BDgraph (version 2.74)

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 = NULL)

Value

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'.

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.

Author

Reza Mohammadi and Ernst Wit

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")

See Also

bdgraph, bdgraph.mpl

Examples

if (FALSE) {
# 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)
}