This function implements a constructor for elements of `S3`

class
`mfData`

, aimed at implementing a representation of a multivariate
functional dataset.

1 |

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

`Data_list` |
a |

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`

).

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 unviariate functional datasets.

`fData`

, `generate_gauss_fdata`

,
`generate_gauss_mfdata`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
# 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 ) )
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.