| 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.