retrainSLGP: Retrain a fitted SLGP model with new data and/or estimation...

In SLGP: Spatial Logistic Gaussian Process for Field Density Estimation

Retrain a fitted SLGP model with new data and/or estimation method

Description

This function retrains an existing SLGP model using either a Bayesian MCMC estimation, a Maximum A Posteriori (MAP) estimation, or a Laplace approximation. The model can be retrained using new data, new inference settings, or updated hyperparameters. It reuses the structure and basis functions from the original model.

Usage

retrainSLGP(
  SLGPmodel,
  newdata = NULL,
  epsilonStart = NULL,
  method,
  interpolateBasisFun = "WNN",
  nIntegral = 51,
  nDiscret = 51,
  hyperparams = NULL,
  sigmaEstimationMethod = "none",
  seed = NULL,
  opts = list(),
  verbose = FALSE
)

Arguments

SLGPmodel	An object of class SLGP-class to be retrained.
newdata	Optional data frame containing new observations. If NULL, the original data is reused.
epsilonStart	Optional numeric vector with initial values for the coefficients \epsilon.
method	Character string specifying the estimation method: one of {"MCMC", "MAP", "Laplace"}.
interpolateBasisFun	Character string specifying how basis functions are evaluated: "nothing" — evaluate directly at sample locations; "NN" — interpolate using nearest neighbor; "WNN" — interpolate using weighted nearest neighbors (default).
nIntegral	Integer specifying the number of quadrature points used to approximate integrals over the response domain.
nDiscret	Integer specifying the discretization grid size (used only if interpolation is enabled).
hyperparams	Optional list with updated hyperparameters. Must include: sigma2: signal variance; lengthscale: vector of lengthscales for the inputs.
sigmaEstimationMethod	Character string indicating how to estimate sigma2: either "none" (default) or "heuristic".
seed	Optional integer to set the random seed for reproducibility.
opts	Optional list of additional options passed to inference routines: stan_chains, stan_iter, ndraws, etc.
verbose	Logical; if TRUE, print progress and diagnostic messages during computation. Defaults to FALSE.

Value

An updated object of class SLGP-class with retrained coefficients and updated posterior information.

References

Gautier, A. (2023). Modelling and Predicting Distribution-Valued Fields with Applications to Inversion Under Uncertainty. PhD Thesis, Universität Bern. https://boristheses.unibe.ch/4377/

Examples

# Load Boston housing dataset
library(MASS)
data("Boston")
range_x <- c(0, 100)
range_response <- c(0, 50)

#Create a SLGP model but don't fit it
modelPrior <- slgp(medv ~ age,        # Use a formula to specify response and covariates
                 data = Boston,     # Use the original Boston housing data
                 method = "none",    # No training
                 basisFunctionsUsed = "RFF",         # Random Fourier Features
                 sigmaEstimationMethod = "heuristic",  # Auto-tune sigma2 (more stable)
                 predictorsLower = range_x[1],         # Lower bound for 'age'
                 predictorsUpper = range_x[2],         # Upper bound for 'age'
                 responseRange = range_response,       # Range for 'medv'
                 opts_BasisFun = list(nFreq = 200,     # Use 200 Fourier features
                                      MatParam = 5/2), # Matern 5/2 kernel
                 seed = 1)                             # Reproducibility
#Retrain using the Boston Housing dataset and a Laplace approximation scheme
modelLaplace <- retrainSLGP(SLGPmodel=modelPrior,
                            newdata = Boston,
                            method="Laplace")

SLGP documentation built on Sept. 9, 2025, 5:25 p.m.