Measure to compare true observed labels with predicted labels in binary classification tasks.

## Usage

```
tpr(truth, response, positive, na_value = NaN, ...)
recall(truth, response, positive, na_value = NaN, ...)
sensitivity(truth, response, positive, na_value = NaN, ...)
```

## Arguments

- truth
(

`factor()`

)

True (observed) labels. Must have the exactly same two levels and the same length as`response`

.- response
(

`factor()`

)

Predicted response labels. Must have the exactly same two levels and the same length as`truth`

.- positive
(

`character(1))`

Name of the positive class.- na_value
(

`numeric(1)`

)

Value that should be returned if the measure is not defined for the input (as described in the note). Default is`NaN`

.- ...
(

`any`

)

Additional arguments. Currently ignored.

## Details

The True Positive Rate is defined as $$ \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}. $$ Also know as "recall" or "sensitivity".

This measure is undefined if TP + FN = 0.

## References

https://en.wikipedia.org/wiki/Template:DiagnosticTesting_Diagram

Goutte C, Gaussier E (2005).
“A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation.”
In *Lecture Notes in Computer Science*, 345–359.
doi:10.1007/978-3-540-31865-1_25
.