graph.sim {BDgraph}  R Documentation 
Simulating undirected graph structures, including
"random"
, "cluster"
, "scalefree"
, "lattice"
, "hub"
, "star"
, and "circle"
.
graph.sim( p = 10, graph = "random", prob = 0.2, size = NULL, class = NULL, vis = FALSE )
p 
The number of variables (nodes). 
graph 
The undirected graph with options

prob 
If 
size 
The number of links in the true graph (graph size). 
class 
If 
vis 
Visualize the true graph structure. 
The adjacency matrix corresponding to the simulated graph structure, as an object with S3
class "graph"
.
Reza Mohammadi a.mohammadi@uva.nl
Mohammadi, R. and Wit, E. C. (2019). BDgraph: An R
Package for Bayesian Structure Learning in Graphical Models, Journal of Statistical Software, 89(3):130
Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109138
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):629645
Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815845
Pensar, J. et al (2017) Marginal pseudolikelihood learning of discrete Markov network structures, Bayesian Analysis, 12(4):1195215
bdgraph.sim
, bdgraph
, bdgraph.mpl
# Generating a 'hub' graph adj < graph.sim( p = 8, graph = "scalefree" ) plot( adj ) adj