modelDef: Retrieve a 'fgpm' from within a 'Xfgpm' object
In funGp: Gaussian Process Models for Scalar and Functional Inputs

modelDef

R Documentation

Retrieve a `fgpm` from within a `Xfgpm` object

Description

Retrieve the fgpm model with index (or rank) i from within a Xfgpm object. By evaluating this code in an environment containing suitable objects sIn, fIn and sOut we can re-create a fgpm object.

Usage

modelDef(
  object,
  ind,
  trace = TRUE,
  pbars = TRUE,
  control.optim = list(trace = TRUE)
)

Arguments

`object`	A `Xfgpm` object as created by `fgpm_factory`.
`ind`	The index (or rank) of the model in `object`.
`trace`	An optional boolean indicating whether funGp-native progress messages should be displayed. Default is TRUE. See the `fgpm()` documentation for more details.
`pbars`	An optional boolean indicating whether progress bars managed by `fgpm()` should be displayed. Default is TRUE. See the `fgpm()` documentation for more details.
`control.optim`	An optional list to be passed as the control argument to `optim`(), the function in charge of the non-linear optimization of the hyperparameters. Default is list(trace = TRUE). See the `fgpm()` documentation for more details.

Details

The models are sorted by decreasing quality so i = 1 extracts the definition of the best model.

Value

A parsed R code defining the fgpm model.

Note

Remind that the models are sorted by decreasing quality so i = 1 extracts the definition of the best model.

Examples

## =========================================================================
## Using the pre-calculated object `xm` to save time. See `?xm` to re-create
## this object.
## =========================================================================

## 'xm@model' is the best 'fgpm' model in 'xm'
plot(xm@model)

## see the R code to use to recreate the model
modelDef(xm, i = 1)

## Not run: 
    ## Define new data in a list. Using an environment would also work,
    ## including the global environment, which is the default in  `eval`.
    L <- list()
    set.seed(341)
    n.new <- 3^5
    x1 <- x2 <- x3 <- x4 <- x5 <- seq(0, 1, length = n.new^(1/5))

    ## create the data objects required to fit the model
    L$sIn <- as.matrix(expand.grid(x1 = x1, x2 = x2, x3 = x3, x4 = x4, x5 = x5))
    L$fIn <- list(f1 = matrix(runif(n.new * 10), ncol = 10),
                   f2 = matrix(runif(n.new * 22), ncol = 22))
    L$sOut <- fgp_BB7(L$sIn, L$fIn, n.new)

    ## Now evaluate
    fgpm.new <- eval(modelDef(xm, i = 1), envir = L)
    plot(fgpm.new, main = "Re-created 'fgpm' model with different data")
    plot(xm[[1]], main = "Re-created 'fgpm' model with the same data")

## End(Not run)

funGp documentation built on April 25, 2023, 9:07 a.m.