auc: Retrieve a data frame of AUC scores

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

Description

The auc function takes an S3 object generated by evalmod and retrieves a data frame with the Area Under the Curve (AUC) scores of ROC and Precision-Recall curves.

Usage

auc(curves)

## S3 method for class 'aucs'
auc(curves)

Arguments

curves

An S3 object generated by evalmod. The auc 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 AUC scores.

See Also

evalmod for generating S3 objects with performance evaluation measures. pauc for retrieving a dataset of pAUCs.

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)

## Shows AUCs
auc(sscurves)


##################################################
### 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)

## Shows AUCs
auc(mscurves)


##################################################
### 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)

## Get AUCs
sm_aucs <- auc(smcurves)

## Shows AUCs
sm_aucs

## Get AUCs of Precision-Recall
sm_aucs_prc <- subset(sm_aucs, curvetypes == "PRC")

## Shows AUCs
sm_aucs_prc

##################################################
### 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)

## Get AUCs
mm_aucs <- auc(mmcurves)

## Shows AUCs
mm_aucs

## Get AUCs of Precision-Recall
mm_aucs_prc <- subset(mm_aucs, curvetypes == "PRC")

## Shows AUCs
mm_aucs_prc

guillermozbta/precrec documentation built on May 11, 2019, 7:22 p.m.