false_omission_rate: Calculate the false omission and false discovery rate

In cutpointr: Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks

Calculate the false omission and false discovery rate

Description

Calculate the false omission rate or false discovery rate from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length.

false_omission_rate = fn / (tn + fn) = 1 - npv false_discovery_rate = fp / (tp + fp) = 1 - ppv

Usage

false_omission_rate(tp, fp, tn, fn, ...)

false_discovery_rate(tp, fp, tn, fn, ...)

Arguments

tp	(numeric) number of true positives.
fp	(numeric) number of false positives.
tn	(numeric) number of true negatives.
fn	(numeric) number of false negatives.
...	for capturing additional arguments passed by method.

See Also

Other metric functions: F1_score(), Jaccard(), abs_d_ppv_npv(), abs_d_sens_spec(), accuracy(), cohens_kappa(), cutpoint(), metric_constrain(), misclassification_cost(), npv(), odds_ratio(), p_chisquared(), plr(), ppv(), precision(), prod_ppv_npv(), prod_sens_spec(), recall(), risk_ratio(), roc01(), sensitivity(), specificity(), sum_ppv_npv(), sum_sens_spec(), total_utility(), tpr(), tp(), youden()

Examples

false_omission_rate(10, 5, 20, 10)
false_omission_rate(c(10, 8), c(5, 7), c(20, 12), c(10, 18))

cutpointr documentation built on April 14, 2022, 1:06 a.m.