LassoShooting.fit: Shooting Lasso

View source: R/LassoShooting.fit.R

LassoShooting.fitR Documentation

Shooting Lasso

Description

Implementation of the Shooting Lasso (Fu, 1998) with variable dependent penalization weights.

Usage

LassoShooting.fit(
  x,
  y,
  lambda,
  control = list(maxIter = 1000, optTol = 10^(-5), zeroThreshold = 10^(-6)),
  XX = NULL,
  Xy = NULL,
  beta.start = NULL
)

Arguments

x

matrix of regressor variables (n times p where n denotes the number of observations and p the number of regressors)

y

dependent variable (vector or matrix)

lambda

vector of length p of penalization parameters for each regressor

control

list with control parameters: maxIter maximal number of iterations, optTol tolerance for parameter precision, zeroThreshold threshold applied to the estimated coefficients for numerical issues.

XX

optional, precalculated matrix t(X)*X

Xy

optional, precalculated matrix t(X)*y

beta.start

start value for beta

Details

The function implements the Shooting Lasso (Fu, 1998) with variable dependent penalization. The arguments XX and Xy are optional and allow to use precalculated matrices which might improve performance.

Value

coefficients

estimated coefficients by the Shooting Lasso Algorithm

coef.list

matrix of coefficients from each iteration

num.it

number of iterations run

References

Fu, W. (1998). Penalized regressions: the bridge vs the lasso. Journal of Computational and Graphical Software 7, 397-416.


hdm documentation built on May 29, 2024, 3:35 a.m.