calculate_performance: Performance measures for two-class classification
In GegznaV/multiROC: Tools for ROC Analyis

Description Usage Arguments Value Author(s) See Also Examples

Performance measures for two-class classification

1	calculate_performance(truth, prediction, pos_label = levels(truth)[2])

`truth`	(`factor`) A factor vector with "true"/reference classes.
`prediction`	A vector with predicted classes.
`pos_label`	(`character(1)`) A string with the name of positive group. By default, the second level of factor `truth` is treated as positive.

A matrix with the following columns:

tp (number of true positives), fn (number of false negatives), fp (number of false positives), tn (number of true negatives), sens (sensitivity), spec (specificity), ppv (positive predictive value), npv (negative predictive value), youden (Youden's j index), kappa (Cohen's kappa).
attribute labels contains a named vector with elements pos_label and neg_label, which indicate labels of positive and negative groups respectively.

Vilmantas Gegzna

Other functions for ROC: access_elements, print.as_str(), roc_performance_measures, roc_predict()

(truth <- gl(n = 2, k = 3, length = 20, labels = c("H", "S")))

(prediction <- rev(truth))


calculate_performance(truth, prediction)

calculate_performance(truth, prediction, pos_label = "S")

calculate_performance(truth, prediction, pos_label = "H")