simulate: Randomly generate a adjacency matrix based on which to...

In hoenlab/SBIC: Structural Bayesian Information Criterion for Graphical Models

Description

According to a given edge density, first generate the adjacency matrix P of a graph. Based on P, the simulated multivariate normal data is generated with mean zero and a specified given precision matrix

Usage

simulate(n, p, m1, m2)

Arguments

n	Sample size
p	The number of vertices in graph or the number of variables
m1	The number of edges in the true graph
m2	The number of elements in adjacency matrix that stay in different states, i.e., 0 or 1, in true and prior graphs

Value

A list including the simulated data, real adjacency matrix and a prior adjacency matrix

data	simulated data
realnetwork	real adjacency matrix
priornetowrk	prior adjacency matrix

Author(s)

Jie Zhou

Examples

  set.seed(1)
  d=simulate(n=100,p=200, m1=100, m2=30)
  d$data
  d$realnetwork
  d$priornetwork
  

hoenlab/SBIC documentation built on March 6, 2021, 11:58 a.m.