mAr.pca | R Documentation |
Estimation of m-variate AR(p) model in reduced PCA space (for dimensionality reduction) and eigen-decomposition of augmented coefficient matrix
mAr.pca(x, p, k = dim(x)[2], ...)
x |
matrix of multivariate time series |
p |
model order |
k |
number of principal components to retain |
... |
additional arguments for specific methods |
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 |
S. M. Barbosa
Neumaier, A. and Schneider, T. (2001), Estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Transactions on Mathematical Software, 27, 1, 27-57.
mAr.est
data(sparrows) A=mAr.est(sparrows,1)$AHat mAr.eig(A)$modes mAr.pca(sparrows,1,k=4)$modes
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.