calculateROCMeasures: Calculate receiver operator measures.
In guillermozbta/s2: Machine Learning in R

Description Usage Arguments Value Methods (by generic) See Also Examples

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.

calculateROCMeasures(pred)

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

`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.

[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.

print:

Other roc: asROCRPrediction, plotViperCharts

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

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