R: Simulate a graphical VAR model

randomGVARmodel {graphicalVAR} | R Documentation

## Simulate a graphical VAR model

### Description

Simulates an contemporaneous and temporal network using the method described by Yin and Li (2001)

### Usage

    randomGVARmodel(Nvar, probKappaEdge = 0.1, probKappaPositive = 0.5, probBetaEdge = 0.1,
          probBetaPositive = 0.5, maxtry = 10, kappaConstant = 1.1)

### Arguments

`Nvar` | Number of variables
`probKappaEdge` | Probability of an edge in contemporaneous network
`probKappaPositive` | Proportion of positive edges in contemporaneous network
`probBetaEdge` | Probability of an edge in temporal network
`probBetaPositive` | Propotion of positive edges in temporal network
`maxtry` | Maximum number of attempts to create a stationairy VAR model
`kappaConstant` | The constant used in making kappa positive definite. See Yin and Li (2001)

### Details

The resulting simulated networks can be plotted using the plot method.

### Value

A list containing:

`kappa` | True kappa structure (residual inverse variance-covariance matrix)
`beta` | True beta structure
`PCC` | True partial contemporaneous correlations
`PDC` | True partial temporal correlations

### Author(s)

Sacha Epskamp

### References

Yin, J., & Li, H. (2011). A sparse conditional gaussian graphical model for analysis of genetical genomics data. The annals of applied statistics, 5(4), 2630-2650.

[Package graphicalVAR version 0.3 Index]