ic.sglfit {midasml}

Information criteria fit for sg-LASSO

Description

Does information criteria for sg-LASSO regression model.

The function runs sglfit 1 time; computes the path solution in lambda sequence. Solutions for BIC, AIC and AICc information criteria are returned.

Usage

ic.sglfit(x, y, lambda = NULL, gamma = 1.0, gindex = 1:p,  ...)

Arguments

x

T by p data matrix, where T and p respectively denote the sample size and the number of regressors.

y

T by 1 response variable.

lambda

a user-supplied lambda sequence. By leaving this option unspecified (recommended), users can have the program compute its own \lambda sequence based on nlambda and lambda.factor. It is better to supply, if necessary, a decreasing sequence of lambda values than a single (small) value, as warm-starts are used in the optimization algorithm. The program will ensure that the user-supplied \lambda sequence is sorted in decreasing order before fitting the model.

gamma

sg-LASSO mixing parameter. \gamma = 1 gives LASSO solution and \gamma = 0 gives group LASSO solution.

gindex

p by 1 vector indicating group membership of each covariate.

...

Other arguments that can be passed to sglfit.

Details

The sequence of linear regression models implied by λ vector is fit by block coordinate-descent. The objective function is

||y - ια - xβ||²_T + 2λ Ω_γ(β),
where ι∈R^Tenter> and ||u||²_T=<u,u>/T is the empirical inner product. The penalty function Ω_γ(.) is applied on β coefficients and is

Ω_γ(β) = γ |β|₁ + (1-γ)|β|_2,1,
a convex combination of LASSO and group LASSO penalty functions.

Value

ic.sglfit object.

Author(s)

Jonas Striaukas

Examples

set.seed(1)
x = matrix(rnorm(100 * 20), 100, 20)
beta = c(5,4,3,2,1,rep(0, times = 15))
y = x%*%beta + rnorm(100)
gindex = sort(rep(1:4,times=5))
ic.sglfit(x = x, y = y, gindex = gindex, gamma = 0.5, 
  standardize = FALSE, intercept = FALSE)