Binary classification measure defined as $$
\frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}.
$$
Also know as "recall" or "sensitivity".

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. |

## Value

Performance value as `numeric(1)`

.

## Note

This measure is undefined if TP + FN = 0.

## References

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

## See also

Other Binary Classification Measures:
`auc()`

,
`bbrier()`

,
`dor()`

,
`fbeta()`

,
`fdr()`

,
`fnr()`

,
`fn()`

,
`fomr()`

,
`fpr()`

,
`fp()`

,
`mcc()`

,
`npv()`

,
`ppv()`

,
`tnr()`

,
`tn()`

,
`tp()`

## Examples

#> [1] 0.5