which_on {funGp} | R Documentation |
Indices of active inputs in a given model structure
Description
The fgpm_factory function returns an object of class "Xfgpm"
with the function calls of all the evaluated models stored in the @log.success@args
and
@log.crashes@args
slots. The which_on
function interprets the arguments linked to any
structural configuration and returns a list with two elements: (i) an array
of indices of the scalar
inputs kept active; and (ii) an array
of indices of the functional inputs kept active.
Usage
which_on(sIn = NULL, fIn = NULL, args)
Arguments
sIn |
An optional matrix of scalar input coordinates with all the orignal scalar input variables.
This is used only to know the total number of scalar input variables. Any |
fIn |
An optional list of functional input coordinates with all the original functional input
variables. This is used only to know the total number of functional input variables. Any |
args |
An object of class |
Value
An object of class "list"
, containing the following information extracted from the
args parameter: (i) an array of indices of the scalar inputs kept active; and (ii) an array of
indices of the functional inputs kept active.
Author(s)
José Betancourt, François Bachoc, Thierry Klein and Jérémy Rohmer
References
Betancourt, J., Bachoc, F., Klein, T., Idier, D., Rohmer, J., and Deville, Y. (2024), "funGp: An R Package for Gaussian Process Regression with Scalar and Functional Inputs". Journal of Statistical Software, 109, 5, 1–51. (doi:10.18637/jss.v109.i05)
Betancourt, J., Bachoc, F., Klein, T., Idier, D., Rohmer, J., and Deville, Y. (2024), "funGp: An R Package for Gaussian Process Regression with Scalar and Functional Inputs". Journal of Statistical Software, 109, 5, 1–51. (doi:10.18637/jss.v109.i05)
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]
See Also
* get_active_in for details on how to obtain the data structures linked to the active inputs;
* modelCall for details on the args argument;
* fgpm_factory for funGp heuristic model selection;
* Xfgpm for details on object delivered by fgpm_factory.
Examples
# extracting the indices of the active inputs in an optimized model________________________
# use precalculated Xfgpm object named xm
# active inputs in the best model
xm@log.success@args[[1]] # the full fgpm call
set.seed(100)
n.tr <- 32
sIn <- expand.grid(x1 = seq(0,1,length = n.tr^(1/5)), x2 = seq(0,1,length = n.tr^(1/5)),
x3 = seq(0,1,length = n.tr^(1/5)), x4 = seq(0,1,length = n.tr^(1/5)),
x5 = seq(0,1,length = n.tr^(1/5)))
fIn <- list(f1 = matrix(runif(n.tr*10), ncol = 10), f2 = matrix(runif(n.tr*22), ncol = 22))
which_on(sIn, fIn, xm@log.success@args[[1]]) # only the indices extracted by which_on