graph.sim {BDgraph} | R Documentation |
Simulating undirected graph structures, including
"random"
, "cluster"
, "scale-free"
, "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. |
G |
The adjacency matrix corresponding to the simulated graph structure, as an object with |
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):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
bdgraph.sim
, bdgraph
, bdgraph.mpl
# Generating a 'hub' graph adj <- graph.sim( p = 8, graph = "scale-free" ) plot( adj ) adj