auc {precrec} | R Documentation |
Retrieve a data frame of AUC scores
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
See the Value section of |
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
[Package precrec version 0.14.4 Index]