lasso: Lasso Penalty

In lqa: Penalized Likelihood Inference for GLMs

Description

Object of the penalty class to handle the lasso penalty (Tibshirani, 1996).

Usage

lasso (lambda = NULL, ...)

Arguments

lambda	regularization parameter. This must be a nonnegative real number.
...	further arguments

Details

The ‘classic’ penalty that incorporates variables selection. As introduced in Tibshirani (1996) the lasso penalty is defined as

P_λ^r (\boldsymbol{β}) = λ ∑_{i=1}^p |β_j|.

Value

An object of the class penalty. This is a list with elements

penalty	character: the penalty name.
lambda	double: the (nonnegative) regularization parameter.
getpenmat	function: computes the diagonal penalty matrix.

Author(s)

Jan Ulbricht

References

Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society B 58, 267–288.

See Also

penalty, ridge, penalreg

lqa documentation built on May 30, 2017, 3:41 a.m.