BDgraph (version 2.70)

graph.sim: Graph simulation

Description

Simulating undirected graph structures, including "random", "cluster", "scale-free", "lattice", "hub", "star", and "circle".

Usage

graph.sim( p = 10, graph = "random", prob = 0.2, size = NULL, class = NULL, vis = FALSE,
           rewire = 0.05 )

Value

The adjacency matrix corresponding to the simulated graph structure, as an object with S3 class "graph".

Arguments

p

number of variables (nodes).

graph

undirected graph with options "random", "cluster", "smallworld", "scale-free", "lattice", "hub", "star", and "circle". It also could be an adjacency matrix corresponding to a graph structure (an upper triangular matrix in which \(g_{ij}=1\) if there is a link between notes \(i\) and \(j\), otherwise \(g_{ij}=0\)).

prob

if graph = "random", it is the probability that a pair of nodes has a link.

size

number of links in the true graph (graph size).

class

if graph = "cluster", it is the number of classes.

vis

visualize the true graph structure.

rewire

rewiring probability for smallworld network. Must be between 0 and 1.

Author

Reza Mohammadi and Alexander Christensen

References

Mohammadi, R. and Wit, E. C. (2019). BDgraph: An R Package for Bayesian Structure Learning in Graphical Models, Journal of Statistical Software, 89(3):1-30, tools:::Rd_expr_doi("10.18637/jss.v089.i03")

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138, tools:::Rd_expr_doi("10.1214/14-BA889")

Mohammadi, R., Massam, H. and Letac, G. (2021). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, tools:::Rd_expr_doi("10.1080/01621459.2021.1996377")

Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C, 66(3):629-645, tools:::Rd_expr_doi("10.1111/rssc.12171")

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845, tools:::Rd_expr_doi("10.1214/18-AOAS1164")

Pensar, J. et al (2017) Marginal pseudo-likelihood learning of discrete Markov network structures, Bayesian Analysis, 12(4):1195-215, tools:::Rd_expr_doi("10.1214/16-BA1032")

See Also

bdgraph.sim, bdgraph, bdgraph.mpl

Examples

# Generating a 'hub' graph 
adj <- graph.sim( p = 8, graph = "scale-free" )

plot( adj )

adj