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Last data update: 2014.03.03

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

CIGofVAR1

R Documentation

Conditional independence graphs of the VAR(1) model

Description

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

Usage

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. (2015), "Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data", submitted.

Examples

# 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")