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).
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"))
Number of nodes
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)"
Neighborhood (see watts.strogatz.game).
Range of partial correlation coefficients to be originally sampled.
A constant as described by Yin and Li (2011).
Proportion of edges to be set positive.
Number of clusters if graph = "cluster"
Type of graph to simulate
Sacha Epskamp <mail@sachaepskamp.com>
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.