genGGM | R Documentation |

Simulates a GGM as described by Yin and Li (2011), using the Watts and Strogatz (1998) algorithm for generating the graph structure (see `watts.strogatz.game`

).

```
genGGM(Nvar, p = 0, nei = 1, parRange = c(0.5,1), constant = 1.5, propPositive = 0.5,
clusters = NULL, graph = c("smallworld","random", "scalefree", "hub", "cluster"))
```

`Nvar` |
Number of nodes |

`p` |
Rewiring probability if graph = "smallworld" or "cluster", or connection probability if graph = "random". If cluster, can add multiple p's for each cluster, e.g., "c(.1, .5)" |

`nei` |
Neighborhood (see |

`parRange` |
Range of partial correlation coefficients to be originally sampled. |

`constant` |
A constant as described by Yin and Li (2011). |

`propPositive` |
Proportion of edges to be set positive. |

`clusters` |
Number of clusters if graph = "cluster" |

`graph` |
Type of graph to simulate |

Sacha Epskamp <mail@sachaepskamp.com>

Yin, J., and Li, H. (2011). A sparse conditional gaussian graphical model for analysis of genetical genomics data. The annals of applied statistics, 5(4), 2630.

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. nature, 393(6684), 440-442.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.