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Simulate Multivariate Functional Data

Source: R/simulation.R
simMultiFunData.Rd

Generates multivariate (vector-valued) functional data where each component is simulated via Karhunen-Loeve expansion with potentially different eigenfunctions, eigenvalues, and domains.

Usage

simMultiFunData(
  n,
  argvals,
  M,
  eFun.type = "Fourier",
  eVal.type = "linear",
  mean = NULL,
  seed = NULL
)

Arguments

n

Number of multivariate curves to generate.

argvals

List of numeric vectors, one per component.

M

Integer or integer vector. Number of basis functions per component. If a single integer, used for all components.

eFun.type

Character or character vector specifying eigenfunction type for each component. See eFun for options.

eVal.type

Character or character vector specifying eigenvalue decay for each component. See eVal for options.

mean

List of mean functions (one per component), or NULL.

seed

Optional integer random seed.

Value

A list of class multiFunData containing:

components

List of fdata objects, one per component

n

Number of observations

p

Number of components

See also

simFunData, eFun, eVal

Examples

# Two-component multivariate functional data
t1 <- seq(0, 1, length.out = 100)
t2 <- seq(0, 0.5, length.out = 50)

mfd <- simMultiFunData(
  n = 20,
  argvals = list(t1, t2),
  M = c(5, 3),
  eFun.type = c("Fourier", "Wiener"),
  eVal.type = c("exponential", "linear")
)

# Plot first component
plot(mfd$components[[1]], main = "Component 1")