roc01: Calculate the distance between points on the ROC curve and...

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

Calculate the distance between points on the ROC curve and (0,1)

Description

Calculate the distance on the ROC space between points on the ROC curve and the point of perfect discrimination from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. To be used with method = minimize_metric.

sensitivity = tp / (tp + fn)
specificity = tn / (tn + fp)
roc01 = sqrt((1 - sensitivity)^2 + (1 - specificity)^2)

Usage

roc01(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(), sensitivity(), specificity(), sum_ppv_npv(), sum_sens_spec(), total_utility(), tpr(), tp(), youden()

Examples

roc01(10, 5, 20, 10)
roc01(c(10, 8), c(5, 7), c(20, 12), c(10, 18))
oc <- cutpointr(suicide, dsi, suicide,
  method = minimize_metric, metric = roc01)
plot_roc(oc)

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