evaBin: Evaluation of binary classifiers
In moritz-hanke/LittleHelpers: Little Helpers: Small and less small functions to make life easier

evaBin returns different measures for the performance of binary classifiers.

1 2	evaBin(p = NULL, n = NULL, tp = NULL, tn = NULL, fp = NULL, fn = NULL, s = NULL, s.est = NULL)

`p`	Numeric, the number of positives.
`n`	Numeric, the number of negatives.
`tp`	Numeric, the number of true positives.
`tn`	Numeric, the number of true negatives.
`fp`	Numeric, the number of false positives.
`fn`	Numeric, the number of false negatives.
`s`	Numeric, true vector with values `TRUE` and `FALSE` or `1`s and `0`s.
`s.est`	Numeric, extimated vector with values `TRUE` and `FALSE` or `1`s and `0`s.

Returns a list with the following measures:

`True positive (TP)`
`False positive (FP)`
`True negative (TN)`
`False negative (FN)`
`Precision`
`F1-Score (F1)`
`Accuracy (ACC)`
`Balanced accuracy (BACC)`
`Matthews correlation coefficient(MCC)`
`True postive rate (TPR)`
`True negative rate (TNR)`
`False positive rate (FPR)`
`False negative rate (FNR)`
`False desovery rate (FDR)`

1	evaBin(7, 13, 2, 10)

moritz-hanke/LittleHelpers documentation built on May 3, 2019, 4:07 p.m.

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