plot.bdgraph: Plot function for `S3` class "`bdgraph`"

Description

Visualizes structure of the selected graphs which could be a graph with links for which their estimated posterior probabilities are greater than 0.5 or graph with the highest posterior probability.

Usage

# S3 method for bdgraph
plot(x, cut = 0.5, number.g = NULL, main = NULL, 
           layout = igraph::layout_with_fr, vertex.size = 2, vertex.color = "orange", 
           vertex.frame.color = "orange", vertex.label = NULL, vertex.label.dist = 0.5, 
           vertex.label.color = "blue", edge.color = "lightblue", ...)

Arguments

x: object of S3 class "bdgraph", from function bdgraph.
cut: threshold for including the links in the selected graph based on the estimated posterior probabilities of the links; See the examples.
number.g: number of graphs with the highest probabilities. This option works for the case running function bdgraph() with option save = TRUE; See the examples.
main: Graphical parameter (see plot).
layout: vertex placement which is according to R package igraph; For different layouts, see layout of R package igraph.
vertex.size: vertex size which is according to R package igraph.
vertex.color: vertex color which is according to R package igraph.
vertex.frame.color: vertex frame color which is according to R package igraph.
vertex.label: vertex label. The default vertex labels are the vertex ids.
vertex.label.dist: vertex label distance which is according to R package igraph.
vertex.label.color: vertex label color which is according to R package igraph.
edge.color: edge color which is according to R package igraph.
...: additional plotting parameters. For the complete list, see igraph.plotting of R package igraph.

Author

Reza Mohammadi a.mohammadi@uva.nl and Ernst Wit

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, tools:::Rd_expr_doi("10.18637/jss.v089.i03")

Examples

Run this code

if (FALSE) {
set.seed(100)

# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim(n = 100, p = 15, graph = "random", prob = 0.2, vis = TRUE)
   
bdgraph.obj <- bdgraph(data = data.sim)
   
plot(bdgraph.obj)
   
bdgraph.obj <- bdgraph(data = data.sim, save = TRUE)
   
plot(bdgraph.obj, cut = 0.5)

plot(bdgraph.obj, number.g = 4)
}