CIGofVAR1: Conditional independence graphs of the VAR(1) model In ragt2ridges: Ridge Estimation of Vector Auto-Regressive (VAR) Processes

Description

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

Usage

 1 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. (2017), "Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data", Biometrical Journal, 59(1), 172-191.

graphVAR1, sparsify, sparsifyVAR1.

Examples

 1 2 3 4 5 6 # 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") 

ragt2ridges documentation built on May 19, 2017, 11:49 p.m.
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