prc function

Build precision-recall curve

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.

prc(...)

## Default S3 method:
prc(response, predictor, positive = 2, ...)

## S3 method for class 'data.frame'
prc(output, ...)

## S3 method for class 'nestcv.glmnet'
prc(object, ...)

## S3 method for class 'nestcv.train'
prc(object, ...)

## S3 method for class 'nestcv.SuperLearner'
prc(object, ...)

## S3 method for class 'outercv'
prc(object, ...)

## S3 method for class 'repeatcv'
prc(object, ...)

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.

Returns

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

Examples

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)