nestedcv (version 0.9.0)

prc: Build precision-recall curve

Description

Builds a precision-recall curve for a 'nestedcv' model using prediction() and performance() functions from the ROCR package and returns an object of class 'prc' for plotting.

Usage

prc(...)
# S3 method for default
prc(response, predictor, positive = 2, ...)
# S3 method for data.frame
prc(output, ...)
# S3 method for nestcv.glmnet
prc(object, ...)
# S3 method for nestcv.train
prc(object, ...)
# S3 method for nestcv.SuperLearner
prc(object, ...)
# S3 method for outercv
prc(object, ...)
# S3 method for repeatcv
prc(object, ...)

Value

An object of S3 class 'prc' containing the following fields:

recall

vector of recall values

precision

vector of precision values

auc

area under precision-recall curve value using trapezoid method

baseline

baseline precision value

Arguments

...

other arguments

response

binary factor vector of response of default order controls, cases.

predictor

numeric vector of probabilities

positive

Either an integer 1 or 2 for the level of response factor considered to be 'positive' or 'relevant', or a character value for that factor.

output

data.frame with columns testy containing observed response from test folds, and predyp predicted probabilities for classification

object

a 'nestcv.glmnet', 'nestcv.train', 'nestcv.SuperLearn', 'outercv' or 'repeatcv' S3 class results object.

Examples

# \donttest{
if (requireNamespace("mlbench")) {
library(mlbench)
data(Sonar)
y <- Sonar$Class
x <- Sonar[, -61]

fit1 <- nestcv.glmnet(y, x, family = "binomial", alphaSet = 1, cv.cores = 2)

fit1$prc <- prc(fit1)  # calculate precision-recall curve
fit1$prc$auc  # precision-recall AUC value

fit2 <- nestcv.train(y, x, method = "gbm", cv.cores = 2)
fit2$prc <- prc(fit2)
fit2$prc$auc

plot(fit1$prc, ylim = c(0, 1))
lines(fit2$prc, col = "red")

res <- nestcv.glmnet(y, x, family = "binomial", alphaSet = 1) |>
  repeatcv(n = 4, rep.cores = 2)

res$prc <- prc(res)  # precision-recall curve on repeated predictions
plot(res$prc)
}
# }