complete-controls {ForeCA} | R Documentation |
Completes several control settings
Description
Completes algorithm, entropy, and spectrum control lists.
Usage
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("mvspec", "pspectrum", "ar",
"pgram"), smoothing = FALSE)
)
Arguments
algorithm.control |
list; control parameters for any iterative ForeCA algorithm. |
entropy.control |
list; control settings for entropy estimation. |
num.outcomes |
positive integer; number of outcomes for the discrete probability distribution. Must be specified (no default value). |
spectrum.control |
list; control settings for spectrum estimation. |
Value
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:
max.iter |
maximum number of iterations; default: |
num.starts |
number of random starts to avoid local optima; default: |
tol |
tolerance for when convergence is reached in any iterative
ForeCA algorithm; default: |
type |
string; type of algorithm. Default: |
complete_entropy_control
returns a list with:
base |
logarithm base for the entropy. |
method |
string; method to estimate entropy; default: |
prior.probs |
prior distribution; default: uniform
|
prior.weight |
weight of the prior distribution; default: |
threshold |
non-negative float; set probabilities below threshold to
zero; default: |
complete_spectrum_control
returns a list containing:
kernel |
R function; function to weigh each Fourier frequency |
method |
string; method to estimate the spectrum; default:
|
smoothing |
logical; default: |
Available methods for spectrum estimation are (alphabetical order)
"ar" |
autoregressive spectrum fit via |
"mvspec" |
smoothed estimate using |
"pgram" |
raw periodogram using |
"pspectrum" |
advanced non-parametric estimation of a tapered power
spectrum using |
Setting smoothing = TRUE
will smooth the estimated spectrum
(again); this option is only available for univariate time series/spectra.
See Also
mvspectrum
, discrete_entropy
,
continuous_entropy