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.
pauc(curves)
# S3 method for class 'aucs'
pauc(curves)The auc function returns a data frame with pAUC scores.
##################################################
### 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)
#> modnames dsids curvetypes paucs spaucs
#> 1 m1 1 ROC 0.3771875 0.7543750
#> 2 m1 1 PRC 0.3616708 0.7233417
##################################################
### 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)
#> modnames dsids curvetypes paucs spaucs
#> 1 random 1 ROC 0.0878000 0.2341333
#> 2 random 1 PRC 0.1495042 0.3986777
#> 3 poor_er 1 ROC 0.3028000 0.8074667
#> 4 poor_er 1 PRC 0.3748271 0.9995389
#> 5 good_er 1 ROC 0.2990000 0.7973333
#> 6 good_er 1 PRC 0.3626852 0.9671606
#> 7 excel 1 ROC 0.3677000 0.9805333
#> 8 excel 1 PRC 0.3750000 1.0000000
#> 9 perf 1 ROC 0.3750000 1.0000000
#> 10 perf 1 PRC 0.3750000 1.0000000
##################################################
### 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)
#> modnames dsids curvetypes paucs spaucs
#> 1 good_er 1 ROC 0.4265000 0.8530000
#> 2 good_er 1 PRC 0.4233741 0.8467483
#> 3 good_er 2 ROC 0.4368000 0.8736000
#> 4 good_er 2 PRC 0.4687105 0.9374211
#> 5 good_er 3 ROC 0.4188000 0.8376000
#> 6 good_er 3 PRC 0.4204513 0.8409027
#> 7 good_er 4 ROC 0.4232000 0.8464000
#> 8 good_er 4 PRC 0.4557503 0.9115005
##################################################
### 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)
#> modnames dsids curvetypes paucs spaucs
#> 1 random 1 ROC 0.01850000 0.0740000
#> 2 random 1 PRC 0.08953401 0.3581360
#> 3 poor_er 1 ROC 0.11170000 0.4468000
#> 4 poor_er 1 PRC 0.21302642 0.8521057
#> 5 good_er 1 ROC 0.14020000 0.5608000
#> 6 good_er 1 PRC 0.25000000 1.0000000
#> 7 excel 1 ROC 0.21380000 0.8552000
#> 8 excel 1 PRC 0.25000000 1.0000000
#> 9 perf 1 ROC 0.25000000 1.0000000
#> 10 perf 1 PRC 0.25000000 1.0000000
#> 11 random 2 ROC 0.02960000 0.1184000
#> 12 random 2 PRC 0.11552352 0.4620941
#> 13 poor_er 2 ROC 0.10530000 0.4212000
#> 14 poor_er 2 PRC 0.22490831 0.8996333
#> 15 good_er 2 ROC 0.14880000 0.5952000
#> 16 good_er 2 PRC 0.25000000 1.0000000
#> 17 excel 2 ROC 0.24040000 0.9616000
#> 18 excel 2 PRC 0.25000000 1.0000000
#> 19 perf 2 ROC 0.25000000 1.0000000
#> 20 perf 2 PRC 0.25000000 1.0000000
#> 21 random 3 ROC 0.02410000 0.0964000
#> 22 random 3 PRC 0.11096931 0.4438773
#> 23 poor_er 3 ROC 0.09510000 0.3804000
#> 24 poor_er 3 PRC 0.17398022 0.6959209
#> 25 good_er 3 ROC 0.15660000 0.6264000
#> 26 good_er 3 PRC 0.25000000 1.0000000
#> 27 excel 3 ROC 0.23810000 0.9524000
#> 28 excel 3 PRC 0.25000000 1.0000000
#> 29 perf 3 ROC 0.25000000 1.0000000
#> 30 perf 3 PRC 0.25000000 1.0000000
#> 31 random 4 ROC 0.03760000 0.1504000
#> 32 random 4 PRC 0.12427433 0.4970973
#> 33 poor_er 4 ROC 0.08250000 0.3300000
#> 34 poor_er 4 PRC 0.19368209 0.7747284
#> 35 good_er 4 ROC 0.15300000 0.6120000
#> 36 good_er 4 PRC 0.25000000 1.0000000
#> 37 excel 4 ROC 0.23570000 0.9428000
#> 38 excel 4 PRC 0.25000000 1.0000000
#> 39 perf 4 ROC 0.25000000 1.0000000
#> 40 perf 4 PRC 0.25000000 1.0000000