BDgraph (version 2.70)

bf: Bayes factor between two graphs

Description

Compute the Bayes factor between the structure of two graphs.

Usage

bf( num, den, bdgraph.obj, log = TRUE )

Value

single numeric value, the Bayes factor of the two graph structures num and den.

Arguments

num, den

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

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().

log

character value. If TRUE the Bayes factor is given as log(BF).

Author

Reza Mohammadi

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. (2021). Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models, Journal of the American Statistical Association, tools:::Rd_expr_doi("10.1080/01621459.2021.1996377")

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")

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")

See Also

bdgraph, bdgraph.mpl, compare, bdgraph.sim

Examples

    if (FALSE) {
        # Generating multivariate normal data from a 'circle' graph
        data.sim <- bdgraph.sim( n = 50, p = 6, graph = "circle", vis = TRUE )

        # Running sampling algorithm
        bdgraph.obj <- bdgraph( data = data.sim )

        graph_1 <- graph.sim( p = 6, vis = TRUE )
        
        graph_2 <- graph.sim( p = 6, vis = TRUE )

        bf( num = graph_1, den = graph_2, bdgraph.obj = bdgraph.obj )
    }