CIGofVAR1: Conditional independence graphs of the VAR(1) model

Description Usage Arguments Author(s) References See Also Examples

Description

Constructs the global or contemporaneous conditional independence graph (CIG) of the VAR(1) model, as implied by the partial correlations.

Usage

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CIGofVAR1(sparseA, sparseP, type="global")

Arguments

sparseA

A matrix \mathbf{A} of regression 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 <w.vanwieringen@vumc.nl>

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. (2016), "Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data", Biometrical Journal, accepted.

See Also

graphVAR1, sparsify, sparsifyVAR1.

Examples

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# specify VAR(1) model parameters
A <- matrix(c(-0.1, -0.3, 0, 0.5, 0, 0, 0, 0, -0.4), 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.
CIGofVAR1(A, P, type="global")


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