degreeComp: Computing the degree of the inferred network

In DWLasso: Degree Weighted Lasso

Description

This function computes the degree of inferred network

Usage

degreeComp(out.mat)

Arguments

out.mat

Symmetric matrix that represents the inferred network

Details

This function computes the degree of estimated networks.

Value

wlasso_norm

Degree vector computed from the inferred network

Examples

library(DWLasso)
library(glmnet)
library(hglasso)


# Generate inverse covariance matrix with 3 hubs
# 20 % of the elements within a hub are zero
# 97 % of the elements that are not within hub nodes are zero
p <- 60 # Number of variables
n <- 40 # Number of samples

hub_number = 3  # Number of hubs

# Generate the adjacency matrix
Theta <- HubNetwork(p,0.97,hub_number,0.2)$Theta

# Generate a data matrix
out <- rmvnorm(n,rep(0,p),solve(Theta))

# Standardize the data
dat <- scale(out)

# Infer the network using weighted nodewise regression
w.mb <- rep(1,p)
adj.mat <- MBLasso(dat,lambda=0.4,w.mb)

# Compute the degree of the inferred network
deg.mat <- degreeComp(adj.mat)

DWLasso documentation built on May 2, 2019, 7:27 a.m.