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 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 0.1419000 0.3784000
#> 2 random 1 PRC 0.2258450 0.6022533
#> 3 poor_er 1 ROC 0.3130000 0.8346667
#> 4 poor_er 1 PRC 0.3739481 0.9971950
#> 5 good_er 1 ROC 0.3097000 0.8258667
#> 6 good_er 1 PRC 0.3655041 0.9746776
#> 7 excel 1 ROC 0.3727000 0.9938667
#> 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 0.4072000 0.8144000
#> 2 good_er 1 PRC 0.4342177 0.8684353
#> 3 good_er 2 ROC 0.4138000 0.8276000
#> 4 good_er 2 PRC 0.4392181 0.8784363
#> 5 good_er 3 ROC 0.3907000 0.7814000
#> 6 good_er 3 PRC 0.4182098 0.8364195
#> 7 good_er 4 ROC 0.4050000 0.8100000
#> 8 good_er 4 PRC 0.4321187 0.8642374
##################################################
### 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 0.0324000 0.1296000
#> 2 random 1 PRC 0.1148775 0.4595100
#> 3 poor_er 1 ROC 0.0810000 0.3240000
#> 4 poor_er 1 PRC 0.1737708 0.6950832
#> 5 good_er 1 ROC 0.1045000 0.4180000
#> 6 good_er 1 PRC 0.2458400 0.9833601
#> 7 excel 1 ROC 0.2278000 0.9112000
#> 8 excel 1 PRC 0.2500000 1.0000000
#> 9 perf 1 ROC 0.2500000 1.0000000
#> 10 perf 1 PRC 0.2500000 1.0000000
#> 11 random 2 ROC 0.0541000 0.2164000
#> 12 random 2 PRC 0.1845236 0.7380944
#> 13 poor_er 2 ROC 0.0919000 0.3676000
#> 14 poor_er 2 PRC 0.2068986 0.8275943
#> 15 good_er 2 ROC 0.1528000 0.6112000
#> 16 good_er 2 PRC 0.2500000 1.0000000
#> 17 excel 2 ROC 0.2299000 0.9196000
#> 18 excel 2 PRC 0.2500000 1.0000000
#> 19 perf 2 ROC 0.2500000 1.0000000
#> 20 perf 2 PRC 0.2500000 1.0000000
#> 21 random 3 ROC 0.0388000 0.1552000
#> 22 random 3 PRC 0.1630154 0.6520615
#> 23 poor_er 3 ROC 0.0595000 0.2380000
#> 24 poor_er 3 PRC 0.1756503 0.7026013
#> 25 good_er 3 ROC 0.1382000 0.5528000
#> 26 good_er 3 PRC 0.2500000 1.0000000
#> 27 excel 3 ROC 0.2317000 0.9268000
#> 28 excel 3 PRC 0.2500000 1.0000000
#> 29 perf 3 ROC 0.2500000 1.0000000
#> 30 perf 3 PRC 0.2500000 1.0000000
#> 31 random 4 ROC 0.0602000 0.2408000
#> 32 random 4 PRC 0.1589261 0.6357043
#> 33 poor_er 4 ROC 0.1074000 0.4296000
#> 34 poor_er 4 PRC 0.2461646 0.9846584
#> 35 good_er 4 ROC 0.1290000 0.5160000
#> 36 good_er 4 PRC 0.2382133 0.9528533
#> 37 excel 4 ROC 0.2287000 0.9148000
#> 38 excel 4 PRC 0.2487740 0.9950959
#> 39 perf 4 ROC 0.2500000 1.0000000
#> 40 perf 4 PRC 0.2500000 1.0000000