summary.bdgraph: Summary function for `S3` class `"bdgraph"`

Description

Provides a summary of the results for function bdgraph.

Usage

# S3 method for bdgraph
summary( object, round = 2, vis = TRUE, ... )

Arguments

object

An object of S3 class "bdgraph", from function bdgraph.

round

A value for rounding all probabilities to the specified number of decimal places.

vis

Visualize the results.

…

System reserved (no specific usage).

Value

best.graph

The adjacency matrix corresponding to the selected graph which has the highest posterior probability.

p_links

An upper triangular matrix corresponding to the posterior probabilities of all possible links.

K_hat

The estimated precision matrix.

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

Examples

Run this code

# 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 )
   
summary( bdgraph.obj )
   
bdgraph.obj <- bdgraph( data = data.sim, save.all = TRUE )
   
summary( bdgraph.obj )
   
summary( bdgraph.obj, vis = FALSE )
# }