which_on: Indices of active inputs in a given model structure
In funGp: Gaussian Process Models for Scalar and Functional Inputs

which_on

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 `matrix` with as many columns as original scalar input variables could be used instead.
`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 `list` with as many elements as original functional input variables could be used instead.
`args`	An object of class `"modelCall"`, which specifies the model structure for which the active inputs should be extracted.

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. (\Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/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]

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

funGp documentation built on May 29, 2024, 8 a.m.