BDgraph (version 2.73)

plinks: Estimated posterior link probabilities

Description

Provides the estimated posterior link probabilities for all possible links in the graph.

Usage

plinks( bdgraph.obj, round = 2, burnin = NULL )

Value

An upper triangular matrix which corresponds the estimated posterior probabilities for all possible links.

Arguments

bdgraph.obj

object of S3 class "bdgraph", from function bdgraph. It also can be an object of S3 class "ssgraph", from the function ssgraph::ssgraph() of R package ssgraph::ssgraph().

round

value for rounding all probabilities to the specified number of decimal places.

burnin

number of burn-in iteration to scape.

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")

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845, tools:::Rd_expr_doi("10.1214/18-AOAS1164")

Mohammadi, A. et al (2017). Bayesian modelling of Dupuytren disease by using Gaussian copula graphical models, Journal of the Royal Statistical Society: Series C, 66(3):629-645, tools:::Rd_expr_doi("10.1111/rssc.12171")

See Also

bdgraph, bdgraph.mpl

Examples

if (FALSE) {
# Generating multivariate normal data from a 'circle' graph
data.sim <- bdgraph.sim( n = 70, p = 6, graph = "circle", vis = TRUE )

bdgraph.obj   <- bdgraph( data = data.sim, iter = 10000 )

plinks( bdgraph.obj, round = 2 )
}