Autoregressive model coefficients - Yule-Walker method
compute autoregressive all-pole model parameters using the Yule-Walker method.
aryule(x, p)
x |
input data, specified as a numeric or complex vector or matrix. In case of a vector it represents a single signal; in case of a matrix each column is a signal. |
p |
model order; number of poles in the AR model or limit to the number
of poles if a valid criterion is provided. Must be smaller than the length
of |
aryule uses the Levinson-Durbin recursion on the biased estimate of
the sample autocorrelation sequence to compute the parameters.
A list containing the following elements:
vector or matrix containing (p + 1) autoregression
coefficients. If x is a matrix, then each row of a corresponds to
a column of x. a has p + 1 columns.
white noise input variance, returned as a vector. If x is
a matrix, then each element of e corresponds to a column of x.
Reflection coefficients defining the lattice-filter embodiment
of the model returned as vector or a matrix. If x is a matrix,
then each column of k corresponds to a column of x.
k has p rows.
The power spectrum of the resulting filter can be plotted with
pyulear(x, p), or you can plot it directly with
ar_psd(a,v,...).
Paul Kienzle, pkienzle@users.sf.net,
Peter V. Lanspeary, pvl@mecheng.adelaide.edu.au.
Conversion to R by Geert van Boxtel, gjmvanboxtel@gmail.com.
a <- Arma(1, c(1, -2.7607, 3.8106, -2.6535, 0.9238)) y <- filter(a, rnorm(1024)) coefs <- aryule(y, 4)
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