# simulateGraph: Generate sparse Gaussian Graphical Models In GGMselect: Gaussian Graphs Models Selection

## Description

Generate random covariance matrices `C` with sparse inverse. The Gaussian law `N(0,C)` is then a sparse (non-uniform) Gaussian Graphical Model.

## Usage

 `1` ```simulateGraph(p, eta, extraeta = eta/5) ```

## Arguments

 `p` integer. Number of rows and columns of `C`. Should be greater than `1`. `eta` real number in (0,1). Proportion of edges in subgroups. Small values of `eta` give sparse graphs. `extraeta` real number in (0,1). Proportion of edges inter groups.

## Details

More details are available on ../doc/Notice.pdf

## Value

 `G ` p x p matrix. Adjacency matrix of the graph. `Dmax` integer. Maximum degree of the graph. `Neighb ` array of dimension `p x Dmax`. `Neighb[a, ]` contains the indices of the nodes connected to node `a`. `Nnodes` integer. Number of nodes. `C` p x p matrix. Covariance matrix. `PCor` p x p matrix. Partial correlation matrix.

## Author(s)

Bouvier A, Giraud C, Huet S, Verzelen N

## References

Please use `citation("GGMselect")`.

`selectQE`, `selectMyFam`, `selectFast`, `penalty`, `convertGraph`
 ``` 1 2 3 4 5 6 7 8 9 10``` ```# simulate a graph p=30 eta=0.13 Gr <- simulateGraph(p,eta) # plot the graph library(network) par(mfrow=c(1,1)) gV <- network(Gr\$G) plot(gV,jitter=TRUE, usearrows = FALSE, label=1:p,displaylabels=TRUE) ```