This function prepared input data structures according to the active inputs specified by a
"'>modelCall" object. This function is intended to easily adapt the data structures
to the requirements of a specific model delivered by the model factory function
fgpm_factory.
format4pred(sIn.pr = NULL, fIn.pr = NULL, args)sIn.pr an optional matrix of scalar input coordinates at which the output values should be
predicted. Each column is interpreted as a scalar input variable and each row as a coordinate.
Either scalar input coordinates (sIn.pr), functional input coordinates (fIn.pr), or both must be provided.
The "'>modelCall" object provided through args will lead the extraction of only the
active scalar inputs in the model.
an optional list of functional input coordinates at which the output values should be
predicted. Each element of the list is interpreted as a functional input variable. Every functional input
variable should be provided as a matrix with one curve per row. Either scalar input coordinates (sIn.pr),
functional input coordinates (fIn.pr), or both must be provided. The "'>modelCall"
object provided through args will lead the extraction of only the active functional inputs in the model.
An object of class "list", containing the input data structures with only the active inputs
specified by args.
Betancourt, J., Bachoc, F., and Klein, T. (2020), R Package Manual: "Gaussian Process Regression for Scalar and Functional Inputs with funGp - The in-depth tour". RISCOPE project. [HAL]
* predict for predictions based on a funGp model;
* fgpm for creation of a funGp model;
* fgpm_factory for funGp heuristic model selection.