nethet (version 1.4.2)

screen_bic.glasso: BIC-tuned glasso with additional thresholding

Description

BIC-tuned glasso with additional thresholding

Usage

screen_bic.glasso(x, include.mean = TRUE, length.lambda = 20,
  lambdamin.ratio = ifelse(ncol(x) > nrow(x), 0.01, 0.001),
  penalize.diagonal = FALSE, plot.it = FALSE,
  trunc.method = "linear.growth", trunc.k = 5, use.package = "huge",
  verbose = FALSE)

Arguments

x

The input data. Needs to be a num.samples by dim.samples matrix.

include.mean

Include mean in likelihood. TRUE / FALSE (default).

length.lambda

Length of lambda path to consider (default=20).

lambdamin.ratio

Ratio lambda.min/lambda.max.

penalize.diagonal

If TRUE apply penalization to diagonal of inverse covariance as well. (default=FALSE)

plot.it

TRUE / FALSE (default)

trunc.method

None / linear.growth (default) / sqrt.growth

trunc.k

truncation constant, number of samples per predictor (default=5)

use.package

'glasso' or 'huge' (default).

verbose

If TRUE, output la.min, la.max and la.opt (default=FALSE).

Value

• Returns a list with named elements 'rho.opt', 'wi', 'wi.orig', Variable rho.opt is the optimal (scaled) penalization parameter (rho.opt=2*la.opt/n). The variables wi and wi.orig are matrices of size dim.samples by dim.samples containing the truncated and untruncated inverse covariance matrix.

Examples

n=50
p=5
x=matrix(rnorm(n*p),n,p)
wihat=screen_bic.glasso(x,length.lambda=5)$wi