evaluate.predictions {SIAMCAT} | R Documentation

## Evaluate prediction results

### Description

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

- Area Under the Receiver Operating Characteristic (ROC) Curve (AU-ROC)

- and the Precision-Recall Curve (PR)

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.

### Usage

    evaluate.predictions(siamcat, verbose = 1)

### Arguments

`siamcat` | object of class siamcat-class
`verbose` | 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`

### Details

This functions calculates for the predictions in the `pred_matrix` -slot of the siamcat-class-object several metrices. 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` average ROC-curve across repeats or a single ROC-curve on complete dataset;

- `$auc.average` AUC value for the average ROC-curve;

- `$ev.list` list of `length(num.folds)`, containing for different decision thresholds the number of false positives, false negatives, true negatives, and true positives;

- `$pr.list` list of `length(num.folds)`, containing the positive predictive value (precision) and true positive rate (recall) values used to plot the PR curves.

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

- `$roc.all` list of roc objects (see roc) for every repeat;

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

- `$auc.all` vector of AUC values for the ROC curves for every repeat.

### Value

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

### Examples

        data(siamcat_example)
        # simple working example
        siamcat_evaluated <- evaluate.predictions(siamcat_example)

[Package SIAMCAT version 1.0.0 Index]