plr: Calculate the positive or negative likelihood ratio

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

Calculate the positive or negative likelihood ratio

Description

Calculate the positive or negative likelihood ratio from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length.

plr = tpr / fpr
nlr = fnr / tnr

Usage

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

nlr(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(), false_omission_rate(), metric_constrain(), misclassification_cost(), npv(), odds_ratio(), p_chisquared(), 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

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

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