islasso (version 1.6.0)

GoF.islasso.path: Select Optimal Lambda via Goodness-of-Fit Criteria

Description

Extracts the tuning parameter lambda minimizing multiple information criteria from a fitted islasso.path object. Supported criteria include AIC, BIC, AICc, eBIC, GCV, and GIC.

Usage

GoF.islasso.path(object, plot = TRUE, ...)

Value

A list with components:

gof

Matrix of goodness-of-fit values across lambda values.

minimum

Index positions of the minimum for each criterion.

lambda.min

Optimal lambda values that minimize each criterion.

Arguments

object

A fitted model of class "islasso.path".

plot

Logical. If TRUE (default), displays plots for each criterion over the lambda path.

...

Additional arguments passed to lower-level plotting or diagnostic methods.

Author

Gianluca Sottile

Details

This function identifies the optimal regularization parameter lambda by minimizing various information-based selection criteria. Degrees of freedom are computed as the trace of the hat matrix, which may be fractional under induced smoothing. This provides a robust alternative to cross-validation, especially in high-dimensional settings.

See Also

islasso.path, summary.islasso.path, predict.islasso.path, coef.islasso.path, deviance.islasso.path, logLik.islasso.path, residuals.islasso.path, fitted.islasso.path

Examples

set.seed(1)
n <- 100; p <- 30
beta <- c(runif(10, -2, 2), rep(0, p - 10))
sim <- simulXy(n = n, p = p, beta = beta, seed = 1, family = gaussian())
fit <- islasso.path(y ~ ., data = sim$data, family = gaussian())
GoF.islasso.path(fit)