BDgraph (version 2.72)

select: Graph selection

Description

Provides the selected graph which, based on input, could be a graph with links for which their estimated posterior probabilities are greater than 0.5 (default) or a graph with the highest posterior probability; see examples.

Usage

select( bdgraph.obj, cut = NULL, vis = FALSE )

Value

An adjacency matrix corresponding to the selected graph.

Arguments

bdgraph.obj

matrix in which each element response to the weight of the links. It can be an object of S3 class "bdgraph", from function bdgraph. It can be an object of S3 class "ssgraph", from the function ssgraph::ssgraph() of R package ssgraph::ssgraph().

cut

threshold for including the links in the selected graph based on the estimated posterior probabilities of the links; see the examples.

vis

visualize the selected graph structure.

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 )
   
select( bdgraph.obj )
  
bdgraph.obj <- bdgraph( data = data.sim, save = TRUE )
  
select( bdgraph.obj )
  
select( bdgraph.obj, cut = 0.5, vis = TRUE )
}