BDgraph (version 2.62)

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, ... )

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. This option works for the case running function bdgraph() with option save = TRUE; See the examples.

…

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

See Also

bdgraph, bdgraph.mpl

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 = TRUE )
   
plot( bdgraph.obj, number.g = 4 )
  
plot( bdgraph.obj, cut = 0.4 )
# }