evaluateVAR1fit: Visualize the fit of a VAR(1) model
In ragt2ridges: Ridge Estimation of Vector Auto-Regressive (VAR) Processes

Description Usage Arguments Value Author(s) See Also Examples

Simple plots for assessment of the fit of an estimated VAR(1) model.

1 2	evaluateVAR1fit(Y, A, SigmaE, unbalanced=NULL, diag=FALSE, fileType="eps", dir=getwd())

`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.
`SigmaE`	Covariance `matrix` of the errors (innovations).
`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.
`diag`	A `logical`, should the diagonal be included in the evaluation of the fit.
`fileType`	A `character` specifying the format in which figures should be save. Either '`pdf`' or '`eps`'.
`dir`	A `character` specifying the directory where plots should be saved.

Plots are saved in the specified directory.

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

evaluateSfit, ridgeVAR1, dataVAR1.

# 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 fit VAR(1) model
## Not run:  evaluateVAR1fit(Y, VAR1hat$A, symm(VAR1hat$P))