part | R Documentation |
The part
function takes an S3
object generated by
evalmod
and calculate partial AUCs and Standardized partial
AUCs of ROC and Precision-Recall curves.
Standardized pAUCs are standardized to the range between 0 and 1.
part(curves, xlim = NULL, ylim = NULL, curvetype = NULL)
## S3 method for class 'sscurves'
part(curves, xlim = c(0, 1), ylim = c(0, 1), curvetype = c("ROC", "PRC"))
## S3 method for class 'mscurves'
part(curves, xlim = c(0, 1), ylim = c(0, 1), curvetype = c("ROC", "PRC"))
## S3 method for class 'smcurves'
part(curves, xlim = c(0, 1), ylim = c(0, 1), curvetype = c("ROC", "PRC"))
## S3 method for class 'mmcurves'
part(curves, xlim = c(0, 1), ylim = c(0, 1), curvetype = c("ROC", "PRC"))
curves |
An
See the Value section of | |||||||||||||||
xlim |
A numeric vector of length two to specify x range between two points in [0, 1] | |||||||||||||||
ylim |
A numeric vector of length two to specify y range between two points in [0, 1] | |||||||||||||||
curvetype |
A character vector with the following curve types.
Multiple |
The part
function returns the same S3 object specified as
input with calculated pAUCs and standardized pAUCs.
evalmod
for generating S3
objects with
performance evaluation measures. pauc
for retrieving
a dataset of pAUCs.
## Not run:
## Load library
library(ggplot2)
##################################################
### 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))
## Show AUCs
sscurves.part
## Plot partial curve
plot(sscurves.part)
## Plot partial curve with ggplot
autoplot(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))
## Show AUCs
mscurves.part
## Plot partial curves
plot(mscurves.part)
## Plot partial curves with ggplot
autoplot(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)
## Calculate partial AUCs
smcurves.part <- part(smcurves, xlim = c(0.25, 0.75))
## Show AUCs
smcurves.part
## Plot partial curve
plot(smcurves.part)
## Plot partial curve with ggplot
autoplot(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))
## Show AUCs
mmcurves.part
## Plot partial curves
plot(mmcurves.part)
## Plot partial curves with ggplot
autoplot(mmcurves.part)
## End(Not run)
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