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

Description Usage Arguments Author(s) References See Also Examples

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

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

`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.

Wessel N. van Wieringen <w.vanwieringen@vumc.nl>

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. (2019), “Ridge estimation of network models from time-course omics data”, Biometrical Journal, 61(2), 391-405.

CIGofVAR1, graphVAR2, sparsify, sparsifyVAR2.

# 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")