fsd.sfarma: Simulate a spatial functional ARMA process

In kuenzer/fsd: Functional Spatial Data: Spatial Functional PCA

Description

This function is used to simulate a sample from a spatial functional ARMA (SFARMA) process using a normal or t-distributed noise process.

Usage

fsd.sfarma(
  n,
  Sigma = NULL,
  ARfilter = NULL,
  MAfilter = NULL,
  burnin = 30,
  basisobj = NULL,
  noise = "normal",
  do.fixed.point = FALSE,
  max.iter = 50,
  eps = 1e-05
)

Arguments

n	the sample size.
Sigma	the covariance matrix of the noise.
ARfilter	the functional spatial filter of the autoregressive part.
MAfilter	the functional spatial filter of the moving-average part.
burnin	the number of grid points to add on each side of the sample to achieve stationarity.
basisobj	the basis of the functional data.
noise	the noise distribution to use. Either "normal" or an integer for the degrees of freedom of a multivariate Student's t-distribution.
do.fixed.point	whether to always use a fixed point iteration.
max.iter	the maximum number of iterations in the fixed point iteration.
eps	the stopping criterion for the fixed point iteration.

Details

Please note that convergence in the fixed point iteration depends strongly on the sample size and the structure of the AR filter. For certain choices of parameters, even more than n iterations may be needed.

Value

one sample of the SFARMA process.

See Also

fsd.fd

Examples

## Not run: 
fsd.sfarma(n, Sigma, ARfilter, MAfilter, basisobj)

## End(Not run)

kuenzer/fsd documentation built on July 21, 2020, 1:57 p.m.