Completes algorithm, entropy, and spectrum control lists.
complete_algorithm_control(algorithm.control = list(max.iter = 50, num.starts
= 10, tol = 0.001, type = "EM"))complete_entropy_control(entropy.control = list(base = NULL, method = "MLE",
prior.probs = NULL, prior.weight = 0.001, threshold = 0), num.outcomes)
complete_spectrum_control(spectrum.control = list(kernel = NULL, method =
c("wosa", "direct", "multitaper", "mvspec", "ar", "pgram"), smoothing =
FALSE))
list; control parameters for any iterative ForeCA algorithm.
list; control settings for entropy estimation.
positive integer; number of outcomes for the discrete probability distribution. Must be specified (no default value).
list; control settings for spectrum estimation.
A list with fully specified algorithm, entropy, or spectrum controls.
Default values are only added if the input {spectrum,entropy,algorithm}.control
list does not already set this value.
complete_algorithm_control
returns a list containing:
maximum number of iterations; default: 50
.
number of random starts to avoid local optima; default: 10
.
tolerance for when convergence is reached in any iterative
ForeCA algorithm; default: 1e-03
.
string; type of algorithm. Default: 'EM'
.
complete_entropy_control returns a list with:
logarithm base for the entropy.
string; method to estimate entropy; default: "MLE"
.
prior distribution; default: uniform
rep(1 / num.outcomes, num.outcomes)
.
weight of the prior distribution; default: 1e-3
.
non-negative float; set probabilities below threshold to
zero; default: 0
.
complete_spectrum_control returns a list containing:
R function; function to weigh each Fourier frequency \(\lambda\);
default: NULL
(no re-weighting).
string; method to estimate the spectrum; default:
'wosa'
if sapa is installed, 'mvspec'
if only astsa is installed, and 'pgram'
if
neither is installed.
logical; default: FALSE
.
Available methods for spectrum estimation are (alphabetical order)
autoregressive spectrum fit via spec.ar
;
only for univariate time series.
raw periodogram using SDF
.
tapering the periodogram using SDF
.
smoothed estimate using mvspec
; many tuning parameters
are available -- they can be passed as additional arguments (...
)
to mvspectrum
.
uses mvpgram
; is the same as the
'direct'
method, but does not rely on the SDF
package.
Welch overlapping segment averaging (WOSA) using SDF
.
Setting smoothing = TRUE will smooth the estimated spectrum (again); this option is only available for univariate time series/spectra.