SLGP-class: The SLGP S4 Class: Spatial Logistic Gaussian Process Model

In SLGP: Spatial Logistic Gaussian Process for Field Density Estimation

The SLGP S4 Class: Spatial Logistic Gaussian Process Model

Description

This S4 class represents a Spatial Logistic Gaussian Process (SLGP) model, designed for modeling conditional or spatially dependent probability distributions. It encapsulates all necessary components for training, sampling, and prediction, including the basis function setup, learned coefficients, and fitted hyperparameters.

Slots

formula

A formula specifying the model structure and covariates.

data

A data.frame containing the observations used to train the model.

responseName

A character string specifying the name of the response variable.

covariateName

A character vector specifying the names of the covariates.

responseRange

A numeric vector of length 2 indicating the lower and upper bounds of the response.

predictorsRange

A list containing:

• predictorsLower: lower bounds of the covariates;

• predictorsUpper: upper bounds of the covariates.

method

A character string indicating the training method used: one of {"MCMC", "MAP", "Laplace", "none"}.

p

An integer indicating the number of basis functions used.

basisFunctionsUsed

A character string specifying the type of basis functions used: "inducing points", "RFF", "Discrete FF", "filling FF", or "custom cosines".

opts_BasisFun

A list of additional options used to configure the basis functions.

BasisFunParam

A list containing the computed parameters of the basis functions, e.g., Fourier frequencies or interpolation weights.

coefficients

A matrix of coefficients for the finite-rank Gaussian process. Each row corresponds to a realization of the latent field: Z(x, t) = \sum_{i=1}^p \epsilon_i f_i(x, t) .

hyperparams

A list of hyperparameters, including:

• sigma: numeric signal standard deviation;

• lengthscale: a vector of lengthscales for each input dimension.

logPost

A numeric value representing the (unnormalized) log-posterior of the model. Currently available only for MAP and Laplace-trained models.

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