tpr: Calculate true / false positive / negative rate

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

Calculate true / false positive / negative rate

Description

Calculate the true positive rate (tpr, equal to sensitivity and recall), the false positive rate (fpr, equal to fall-out), the true negative rate (tnr, equal to specificity), or the false negative rate (fnr) from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length.

tpr = tp / (tp + fn)
fpr = fp / (fp + tn)
tnr = tn / (tn + fp)
fnr = fn / (fn + tp)

Usage

tpr(tp, fn, ...)

fpr(fp, tn, ...)

tnr(fp, tn, ...)

fnr(tp, fn, ...)

Arguments

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

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(), plr(), ppv(), precision(), prod_ppv_npv(), prod_sens_spec(), recall(), risk_ratio(), roc01(), sensitivity(), specificity(), sum_ppv_npv(), sum_sens_spec(), total_utility(), tp(), youden()

Examples

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

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