### Description

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

### Usage

 link2adj( link, p = NULL )

### Arguments

 link An (2 \times p) matrix or a data.frame corresponding to the links from the graph structure. p The number of nodes of the graph.

### Value

An 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.

### 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

Mohammadi, A. and Wit, E. C. (2015). Bayesian Structure Learning in Sparse Gaussian Graphical Models, Bayesian Analysis, 10(1):109-138

Letac, G., Massam, H. and Mohammadi, R. (2018). The Ratio of Normalizing Constants for Bayesian Graphical Gaussian Model Selection, arXiv preprint arXiv:1706.04416v2

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

Dobra, A. and Mohammadi, R. (2018). Loglinear Model Selection and Human Mobility, Annals of Applied Statistics, 12(2):815-845

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

adj2link, graph.sim

### Examples

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