BDgraph (version 2.43)

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 = NULL, number.g = 1, layout = layout.circle, ... )

Arguments

x

An 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

The number of graphs with the highest probabilities.

layout

The vertex placement algorithm which is according to igraph package.

…

System reserved (no specific usage).

References

Mohammadi, A. and E. Wit (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138

Mohammadi, A. and E. Wit (2015). BDgraph: An R Package for Bayesian Structure Learning in Graphical Models, arXiv preprint arXiv:1501.05108

Dobra, A. and A. Mohammadi (2017). Loglinear Model Selection and Human Mobility, arXiv preprint arXiv:1711.02623

Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C

Mohammadi, A., Massam H., and G. Letac (2017). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416

See Also

bdgraph

Examples

# NOT RUN {
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 6, size = 7, vis = TRUE )
   
bdgraph.obj <- bdgraph( data = data.sim )
   
plot( bdgraph.obj )
   
bdgraph.obj <- bdgraph( data = data.sim, save.all = TRUE )
   
plot( bdgraph.obj, number.g = 4 )
  
plot( bdgraph.obj, cut = 0.4 )
# }