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, 
           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 "graph", from function graph.sim.
cut: 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).
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

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")

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138, tools:::Rd_expr_doi("10.1214/14-BA889")

Mohammadi, R., Massam, H. and Letac, G. (2021). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, tools:::Rd_expr_doi("10.1080/01621459.2021.1996377")

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845, tools:::Rd_expr_doi("10.1214/18-AOAS1164")

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, tools:::Rd_expr_doi("10.1111/rssc.12171")

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

Examples

Run this code

# Generating a 'scale-free' graph 
adj <- graph.sim( p = 20, graph = "scale-free" )

plot( adj )