sum_ppv_npv: Calculate the sum of positive and negative predictive value

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

Calculate the sum of positive and negative predictive value

Description

Calculate the sum of positive predictive value (PPV) and negative predictive value (NPV) from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length.

ppv = tp / (tp + fp)
npv = tn / (tn + fn)
sum_ppv_npv = ppv + npv

Usage

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

Examples

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

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