mAr.pca {mAr}R Documentation

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

Author(s)

S. M. Barbosa

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


data(sparrows)
A=mAr.est(sparrows,1)$AHat
mAr.eig(A)$modes
mAr.pca(sparrows,1,k=4)$modes


[Package mAr version 1.1-2 Index]