# CIGofVAR2: Conditional independence graphs of the VAR(2) model In wvanwie/ragt2ridges: Ridge Estimation of Vector Auto-Regressive (VAR) Processes

## Description

Constructs the global or contemporaneous conditional independence graph (CIG) of the VAR(2) model, as implied by the partial correlations.

## Usage

 1 CIGofVAR2(sparseA1, sparseA2, sparseP, type="global") 

## Arguments

 sparseA1 A matrix \mathbf{A}_1 of lag one autoregression parameters, which is assumed to be sparse. sparseA2 A matrix \mathbf{A}_2 of lag-two autoregression parameters, which is assumed to be sparse. sparseP Precision matrix \mathbf{Ω}_{\varepsilon} the error, which is assumed to be sparse. type A character indicating whether the global or contemp (contemporaneous) CIG should be plotted.

## Author(s)

Wessel N. van Wieringen <[email protected]>

## References

Dahlhaus (2000), “Graphical interaction models for multivariate time series”, Metrika, 51, 157-172.

Dahlhaus, Eichler (2003), “Causality and graphical models in time series analysis”, Oxford Statistical Science Series, 115-137.

Miok, V., Wilting, S.M., Van Wieringen, W.N. (2018), “Ridge estimation of network models from time-course omics data”, Biometrical Journal, <DOI:10.1002/bimj.201700195>.

CIGofVAR1, graphVAR2, sparsify, sparsifyVAR2.
  1 2 3 4 5 6 7 8 9 10 11 12 13 # specify VAR(2) model parameters A1 <- matrix(c(-0.1, -0.3, 0, 0.5, 0, 0, 0, 0, -0.4), byrow=TRUE, ncol=3) A2 <- matrix(c( 0, 0, 0, 0, 0, 0.2, 0, -0.4, 0), byrow=TRUE, ncol=3) P <- matrix(c( 1, 0.5, 0, 0.5, 1, 0, 0, 0, 1), byrow=TRUE, ncol=3) # adjacency matrix of (global) conditional independencies. CIGofVAR2(A1, A2, P, type="global")