BDgraph (version 2.70)

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, ... )

Value

selected_g

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

p_links

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

K_hat

estimated precision matrix.

Arguments

object

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

round

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

vis

visualize the results.

...

additional plotting parameters for the case vis = TRUE. See plot.graph.

Author

Reza Mohammadi 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")

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")

See Also

bdgraph, bdgraph.mpl

Examples

if (FALSE) {
# 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 = TRUE )
   
summary( bdgraph.obj )
   
summary( bdgraph.obj, vis = FALSE )
}