lqa (version 1.0-3)

lasso: Lasso Penalty

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

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.

Details

The `classic' penalty that incorporates variables selection. As introduced in Tibshirani (1996) the lasso penalty is defined as $$P_{\lambda }^r(\mathit{\beta })=\lambda \sum _{i=1}^p|{\beta }_j|.$$

References

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

See Also

penalty, ridge, penalreg