roclab (version 0.1.4)

plot_roc: Plot Receiver Operating Characteristic (ROC) curve using ggroc

Description

Draws an ROC curve based on decision values. There is an option to display the AUC in the plot title and to print the ROC summary object.

Usage

plot_roc(
  y_true,
  y_score,
  col = "blue",
  size = 1.2,
  title = TRUE,
  summary = FALSE,
  ...
)

Value

A ggplot object of the ROC curve.

Arguments

y_true

Response vector with class labels in {-1, 1}. Labels given as {0, 1} or as a two-level factor/character are automatically converted to this format.

y_score

Numeric vector of predicted scores or decision values.

col

Line color.

size

Line width.

title

Logical; if TRUE, displays AUC in the plot title.

summary

Logical; if TRUE, prints the ROC object summary.

...

Additional arguments passed to pROC::ggroc().

Examples

set.seed(123)
n <- 100
n_pos <- round(0.2 * n)
n_neg <- n - n_pos
X <- rbind(
  matrix(rnorm(2 * n_neg, mean = -1), ncol = 2),
  matrix(rnorm(2 * n_pos, mean =  1), ncol = 2)
)
y <- c(rep(-1, n_neg), rep(1, n_pos))

fit <- roclearn(X, y, lambda = 0.1, approx=TRUE)

y_score <- predict(fit, X, type = "response")

plot_roc(y, y_score)