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

In hdsvm: Fast Algorithm for Support Vector Machine

Cross-validation for Selecting the Tuning Parameter of Nonconvex Penalized SVM

Description

Conducts k-fold cross-validation for the 'nc.hdsvm()' function.

Usage

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

Arguments

x	A numerical matrix with dimensions (n rows and p columns), where each row represents an observation.
y	Response variable.
lambda	Optional user-supplied sequence of lambda values.
nfolds	Number of folds in the cross-validation, default is 5.
foldid	An optional vector that assigns each observation to a specific fold. If provided, this parameter overrides nfolds.
...	Additional arguments passed to nc.hdsvm.

Details

This function estimates the average cross-validation error and its standard error across folds. It is primarily used to identify the optimal lambda value for fitting nonconvex penalized SVM models.

Value

An object of class cv.nc.hdsvm is returned, which is a list with the ingredients of the cross-validated fit.

lambda	the values of lambda used in the fits.
cvm	the mean cross-validated error - a vector of length length(lambda).
cvsd	estimate of standard error of cvm.
cvupper	upper curve = cvm+cvsd.
cvlower	lower curve = cvm-cvsd.
nzero	number of non-zero coefficients at each lambda.
name	a text string indicating type of measure (for plotting purposes).
nchdsvm.fit	a fitted nc.hdsvm object for the full data.
lambda.min	The optimal value of lambda that gives minimum cross validation error cvm.
lambda.1se	The largest value of lambda such that error is within 1 standard error of the minimum.

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
lambda <- 10^(seq(1,-4, length.out=30))
cv.nc.fit <- cv.nc.hdsvm(x=x, y=y, lambda=lambda, lam2=lam2, pen="scad")

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