BDgraph (version 2.62)

plot.graph: Plot function for S3 class "graph"

Description

Visualizes structure of the graph.

Usage

# S3 method for graph
plot( x, cut = 0.5, mode = "undirected", diag = FALSE, main = NULL, 
                      vertex.color = "white", vertex.label.color = 'black', ... )

Arguments

x

An object of S3 class "graph", from function graph.sim.

cut

This option is for the case where input 'x' is the object of class "bdgraph" or "ssgraph". Threshold for including the links in the selected graph based on the estimated posterior probabilities of the links.

mode

Type of graph which is according to R package igraph.

diag

Logical which is according to R package igraph.

main

Graphical parameter (see plot).

vertex.color

The vertex color which is according to R package igraph.

vertex.label.color

The vertex label color which is according to R package igraph.

…

System reserved (no specific usage).

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

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

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

Mohammadi, A. and Dobra, A. (2017). The R Package BDgraph for Bayesian Structure Learning in Graphical Models, ISBA Bulletin, 24(4):11-16

See Also

graph.sim, bdgraph.sim

Examples

# NOT RUN {
# Generating a 'random' graph 
adj <- graph.sim( p = 10, graph = "random" )
plot( adj )
adj
# }