plotLAA,fgpm-method

<p>This method provides a diagnostic plot for the validation of a funGp Gaussian process model.
  It displays a calibration plot based on the leave-one-out predictions of the output at the points
  used to train the model.</p>

internal

Construction and smart selection of Gaussian process models
with emphasis on treatment of functional inputs. This package
offers: (i) flexible modeling of functional-input regression
problems through the fairly general Gaussian process model; (ii)
built-in dimension reduction for functional inputs; (iii)
heuristic optimization of the structural parameters of the model
(e.g., active inputs, kernel function, type of distance).
Metamodeling background is provided in
Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>.
The algorithm for structural parameter optimization is described
in <https://hal.archives-ouvertes.fr/hal-02532713>.

Jose Betancourt

funGp

Gaussian Process Models for Scalar and Functional Inputs

Fran<c3><a7>ois Bachoc

Thierry Klein

Deborah Idier

Jeremy Rohmer

plotLAA,fgpm-method function

Leave-one-out calibration plot for a funGp model — plotLAA,fgpm-method

Leave-one-out calibration plot for a funGp model

plotLAA,fgpm-method: Leave-one-out calibration plot for a funGp model

Description

Usage

Arguments