pauc: Retrieve a data frame of pAUC scores

In precrec: Calculate Accurate Precision-Recall and ROC (Receiver Operator Characteristics) Curves

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 class 'aucs'
pauc(curves)

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.

Value

The auc function returns a data frame with pAUC scores.

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)

precrec documentation built on Oct. 12, 2023, 1:06 a.m.