen. If 'misclasserror' is specified, the probability cut-off that gives minimum mis-classification error is chosen.
returnDiagnostics	If TRUE, would return additional diagnostics such as 'sensitivityTable', 'misclassificationError', 'TPR', 'FPR' and 'specificity' for the chosen cut-off.

Details

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

Value

The optimal probability score cutoff that maximises a given criterion. If 'returnDiagnostics' is TRUE, then the following items are returned in a list:

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