BDgraph (version 2.57)

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 )

Arguments

p

The number of variables (nodes).

graph

The undirected graph with options "random", "cluster", "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

The 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.

Value

G

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

References

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138

Mohammadi, A. and Wit, E. C. (2017). BDgraph: An R Package for Bayesian Structure Learning in Graphical Models, arXiv preprint arXiv:1501.05108v5

Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2

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

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845

Pensar, J. et al (2017) Marginal pseudo-likelihood learning of discrete Markov network structures, Bayesian Analysis, 12(4):1195-215

See Also

bdgraph.sim, bdgraph, bdgraph.mpl

Examples

# NOT RUN {
# Generating a 'hub' graph 
adj <- graph.sim( p = 8, graph = "scale-free" )

plot( adj )

adj
# }