cv.hdsvm: Cross-validation for Selecting the Tuning Parameter in the...

In hdsvm: Fast Algorithm for Support Vector Machine

Cross-validation for Selecting the Tuning Parameter in the Penalized SVM

Description

Performs k-fold cross-validation for hdsvm.

Usage

cv.hdsvm(x, y, lambda = NULL, nfolds = 5L, foldid, ...)

Arguments

x	A numerical matrix with n rows (observations) and p columns (variables).
y	Response variable.
lambda	Optional; a user-supplied sequence of lambda values. If NULL, hdsvm selects its own sequence.
nfolds	Number of folds for cross-validation. Defaults to 5.
foldid	Optional vector specifying the indices of observations in each fold. If provided, it overrides nfolds.
...	Additional arguments passed to hdsvm.

Details

This function computes the average cross-validation error and provides the standard error.

Value

An object with S3 class cv.hdsvm consisting of

lambda	Candidate lambda values.
cvm	Mean cross-validation error.
cvsd	Standard error of the mean cross-validation error.
cvup	Upper confidence curve: cvm + cvsd.
cvlo	Lower confidence curve: cvm - cvsd.
lambda.min	lambda achieving the minimum cross-validation error.
lambda.1se	Largest lambda within one standard error of the minimum error.
cv.min	Cross-validation error at lambda.min.
cv.1se	Cross-validation error at lambda.1se.
hdsvm.fit	a fitted hdsvm object for the full data.
nzero	Number of non-zero coefficients at each lambda.

Examples

set.seed(315)
n <- 100
p <- 400
x1 <- matrix(rnorm(n / 2 * p, -0.25, 0.1), n / 2)
x2 <- matrix(rnorm(n / 2 * p, 0.25, 0.1), n / 2)
x <- rbind(x1, x2)
beta <- 0.1 * rnorm(p)
prob <- plogis(c(x %*% beta))
y <- 2 * rbinom(n, 1, prob) - 1
lam2 <- 0.01
fit <- cv.hdsvm(x, y, lam2=lam2)

hdsvm documentation built on April 12, 2025, 1:27 a.m.