MakeGPFunctionalData: Create a Dense Functional Data sample for a Gaussian process

In hadjipantelis/tPACE: Functional Data Analysis and Empirical Dynamics

Create a Dense Functional Data sample for a Gaussian process

Description

For a Gaussian process, create a dense functional data sample of size n over a [0,1] support.

Usage

MakeGPFunctionalData(
  n,
  M = 100,
  mu = rep(0, M),
  K = 2,
  lambda = rep(1, K),
  sigma = 0,
  basisType = "cos"
)

Arguments

n	number of samples to generate
M	number of equidistant readings per sample (default: 100)
mu	vector of size M specifying the mean (default: rep(0,M))
K	scalar specifying the number of basis to be used (default: 2)
lambda	vector of size K specifying the variance of each components (default: rep(1,K))
sigma	The standard deviation of the Gaussian noise added to each observation points.
basisType	string specifying the basis type used; possible options are: 'sin', 'cos' and 'fourier' (default: 'cos') (See code of 'CreateBasis' for implementation details.)

Value

A list containing the following fields:

Y	A vector of noiseless observations.
Yn	A vector of noisy observations if sigma > 0.

hadjipantelis/tPACE documentation built on July 4, 2024, 8:10 a.m.