genGGM: Generates a GGM small-world network.

Description Usage Arguments Author(s) References

View source: R/simGraph.R

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

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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 <[email protected]>

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.


bootnet documentation built on July 28, 2018, 9:03 a.m.