Description Usage Arguments Value See Also Examples
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.
1 2 3 4 |
curves |
An
See the Value section of |
The auc
function returns a data frame with pAUC scores.
evalmod
for generating S3
objects with
performance evaluation measures. part
for calculation of
pAUCs. auc
for retrieving a dataset of AUCs.
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### 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)
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