This function takes the correct labels and predictions for all
samples and evaluates the results using the

as metric. Predictions can be supplied either for a single case or as
matrix after resampling of the dataset.

Prediction results are usually produced with the function
make.predictions.

evaluate.predictions(siamcat, verbose = 1)

## Arguments

siamcat |
object of class siamcat-class |

verbose |
integer, control output: `0` for no output at all,
`1` for only information about progress and success, `2` for
normal level of information and `3` for full debug information,
defaults to `1` |

## Value

object of class siamcat-class with the
slot `eval_data`

filled

## Details

This functions calculates several metrices for the predictions in
the `pred_matrix`

-slot of the siamcat-class-object.
The Area Under the Receiver Operating Characteristic (ROC)
Curve (AU-ROC) and the Precision-Recall Curve will be evaluated and
the results will be saved in the `eval_data`

-slot of the
supplied siamcat-class-object. The `eval_data`

-slot
contains a list with several entries:

`$roc`

- average ROC-curve across repeats or a
single ROC-curve on complete dataset (see roc);

`$auroc`

- AUC value for the average ROC-curve;

`$prc`

- list containing the positive predictive value
(precision) and true positive rate (recall) values used
to plot the mean PR curve;

`$auprc`

- AUC value for the mean PR curve;

`$ev`

- list containing for different decision thresholds
the number of false positives, false negatives, true
negatives, and true positives.

For the case of repeated cross-validation, the function will
additonally return

`$roc.all`

- list of roc objects (see roc)
for every repeat;

`$auroc.all`

- vector of AUC values for the ROC curves
for every repeat;

`$prc.all`

- list of PR curves for every repeat;

`$auprc.all`

- vector of AUC values for the PR curves
for every repeat;

`$ev.all`

- list of `ev`

lists (see above)
for every repeat.

## Examples

#> Evaluated predictions successfully.