mAr (version 1.2-0)

mAr.pca: Multivariate autoregressive analysis in PCA space

Description

Estimation of m-variate AR(p) model in reduced PCA space (for dimensionality reduction) and eigen-decomposition of augmented coefficient matrix

Usage

mAr.pca(x, p, k = dim(x)[2], ...)

Arguments

x

matrix of multivariate time series

p

model order

k

number of principal components to retain

…

additional arguments for specific methods

Value

A list with components:

p

model order

SBC

Schwartz Bayesian Criterion

fraction.variance

fraction of variance explained by the retained components

resid

residuals from the fitted model

eigv

m*p m-dimensional eigenvectors

modes

periods and damping times associated to each eigenmode

References

Neumaier, A. and Schneider, T. (2001), Estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Transactions on Mathematical Software, 27, 1, 27-57.

See Also

mAr.est

Examples

# NOT RUN {
data(sparrows)
A=mAr.est(sparrows,1)$AHat
mAr.eig(A)$modes
mAr.pca(sparrows,1,k=4)$modes

# }