islasso (version 1.5.2)

GoF.islasso.path: 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, ...)

Value

A list of

gof

the goodness of fit measures

minimum

the position of the optimal lambda values

lambda.min

the optimal lambda values

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.

Author

Maintainer: Gianluca Sottile <[e-mail]>

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.

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)