BDgraph (version 2.60)

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, 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

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

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
# }