part {precrec} | R Documentation |
Calculate partial AUCs
Description
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.
Usage
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"))
Arguments
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 |
Value
The part
function returns the same S3 object specified as
input with calculated pAUCs and standardized pAUCs.
See Also
evalmod
for generating S3
objects with
performance evaluation measures. pauc
for retrieving
a dataset of pAUCs.
Examples
## 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)