select {BDgraph}R Documentation

Graph selection


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.


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



A 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().


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


Visualize the selected graph structure.


An adjacency matrix corresponding to the selected graph.


Reza Mohammadi and Ernst Wit


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


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

## End(Not run)

[Package BDgraph version 2.64 Index]