Calculate discrimination and calibration metrics

Calculate discrimination and calibration metrics

Usage

calculate_classifier_metrics(scores_pos, scores_neg)

Arguments

scores_pos

Numeric suitability scores at presence points. NA is dropped.

scores_neg

Numeric suitability scores at background or absence points. NA is dropped.

Value

A named list: `cbi`, `auc_roc`, `auc_prg`, `tss`, `ba` and `cor`, each a single number. Scores need only be on a common scale within one call, since every metric except `cor` depends on the ranking alone. When either class is empty the list is filled with the no-skill values.

Examples

set.seed(1)
presences <- rbeta(50, 4, 2)
absences  <- rbeta(200, 2, 4)
str(calculate_classifier_metrics(presences, absences))
#> List of 6
#>  $ cbi    : num 0.988
#>  $ auc_roc: num 0.895
#>  $ auc_prg: num 0.908
#>  $ tss    : num 0.64
#>  $ ba     : num 0.82
#>  $ cor    : num 0.59