screen_shrink: Shrinkage approach for estimating Gaussian graphical model

In nethet: A bioconductor package for high-dimensional exploration of biological network heterogeneity

Description

Shrinkage approach for estimating Gaussian graphical model

Usage

screen_shrink(x, include.mean = NULL, trunc.method = "linear.growth",
  trunc.k = 5)

Arguments

x	The input data. Needs to be a num.samples by dim.samples matrix.
include.mean	Include mean in likelihood. TRUE / FALSE (default).
trunc.method	None / linear.growth (default) / sqrt.growth
trunc.k	truncation constant, number of samples per predictor (default=5)

Value

Returns a list with named elements 'rho.opt', 'wi', 'wi.orig'. Variable rho.opt=NULL (no tuning parameter involved). The variables wi and wi.orig are matrices of size dim.samples by dim.samples containing the truncated and untruncated inverse covariance matrix.

Author(s)

n.stadler

nethet documentation built on Nov. 8, 2020, 6:54 p.m.