GoF.islasso.path: Optimization for the selection of the tuning parameter

In islasso: The Induced Smoothed Lasso

Optimization for the selection of the tuning parameter

Description

This function extracts the value of the tuning parameter which minimizes the AIC/BIC/AICc/eBIC/GCV/GIC criterion in “islasso.path”.

Usage

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

Arguments

object	a fitted model object of class "islasso.path".
plot	a logical flag indicating if each criterion have to be plotted
...	further arguments passed to or from other methods.

Details

Minimization of the Akaike Information Criterion (AIC), or Bayesian Information Criterion (BIC) or several other criteria are sometimes employed to select the tuning parameter as an alternative to the cross validation. The model degrees of freedom (not necessarly integers as in the plain lasso) used in all methods are computed as trace of the hat matrix at convergence.

Value

A list of

gof	the goodness of fit measures
minimum	the position of the optimal lambda values
lambda.min	the optimal lambda values

Author(s)

Maintainer: Gianluca Sottile <[e-mail]>

See Also

islasso.path, islasso.path.fit, coef.islasso.path, residuals.islasso.path, summary.islasso.path, logLik.islasso.path, fitted.islasso.path, predict.islasso.path and deviance.islasso.path methods.

Examples

set.seed(1)
n <- 100
p <- 30
p1 <- 10  #number of nonzero coefficients
coef.veri <- sort(round(c(seq(.5, 3, l=p1/2), seq(-1, -2, l=p1/2)), 2))
sigma <- 1

coef <- c(coef.veri, rep(0, p-p1))

X <- matrix(rnorm(n*p), n, p)
mu <- drop(X%*%coef)
y <- mu + rnorm(n, 0, sigma)

o <- islasso.path(y ~ ., data = data.frame(y = y, X))
GoF.islasso.path(o)

islasso documentation built on May 31, 2023, 8:37 p.m.