loglikVAR1: Log-likelihood of the VAR(1) model.
In wvanwie/ragt2ridges: Ridge Estimation of Vector Auto-Regressive (VAR) Processes

Description Usage Arguments Value Author(s) See Also Examples

Log-likelihood of the VAR(1) model specified by the supplied parameters

1	loglikVAR1(Y, A, P, unbalanced=matrix(nrow=0, ncol=2))

`Y`	Three-dimensional `array` containing the data. The first, second and third dimensions correspond to covariates, time and samples, respectively. The data are assumed to be centered covariate-wise.
`A`	A `matrix` \mathbf{A} of autoregression parameters.
`P`	Inverse error covariance `matrix` \mathbf{Ω}_{\varepsilon} (=\mathbf{Σ_{\varepsilon}^{-1}}).
`unbalanced`	A `matrix` with two columns, indicating the unbalances in the design. Each row represents a missing design point in the (time x individual)-layout. The first and second column indicate the time and individual (respectively) specifics of the missing design point.

The log-likelihood of the VAR(1) model with supplied parameters.

Wessel N. van Wieringen <w.vanwieringen@vumc.nl>

ridgeVAR1.

# set dimensions (p=covariates, n=individuals, T=time points)
p <- 3; n <- 4; T <- 10

# set model parameters
SigmaE <- diag(p)/4
A      <- createA(p, "chain")

# generate data
Y <- dataVAR1(n, T, A, SigmaE)

# center data
Y <- centerVAR1data(Y)

# fit VAR(1) model
VAR1hat <- ridgeVAR1(Y, 1, 1)

# evaluate the log-likelihood of this fit.
loglikVAR1(Y, VAR1hat$A, VAR1hat$P)