nethet (version 1.4.2)

screen_mb: Node-wise Lasso-regressions for GGM estimation

Description

Node-wise Lasso-regressions for GGM estimation

Usage

screen_mb(x, include.mean = NULL, 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, 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.

verbose

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

Value

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

Details

(Meinshausen-Buehlmann approach)

Examples

n=50
p=5
x=matrix(rnorm(n*p),n,p)
wihat=screen_mb(x)$wi