nethet (version 1.4.2)

screen_cv.glasso: Cross-validated glasso with additional thresholding

Description

Cross-validated glasso with additional thresholding

Usage

screen_cv.glasso(x, include.mean = FALSE, folds = 10, length.lambda = 20,
  lambdamin.ratio = ifelse(ncol(x) > nrow(x), 0.01, 0.001),
  penalize.diagonal = FALSE, trunc.method = "linear.growth", trunc.k = 5,
  plot.it = FALSE, se = FALSE, 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).

folds

Number of folds in the cross-validation (default=10).

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)

trunc.method

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

trunc.k

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

plot.it

TRUE / FALSE (default)

se

default=FALSE.

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', 'w', 'wi', 'wi.orig', 'mu'. Variable rho.opt is the optimal (scaled) penalization parameter (rho.opt=2*la.opt/n). Variable w is the estimated covariance matrix. The variables wi and wi.orig are matrices of size dim.samples by dim.samples containing the truncated and untruncated inverse covariance matrix. Variable mu is the mean of the input data.

Details

Run glasso on a single dataset, using cross-validation to estimate the penalty parameter lambda. Performs additional thresholding (optionally).

Examples

n=50
p=5
x=matrix(rnorm(n*p),n,p)
wihat=screen_cv.glasso(x,folds=2)$wi