genGGM: Generates a GGM small-world network.
In SachaEpskamp/bootnet: Bootstrap Methods for Various Network Estimation Routines

genGGM

R Documentation

Generates a GGM small-world network.

Description

Simulates a GGM as described by Yin and Li (2011), using the Watts and Strogatz (1998) algorithm for generating the graph structure (see watts.strogatz.game).

Usage

genGGM(Nvar, p = 0, nei = 1, parRange = c(0.5,1), constant = 1.5, propPositive = 0.5,
  clusters = NULL, graph = c("smallworld","random", "scalefree", "hub", "cluster"))

Arguments

`Nvar`	Number of nodes
`p`	Rewiring probability if graph = "smallworld" or "cluster", or connection probability if graph = "random". If cluster, can add multiple p's for each cluster, e.g., "c(.1, .5)"
`nei`	Neighborhood (see `watts.strogatz.game`).
`parRange`	Range of partial correlation coefficients to be originally sampled.
`constant`	A constant as described by Yin and Li (2011).
`propPositive`	Proportion of edges to be set positive.
`clusters`	Number of clusters if graph = "cluster"
`graph`	Type of graph to simulate

Author(s)

Sacha Epskamp <mail@sachaepskamp.com>

References

Yin, J., and Li, H. (2011). A sparse conditional gaussian graphical model for analysis of genetical genomics data. The annals of applied statistics, 5(4), 2630.

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. nature, 393(6684), 440-442.

SachaEpskamp/bootnet documentation built on Oct. 26, 2024, 6:07 p.m.