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Calculate receiver operator measures.

Source: R/calculateROCMeasures.R
calculateROCMeasures.Rd

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 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.

Functions

  • print(ROCMeasures):

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)
#>     predicted
#> true M        R                             
#>    M 95       16        tpr: 0.86 fnr: 0.14 
#>    R 10       87        fpr: 0.1  tnr: 0.9  
#>      ppv: 0.9 for: 0.16 lrp: 8.3  acc: 0.88 
#>      fdr: 0.1 npv: 0.84 lrm: 0.16 dor: 51.66
#> 
#> 
#> Abbreviations:
#> 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