mfData {roahd}

S3 class for multivariate functional datasets

Description

This function implements a constructor for elements of S3 class mfData, aimed at implementing a representation of a multivariate functional dataset.

Usage

mfData(grid, Data_list)

Arguments

grid

the (evenly spaced) grid over which the functional dataset is defined.

Data_list

a list containing the L components of the multivariate functional dataset, defined as 2D data structures (e.g. matrix or array) having as rows the N observations and as columns the P measurements on the grid provided by grid.

Details

The functional dataset is represented as a collection of L components, each one an object of class fData. Each component must contain elements defined on the same grid as the others, and must contain the same number of elements (N).

Value

The function returns a S3 object of class mfData, containing the following elements:

• "N": the number of elements in the dataset;

• "L": the number of components of the functional dataset;

• "P": the number of points in the 1D grid over which elements are measured;

• "t0": the starting point of the 1D grid;

• "tP": the ending point of the 1D grid;

• "fDList": the list of fData objects representing the L components as corresponding univariate functional datasets.

See Also

fData, generate_gauss_fdata, generate_gauss_mfdata

Examples

# Defining parameters
N = 1e2

P = 1e3

t0 = 0
t1 = 1

# Defining the measurement grid
grid = seq( t0, t1, length.out = P )

# Generating an exponential covariance matrix to be used in the simulation of
# the functional datasets (see the related help for details)
C = exp_cov_function( grid, alpha = 0.3, beta = 0.4 )

# Simulating the measurements of two univariate functional datasets with
# required center and covariance function
Data_1 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = C )
Data_2 = generate_gauss_fdata( N, centerline = sin( 2 * pi * grid ), Cov = C )

# Building the mfData object
mfData( grid, list( Data_1, Data_2 ) )