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

## Description

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

## Usage

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

## Arguments

 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.

## Value

Plots are saved in the specified directory.

## Author(s)

Wessel N. van Wieringen <[email protected]>

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # 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))