graph.sim {BDgraph} R Documentation

## Graph simulation

### Description

Simulating undirected graph structures, including `"random"`, `"cluster"`, `"scale-free"`, `"lattice"`, `"hub"`, `"star"`, and `"circle"`.

### Usage

```graph.sim( p = 10, graph = "random", prob = 0.2, size = NULL, class = NULL, vis = FALSE )
```

### Arguments

 `p` The number of variables (nodes). `graph` The undirected graph with options `"random"`, `"cluster"`, `"scale-free"`, `"lattice"`, `"hub"`, `"star"`, and `"circle"`. It also could be an adjacency matrix corresponding to a graph structure (an upper triangular matrix in which g_{ij}=1 if there is a link between notes i and j, otherwise g_{ij}=0). `prob` If `graph="random"`, it is the probability that a pair of nodes has a link. `size` The number of links in the true graph (graph size). `class` If `graph="cluster"`, it is the number of classes. `vis` Visualize the true graph structure.

### Value

The adjacency matrix corresponding to the simulated graph structure, as an object with `S3` class `"graph"`.

### 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

`bdgraph.sim`, `bdgraph`, `bdgraph.mpl`

### Examples

```# Generating a 'hub' graph
adj <- graph.sim( p = 8, graph = "scale-free" )