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Calculate the Kappa metric from true positives, false positives, true negatives and false negatives. The inputs must be vectors of equal length. mrg_a = ((tp + fn) * (tp + fp)) / (tp + fn + fp + tn) mrg_b = ((fp + tn) * (fn + tn)) / (tp + fn + fp + tn) expec_agree = (mrg_a + mrg_b) / (tp + fn + fp + tn) obs_agree = (tp + tn) / (tp + fn + fp + tn) cohens_kappa = (obs_agree - expec_agree) / (1 - expec_agree)
cohens_kappa(tp, fp, tn, fn, ...)
(numeric) number of true positives.
(numeric) number of false positives.
(numeric) number of true negatives.
(numeric) number of false negatives.
for capturing additional arguments passed by method.
A numeric matrix with the column name "cohens_kappa".
Other metric functions: F1_score(), Jaccard(), abs_d_ppv_npv(), abs_d_sens_spec(), accuracy(), 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(), tpr(), tp(), youden()
F1_score()
Jaccard()
abs_d_ppv_npv()
abs_d_sens_spec()
accuracy()
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()
tpr()
tp()
youden()
# NOT RUN { cohens_kappa(10, 5, 20, 10) cohens_kappa(c(10, 8), c(5, 7), c(20, 12), c(10, 18)) # }
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