boot.lasso: Bootstrap for LASSO.

In tathagatabasu/bootlasso: Boot-strap Method for LASSO

Description

Function for getting bootstrap estimates for LASSO.

Usage

boot.lasso(x, y, wt = NULL, ts = NULL, method = lasso_cd, k = 5,
  n_it = 10, df = NULL, n.sim = 500)

Arguments

x	predictors
y	response
wt	weights for the coefficients of weighted LASSO. Defaults to NULL
ts	stepsize for proximal gradient and sub-gradient method (use opt_ts() to generate stepsize). Defaults to NULL
method	optimization method. Three different methods are available to use. method = c(lasso_cd, lasso_sg, lasso_pg). Defaults to lasso_cd
k	no. of folds for cross-validation. Default value is 5.
n_it	no. of iteration for lasso_cd optimization. Default value is 10.
df	degree of freedom. Number of desired variables to be zero. Defaults to NULL
n.sim	no. of bootstrap replicates. Default value is 500.

Value

The summary frame of the bootstrap comprising mean, median, bias, standard deviation and confidence intervals.

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