Defining partially observed functional data terms in VDPO formulae

Auxiliary function used to define ffpo terms within VDPO model formulae.

Usage

ffpo(X, grid, bidimensional_grid = FALSE, nbasis = c(30, 30), bdeg = c(3, 3))

Arguments

X

partially observed functional covariate matrix.

grid

observation grid of the covariate.

bidimensional_grid

boolean value that specifies if the grid should be treated as 1-dimensional or 2-dimensional. The default value is FALSE (1-dimensional). See also 'Details'.

nbasis

number of basis to be used.

bdeg

degree of the basis to be used.

Value

the function is interpreted in the formula of a VDPO model. list containing the following elements:

• B_ffpo design matrix.

• Phi B-spline basis used for the functional coefficient.

• M vector or matrix object indicating the observed domain of the data.

• nbasis number of the basis used.

Details

When the same observation points are used for every functional covariate, we end up with a vector observation grid. Imagine plotting multiple curves, each representing a functional covariate, all measured at the same time instances.

Conversely, if the observation points differ for each functional covariate, we have a matrix observation grid. Picture a matrix where each row represents a functional covariate, and the columns denote distinct observation points. Varying observation points introduce complexity, as each covariate might be sampled at different time instances.

See also

add_grid