Calculate partial AUCs

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 sscurves
part(curves, xlim = c(0, 1), ylim = c(0, 1), curvetype = c("ROC", "PRC"))

# S3 method for mscurves
part(curves, xlim = c(0, 1), ylim = c(0, 1), curvetype = c("ROC", "PRC"))

# S3 method for smcurves
part(curves, xlim = c(0, 1), ylim = c(0, 1), curvetype = c("ROC", "PRC"))

# S3 method for mmcurves
part(curves, xlim = c(0, 1), ylim = c(0, 1), curvetype = c("ROC", "PRC"))

Arguments

curves

An S3 object generated by evalmod. The part function accepts the following S3 objects.

`S3` object	# of models	# of test datasets
sscurves	single	single
mscurves	multiple	single
smcurves	single	multiple
mmcurves	multiple	multiple

See the Value section of evalmod for more details.

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.

curvetype	description
ROC	ROC curve
PRC	Precision-Recall curve

Multiple curvetype can be combined, such as c("ROC", "PRC").

Value

The part function returns the same S3 object specified as input with calculated pAUCs and standardized pAUCs.

Examples

if (FALSE) {

## 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)
}

Arguments

Value

See also

Examples