Defining partially observed functional data terms in VDPO formulae

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

Usage

ffpo(
  X,
  missing_points = NULL,
  grid,
  bidimensional_grid = FALSE,
  nbasis = c(30, 30),
  bdeg = c(3, 3),
  version = c("current", "legacy")
)

Arguments

X

partially observed functional covariate matrix.

missing_points

observation points that were missing for each functional covariate list.

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.

version

Choose the "current" (default) or "legacy" implementation. The legacy version matches the previous ffpo_old() behavior.

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