ragt2ridges (version 0.3.4)

CIGofVAR2: Conditional independence graphs of the VAR(2) model

Description

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

Usage

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 \(\mathbf{\Omega}_{\varepsilon}\) the error, which is assumed to be sparse.

type

A character indicating whether the global or contemp (contemporaneous) CIG should be plotted.

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

See Also

CIGofVAR1, graphVAR2, sparsify, sparsifyVAR2.

Examples

# NOT RUN {
# 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")
# }