adj2link: Extract links from an adjacency matrix

In BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Extract links from an adjacency matrix

Description

Extract links from an adjacency matrix or an object of calsses "sim" from function bdgraph.sim and "graph" from function graph.sim.

Usage

 adj2link( adj ) 

Arguments

adj	adjacency matrix corresponding to a 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 "sim" from function bdgraph.sim. It can be an object with S3 class "graph" from function graph.sim.

Value

matrix corresponding to the extracted links from graph structure.

Author(s)

Reza Mohammadi [e-mail]

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, 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, 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, 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, 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, doi: 10.1214/18-AOAS1164

Pensar, J. et al (2017) Marginal pseudo-likelihood learning of discrete Markov network structures, Bayesian Analysis, 12(4):1195-215, doi: 10.1214/16-BA1032

See Also

link2adj, graph.sim

Examples

# Generating a 'random' graph 
adj <- graph.sim( p = 6, vis = TRUE )

adj2link( adj )

BDgraph documentation built on Dec. 28, 2022, 1:54 a.m.