imp.lasso.tuning: Tuning of LASSO coefficients bounds

In tathagatabasu/bootlasso: Boot-strap Method for LASSO

Description

Tuning of the model over lambda using lower and upper estimates of LASSO coefficients using optimization over weights

Usage

imp.lasso.tuning(x, y, wtl, wtu, ts = NULL, method = lasso_optim_cd,
  n_it = 10)

Arguments

x	predictors
y	response
wtl	lower bound of the weights for the coefficients of weighted LASSO
wtu	upper bound of the weights for the coefficients of weighted LASSO
ts	stepsize for proximal gradient and sub-gradient method (use opt_ts() to generate stepsize). Defaults to NULL
method	lasso optimization function. Three different methods are available to use. method = c(lasso_optim_cd, lasso_optim_sg, lasso_optim_pg). Defaults to lasso_optim_cd
n_it	number of iteration for lasso_optim_cd method. Default value is 10.
lambdas	values of the penalty parameter

Value

The function returns the lower and upper estimates of LASSO coefficients.

tathagatabasu/bootlasso documentation built on Aug. 9, 2019, 1:07 a.m.