reg.fd: Sample Regular Functional Data

In synfd: Synthesize Dense or Sparse Functional Data/Snippets

Description

Sample Regular Functional Data

Usage

reg.fd(
  mu = 0,
  X = wiener.process(),
  n = 100,
  m = 51,
  sig = NULL,
  snr = 5,
  domain = c(0, 1),
  grid = seq(domain[1], domain[2], length.out = m)
)

Arguments

mu	function, scalar or a vector defining the mean function; default value: 0.
X	centered stochastic process defined by a function of the form X(tObs,n) and returning n*length(tObs) matrix, where each row represents observations from a trajectory. Default value: wiener.process().
n	sample size; default value: 100.
m	sampling rate; ignored if grid is specified; default value: 51.
sig	standard deviation of measurement errors; if NULL then determined by snr.
snr	signal to noise ratio to determine sig; default value: 5.
domain	the domain; default value: c(0,1).
grid	vector of design points; default value: NULL.

Value

a list with the following members

t

a vector of design points sorted in increasing order.

y

n*m matrix; each row represents observations from a trajectory.

and with attributes sig, snr, domain, grid and

y0

n*m matrix of observations without measurement errors.

Examples

Y <- reg.fd()
Y <- reg.fd(mu=1, X=gaussian.process(), n=10, m=20)
Y <- reg.fd(mu=cos, X=kl.process(),n=100, m=20)
Y <- reg.fd(mu=cos, X=kl.process(distribution='EXPONENTIAL'),n=100, m=20)

synfd documentation built on July 1, 2020, 6:04 p.m.