Lasso.MHLS: Compute lasso estimator

In EAlasso: Simulation Based Inference of Lasso Estimator

Description

Computes lasso, group lasso, scaled lasso, or scaled group lasso solution. The outputs are coefficient-estimate and subgradient. If type = "slasso" or type = "sgrlasso", the output will include the sigma-estimate.

Usage

Lasso.MHLS(X, Y, type, lbd, group = 1:ncol(X), weights = rep(1, max(group)),
  verbose = FALSE, ...)

Arguments

X	predictor matrix.
Y	response vector.
type	type of penalty. Must be specified to be one of the following: "lasso", "grlasso", "slasso" or "sgrlasso".
lbd	penalty term of lasso. By letting this argument be "cv.1se" or "cv.min", users can have the cross-validated lambda that gives either minimum squared error or that is within 1 std error bound.
group	p x 1 vector of consecutive integers describing the group structure. The number of groups should be the same as max(group). Default is group = 1:p , where p is number of covariates.
weights	weight vector with length equal to the number of groups. Default is rep(1, max(group)).
verbose	logical. Only available for type = "slasso" or type = "sgrlasso".
...	auxiliary arguments for lbd = "cv.min", lbd = "cv.1se". See cv.lasso for details.

Details

Computes lasso, group lasso, scaled lasso, or scaled group lasso solution. Users can specify the value of lbd or choose to run cross-validation to get optimal lambda in term of mean squared error.

Value

B0	coefficient estimator.
S0	subgradient.
lbd, weights, group	same as input arguments.

Examples

set.seed(123)
n <- 50
p <- 10
X <- matrix(rnorm(n*p), n)
Y <- X %*% c(1, 1, rep(0, p-2)) + rnorm(n)
#
# lasso
#
Lasso.MHLS(X = X, Y = Y, type = "lasso", lbd = .5)
#
# group lasso
#
Lasso.MHLS(X = X, Y = Y, type = "grlasso", lbd = .5, weights = rep(1,2),
group = rep(1:2, each=5))

EAlasso documentation built on Sept. 1, 2017, 9:03 a.m.