series |
a T \times K array with T observations from the
K-dimensional time series \mathbf{X}_t. Can be a matrix, data.frame,
or a multivariate ts object.
|
U |
a T \times K array with T observations from the
K-dimensional whitened (whiten)
time series \mathbf{U}_t. Can be a matrix, data.frame, or a
multivariate ts object.
|
mvspectrum.output |
an object of class "mvspectrum" representing
the multivariate spectrum of \mathbf{X}_t (not necessarily normalized).
|
f.U |
multivariate spectrum of class 'mvspectrum' with
normalize = TRUE.
|
algorithm.control |
list; control settings for any iterative ForeCA
algorithm. See complete_algorithm_control for details.
|
entropy.control |
list; control settings for entropy estimation.
See complete_entropy_control for details.
|
spectrum.control |
list; control settings for spectrum estimation.
See complete_spectrum_control for details.
|
entropy.method |
string; method to estimate the entropy from discrete
probabilities p_i; here probabilities are the spectral density
evaluated at the Fourier frequencies,
\widehat{p}_i = \widehat{f}(\omega_i).
|
spectrum.method |
string; method for spectrum estimation; see method
argument in mvspectrum.
|
threshold |
numeric; values of spectral density below threshold are set to
0; default threshold = 0.
|
smoothing |
logical; if TRUE the spectrum will be
smoothed with a nonparametric estimate using gam
and an exponential family (with link = log). Only works
for univariate spectrum. The smoothing
parameter is chosen automatically using generalized cross-validation
(see gam for details). Default: FALSE.
|
base |
logarithm base; entropy is measured in “nats” for
base = exp(1); in “bits” if base = 2 (default).
|