randomGVARmodel: Simulate a graphical VAR model

In graphicalVAR: Graphical VAR for Experience Sampling Data

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.

graphicalVAR documentation built on May 29, 2024, 8:55 a.m.