lasso_weight_opt: Implement weighted Lasso estimation (second step of adaptive...

In zhan-gao/LasForecast: Time series linear predictive regression

Implement weighted Lasso estimation (second step of adaptive lasso) via optimization solver

Description

Estimation function. Tuning parameter inputs needed.

Usage

lasso_weight_opt(
  x,
  y,
  lambda,
  w = NULL,
  intercept = TRUE,
  scalex = FALSE,
  solver = "CVXR",
  rtol = 1e-08,
  verb = 0
)

Arguments

x	Predictor matrix (n-by-p matrix)
y	Response variable
lambda	Shrinkage tuning parameter
w	weights
intercept	A boolean: include an intercept term or not
scalex	A boolean: standardize the design matrix or not
solver	indicate solver in use, c("CVXR", "MOSEK")

Value

A list contains estimated intercept and slope

ahat	Estimated intercept
bhat	Estimated slope

Examples

lasso_weight_opt(x,y)

zhan-gao/LasForecast documentation built on Sept. 18, 2024, 9:40 p.m.