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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)
tpr(tp, fn, ...)fpr(fp, tn, ...)tnr(fp, tn, ...)fnr(tp, fn, ...)
fpr(fp, tn, ...)
tnr(fp, tn, ...)
fnr(tp, fn, ...)
(numeric) number of true positives.
(numeric) number of false negatives.
for capturing additional arguments passed by method.
(numeric) number of false positives.
(numeric) number of true negatives.
Other metric functions: F1_score, abs_d_ppv_npv, abs_d_sens_spec, accuracy, cohens_kappa, cutpoint, false_omission_rate, 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, tp, youden
F1_score
abs_d_ppv_npv
abs_d_sens_spec
accuracy
cohens_kappa
cutpoint
false_omission_rate
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
tp
youden
# NOT RUN { tpr(10, 5, 20, 10) tpr(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) # }
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