precrec (version 0.14.4)

pauc: Retrieve a data frame of pAUC scores

Description

The auc function takes an S3 object generated by part and evalmod and retrieves a data frame with the partial AUC scores of ROC and Precision-Recall curves.

Usage

pauc(curves)
# S3 method for aucs
pauc(curves)

Value

The auc function returns a data frame with pAUC scores.

Arguments

curves

An S3 object generated by part and evalmod. The pauc function accepts the following S3 objects.

S3 object	# of models	# of test datasets
sscurves	single	single
mscurves	multiple	single
smcurves	single	multiple
mmcurves	multiple	multiple

See the Value section of evalmod for more details.

See Also

evalmod for generating S3 objects with performance evaluation measures. part for calculation of pAUCs. auc for retrieving a dataset of AUCs.

Examples

##################################################
### Single model & single test dataset
###

## Load a dataset with 10 positives and 10 negatives
data(P10N10)

## Generate an sscurve object that contains ROC and Precision-Recall curves
sscurves <- evalmod(scores = P10N10$scores, labels = P10N10$labels)

## Calculate partial AUCs
sscurves.part <- part(sscurves, xlim = c(0.25, 0.75))

## Shows pAUCs
pauc(sscurves.part)

##################################################
### Multiple models & single test dataset
###

## Create sample datasets with 100 positives and 100 negatives
samps <- create_sim_samples(1, 100, 100, "all")
mdat <- mmdata(samps[["scores"]], samps[["labels"]],
  modnames = samps[["modnames"]]
)

## Generate an mscurve object that contains ROC and Precision-Recall curves
mscurves <- evalmod(mdat)

## Calculate partial AUCs
mscurves.part <- part(mscurves, xlim = c(0, 0.75), ylim = c(0.25, 0.75))

## Shows pAUCs
pauc(mscurves.part)

##################################################
### Single model & multiple test datasets
###

## Create sample datasets with 100 positives and 100 negatives
samps <- create_sim_samples(4, 100, 100, "good_er")
mdat <- mmdata(samps[["scores"]], samps[["labels"]],
  modnames = samps[["modnames"]],
  dsids = samps[["dsids"]]
)

## Generate an smcurve object that contains ROC and Precision-Recall curves
smcurves <- evalmod(mdat, raw_curves = TRUE)

## Calculate partial AUCs
smcurves.part <- part(smcurves, xlim = c(0.25, 0.75))

## Shows pAUCs
pauc(smcurves.part)

##################################################
### Multiple models & multiple test datasets
###

## Create sample datasets with 100 positives and 100 negatives
samps <- create_sim_samples(4, 100, 100, "all")
mdat <- mmdata(samps[["scores"]], samps[["labels"]],
  modnames = samps[["modnames"]],
  dsids = samps[["dsids"]]
)

## Generate an mscurve object that contains ROC and Precision-Recall curves
mmcurves <- evalmod(mdat, raw_curves = TRUE)

## Calculate partial AUCs
mmcurves.part <- part(mmcurves, xlim = c(0, 0.25))

## Shows pAUCs
pauc(mmcurves.part)