Description Usage Arguments Value Author(s) References See Also Examples

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.

1 |

`bdgraph.obj` |
A matrix in which each element response to the weight of the links.
It can be an object of |

`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. |

An adjacency matrix corresponding to the selected graph.

Reza Mohammadi a.mohammadi@uva.nl 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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## 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)
``` |

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