roclab (version 0.1.4)

plot.cv.kroclearn: Visualize Cross-Validation results for kernel models

Description

Produce a visualization of cross-validation results from a fitted cv.kroclearn object. The plot shows the mean AUC across regularization parameters \(\lambda\), with error bars reflecting the cross-validation standard deviation. Optionally, the selected optimal \(\lambda\) is highlighted with a dashed line and marker.

Usage

# S3 method for cv.kroclearn
plot(x, highlight = TRUE, ...)

Value

A ggplot2 object is returned and drawn to the current device.

Arguments

x

A cross-validation object of class "cv.kroclearn".

highlight

Logical; if TRUE, mark the selected optimal \(\lambda\) with a vertical dashed line with a red point (default TRUE).

...

Additional arguments passed to underlying ggplot2 functions.

Details

This function is a method for the generic plot() function, designed specifically for cross-validation objects from cv.kroclearn. The x-axis is displayed on a log scale for \(\lambda\), and the y-axis represents AUC values. Error bars show variability across folds. This is the kernel counterpart of plot.cv.roclearn.

Examples

set.seed(123)
n <- 100
r <- sqrt(runif(n, 0.05, 1))
theta <- runif(n, 0, 2*pi)
X <- cbind(r * cos(theta), r * sin(theta))
y <- ifelse(r < 0.5, 1, -1)

cvfit <- cv.kroclearn(
  X, y,
  lambda.vec = exp(seq(log(0.01), log(5), length.out = 3)),
  kernel = "radial",
  approx=TRUE, nfolds = 2
)
plot(cvfit)