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
Value
An object of the class penalty. This is a list with elements
penaltycharacter: the penalty name.
lambdadouble: the (nonnegative) regularization parameter.
getpenmatfunction: computes the diagonal penalty matrix.
Details
The `classic' penalty that incorporates variables selection. As introduced in Tibshirani (1996) the lasso penalty is defined as
$$P_\lambda^r (\boldsymbol{\beta}) = \lambda \sum_{i=1}^p |\beta_j|.$$
References
Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society B58, 267--288.