Description Usage Arguments Author(s) References See Also Examples

Prints the information about the selected graph which could be a graph with links for which their estimated posterior probabilities are greater than 0.5 or graph with the highest posterior probability. It provides adjacency matrix, size and posterior probability of the selected graph.

1 2 |

`x` |
An object of |

`round` |
A value to round the probabilities to the specified number of decimal places. |

`...` |
System reserved (no specific usage). |

Reza Mohammadi and Ernst Wit

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*

1 2 3 4 5 6 7 8 9 | ```
## Not run:
# Generating multivariate normal data from a 'random' graph
data.sim <- bdgraph.sim( n = 50, p = 6, vis = TRUE )
bdgraph.obj <- bdgraph( data = data.sim )
print( bdgraph.obj )
## End(Not run)
``` |

BDgraph documentation built on May 20, 2018, 5:03 p.m.

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