pgraph function

Posterior probabilities of the graphs

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

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

Arguments

• bdgraph.obj: object of S3 class "bdgraph", from function bdgraph.
• number.g: number of graphs with the highest posterior probabilities to be shown. This option is ignored if 'adj' is specified.
• adj: 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.

Returns

• selected_g: adjacency matrices which corresponding to the graphs with the highest posterior probabilities.

• prob_g: vector of the posterior probabilities of the graphs corresponding to 'selected\_g'.

References

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, tools:::Rd_expr_doi("10.18637/jss.v089.i03")

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138, tools:::Rd_expr_doi("10.1214/14-BA889")

Mohammadi, R., Massam, H. and Letac, G. (2023). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, tools:::Rd_expr_doi("10.1080/01621459.2021.1996377")

Author(s)

Reza Mohammadi a.mohammadi@uva.nl and Ernst Wit

See Also

bdgraph, bdgraph.mpl

Examples

## 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 = TRUE )
   
# Estimated posterior probability of the true graph
pgraph( bdgraph.obj, adj = data.sim )
   
# Estimated posterior probability of first and second graphs with highest probabilities
pgraph( bdgraph.obj, number.g = 2 )
## End(Not run)