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

Description Usage Arguments Author(s) References See Also Examples

View source: R/CIGofVAR2.r

Description

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

Usage

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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{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 <[email protected]>

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. (2018), “Ridge estimation of network models from time-course omics data”, Biometrical Journal, <DOI:10.1002/bimj.201700195>.

See Also

CIGofVAR1, graphVAR2, sparsify, sparsifyVAR2.

Examples

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# 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")

wvanwie/ragt2ridges documentation built on May 4, 2019, 12:03 p.m.