R: Calculate receiver operator measures.

calculateROCMeasures {mlr} | R Documentation

## Calculate receiver operator measures.

### Description

Calculate the absolute number of correct/incorrect classifications and the following evaluation measures:

- `tpr` True positive rate (Sensitivity, Recall)

- `fpr` False positive rate (Fall-out)

- `fnr` False negative rate (Miss rate)

- `tnr` True negative rate (Specificity)

- `ppv` Positive predictive value (Precision)

- `for` False omission rate

- `lrp` Positive likelihood ratio (LR+)

- `fdr` False discovery rate

- `npv` Negative predictive value

- `acc` Accuracy

- `lrm` Negative likelihood ratio (LR-)

- `dor` Diagnostic odds ratio

For details on the used measures see measures and also https://en.wikipedia.org/wiki/Receiver_operating_characteristic.

The element for the false omission rate in the resulting object is not called `for` but `fomr` since `for` should never be used as a variable name in an object.

### Usage

    calculateROCMeasures(pred)

    ## S3 method for class 'ROCMeasures'
    print(x, abbreviations = TRUE, digits = 2, ...)

### Arguments

`pred` | (Prediction) Prediction object.
`x` | (`ROCMeasures`) Created by calculateROCMeasures.
`abbreviations` | (`logical(1)`) If `TRUE` a short paragraph with explanations of the used measures is printed additionally.
`digits` | (`integer(1)`) Number of digits the measures are rounded to.
`...` | `(any)` Currently not used.

### Value

(`ROCMeasures`). A list containing two elements `confusion.matrix` which is the 2 times 2 confusion matrix of absolute frequencies and `measures`, a list of the above mentioned measures.

### Methods (by generic)

- `print`:

### See Also

Other roc: `asROCRPrediction`

Other performance: `ConfusionMatrix`, `calculateConfusionMatrix`, `estimateRelativeOverfitting`, `makeCostMeasure`, `makeCustomResampledMeasure`, `makeMeasure`, `measures`, `performance`, `setAggregation`, `setMeasurePars`

### Examples

    lrn = makeLearner("classif.rpart", predict.type = "prob")
    fit = train(lrn, sonar.task)
    pred = predict(fit, task = sonar.task)
    calculateROCMeasures(pred)

[Package mlr version 2.12.1 Index]