cv.lasso: Cross-validation

In tathagatabasu/bootlasso: Boot-strap Method for LASSO

Description

Cross-validation for lasso

Usage

cv.lasso(x, y, wt = NULL, ts = NULL, method = lasso_cd, k = 5,
  n_it = 10, df = NULL)

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	lasso optimization function. Three different methods are available to use. method = c(lasso_cd, lasso_sg, lasso_pg). Defaults to lasso_cd
k	number of fold. Default value is 5.
n_it	number of iteration for lasso_cd method. Default value is 10.
df	Degree of freedom. Number of desired variables to be zero. Defaults to NULL

Value

The function returns the following list of outputs

model	The values of coefficients corresponding to each lambda
error	Mean-squared error of the cross-validated model corresponding to each lambda
coeff	The values of coefficients corresponding to minimum mean-squared error
index	Index of the minimum mean-squared error (for internal use)

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