common-arguments: List of common arguments In ForeCA: Forecastable Component Analysis

Description

Common arguments used in several functions in this package.

Arguments

 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}(ω_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).

ForeCA documentation built on July 1, 2020, 7:50 p.m.