pgraph: Posterior probabilities of the graphs

Description

Provides the estimated posterior probabilities for the most likely graphs or a specific graph.

Usage

pgraph( bdgraph.obj, number.g = 4, adj_g = NULL )

Arguments

bdgraph.obj

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

number.g

The number of graphs with the highest posterior probabilities to be shown (default is 4). This option is ignored if 'adj_g' is specified.

adj_g

An adjacency matrix corresponding to a graph structure. It is an upper triangular matrix in which \(a_{ij}=1\) if there is a link between notes \(i\) and \(j\), otherwise \(a_{ij}=0\). It also can be an object of S3 class "sim", from function bdgraph.sim.

Value

selected_g

The graphs with the highest posterior probabilities.

prob_g

A vector of the posterior probabilities of the graphs corresponding to 'selected\_g'.

References

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:1501.05108 Mohammadi, A., F. Abegaz Yazew, E. van den Heuvel, and E. Wit (2016). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C

Examples

Run this code

## Not run: ------------------------------------
# # Generating multivariate normal data from a 'random' graph
# data.sim <- bdgraph.sim( n = 50, p = 6, size = 6, vis = TRUE )
#    
# bdgraph.obj <- bdgraph( data = data.sim, save.all = TRUE )
#    
# # Estimated posterior probability of the true graph
# pgraph( bdgraph.obj, adj_g = data.sim )
#    
# # Estimated posterior probability of first and second graphs with highest probabilities
# pgraph( bdgraph.obj, number.g = 2 )
## ---------------------------------------------