bic.sparsenet: BIC for weighted sparsenet

In andrewchay/aft-hd: Fit sparse weighted linear regression model using coordinate descent algorithm

Description

Choose the best tuning parameter using BIC criterion.

Usage

bic.sparsenet(result, X, y)

Arguments

result	a list consists of result from weighted.sparsenet.
X	Design matrix of size n by p, where n is the sample size and p is the number of variables. This is the original design matrix.
y	Response vector of length n. This is the original response variable.

Value

A list of results.

betalasso	The fitted vector of coefficients with LASSO penalty. The length is equal to the number of coefficients in the model.
betamcp	The fitted vector of coefficients with MCP penalty. The length is equal to the number of coefficients in the model.
lasso.lambda	The best lambda value chosen by BIC criteria when LASSO is used as the penalty function.
mcp.lambda	The best lambda value chosen by BIC criteria when MCP is used as the penalty function.
mcp.kappa	The best kappa value chosen by BIC criteria when MCP is used as the penalty function.
name.lasso	The names associated with the selected variables by LASSO penalty.
name.mcp	The names associated with the selected variables by MCP penalty.

Author(s)

Hao Chai <[e-mail]>

See Also

cv.sparsenet

Examples

X = matrix(rnorm(8000), nrow = 20)
beta0 = c(rep(10, 5), rep(0, 395))
y = rnorm(20) + X %*% beta0
delta = rep(1, 20)
result = weighted.sparsenet(X, y, delta, n_kappa = 40, kappa0 = 0.99)
bic.sparsenet(result, X, y)

andrewchay/aft-hd documentation built on May 14, 2019, 8:21 a.m.