LADlasso: LAD-Lasso for Linear Regression

In MTE: Maximum Tangent Likelihood Estimation for Robust Linear Regression and Variable Selection

LAD-Lasso for Linear Regression

Description

LAD-Lasso for Linear Regression

Usage

LADlasso(
  X,
  y,
  beta.ini,
  lambda = NULL,
  adaptive = TRUE,
  intercept = FALSE,
  penalty.factor = rep(1, ncol(X))
)

Arguments

X	design matrix, standardization is recommended.
y	reponse vector
beta.ini	initial estimates of beta. Using unpenalized LAD is recommended under high-dimensional setting.
lambda	regularization parameter of Lasso or adaptive Lasso (if adaptive=TRUE).
adaptive	logical input that indicates if adaptive Lasso is used. Default is TRUE.
intercept	logical input that indicates if intercept needs to be estimated. Default is FALSE.
penalty.factor	can be used to force nonzero coefficients. Default is rep(1, ncol(X)) as in glmnet.

Value

beta	the regression coefficient estimates.
fitted	predicted response.
iter.steps	iteration steps.

Examples

set.seed(2017)
n=200; d=50
X=matrix(rnorm(n*d), nrow=n, ncol=d)
beta=c(rep(2,6), rep(0, 44))
y=X%*%beta+c(rnorm(150), rnorm(30,10,10), rnorm(20,0,100))
output.LADLasso=LADlasso(X, y, beta.ini=LAD(X, y))
beta.est=output.LADLasso$beta

MTE documentation built on April 11, 2023, 6:11 p.m.