aic.islasso: Optimization for Lambda Selection

In islasso: The Induced Smoothed Lasso

Optimization for Lambda Selection

Description

Minimizes information criteria to select the optimal tuning parameter lambda for islasso models. Supports AIC, BIC, AICc, GCV, and GIC.

Usage

aic.islasso(
  object,
  method = c("AIC", "BIC", "AICc", "GCV", "GIC"),
  interval,
  g = 0,
  y,
  X,
  intercept = FALSE,
  family = gaussian(),
  alpha = 1,
  offset,
  weights,
  unpenalized,
  control = is.control(),
  trace = TRUE
)

Arguments

object	Fitted model of class "islasso".
method	Criterion to minimize. Options are "AIC", "BIC", "AICc", "GCV", "GIC".
interval	Numeric vector (length 2) giving lower and upper bounds for lambda optimization. Optional if object includes prior cross-validation.
g	Numeric in [0,1]. Governs BIC generalization: g = 0 is classic BIC, g = 0.5 is extended BIC.
y	Response vector. Required only if object is missing.
X	Design matrix. Required only if object is missing.
intercept	Logical. Whether to include intercept in X. Used if object is missing.
family	Error distribution. Accepted: gaussian, binomial, poisson. Uses canonical link.
alpha	Elastic-net mixing parameter, 0 <= alpha <= 1. Lasso: alpha = 1; Ridge: alpha = 0.
offset	Optional numeric vector. Adds known linear predictor component.
weights	Optional weights for observations. Defaults to 1.
unpenalized	Logical vector indicating variables to exclude from penalization.
control	List of control parameters. See is.control.
trace	Logical. If TRUE, prints progress of optimization. Default is TRUE.

Details

Instead of using cross-validation, this function selects the best lambda by minimizing criteria like AIC or BIC. Degrees of freedom are computed as the trace of the hat matrix (not necessarily an integer).

Value

Optimal lambda value as numeric.

Author(s)

Gianluca Sottile [e-mail]

See Also

islasso, islasso.fit, summary.islasso, logLik.islasso, predict.islasso

Examples

set.seed(1)
n <- 100; p <- 100
beta <- c(rep(2, 20), rep(0, p - 20))
sim1 <- simulXy(n = n, p = p, beta = beta, seed = 1, family = gaussian())
o <- islasso(y ~ ., data = sim1$data, family = gaussian())

## Not run: 
# Use the evaluation interval of the fit
lambda_aic <- aic.islasso(o, method = "AIC")

# Overwrites the evaluation interval for lambda
lambda_bic <- aic.islasso(o, interval = c(0.1, 30), method = "BIC")

# Overwrites the evaluation interval for lambda using eBIC criterion
lambda_ebic <- aic.islasso(o, interval = c(0.1, 30), method = "BIC", g = 0.5)

## End(Not run)

islasso documentation built on Aug. 9, 2025, 1:06 a.m.