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 <

Value

selected_gThe graphs with the highest posterior probabilities.
prob_gA 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 (2015). Bayesian Gaussian Copula Graphical Modeling for Dupuytren Disease, arXiv:1501.04849

Examples

Run this code

# 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 )