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.
formulaA formula specifying the model structure and covariates.
dataA data.frame containing the observations used to train the model.
responseNameA character string specifying the name of the response variable.
covariateNameA character vector specifying the names of the covariates.
responseRangeA numeric vector of length 2 indicating the lower and upper bounds of the response.
predictorsRangeA list containing:
predictorsLower: lower bounds of the covariates;
predictorsUpper: upper bounds of the covariates.
methodA character string indicating the training method used: one of {"MCMC", "MAP", "Laplace", "none"}.
pAn integer indicating the number of basis functions used.
basisFunctionsUsedA character string specifying the type of basis functions used:
"inducing points", "RFF", "Discrete FF", "filling FF", or "custom cosines".
opts_BasisFunA list of additional options used to configure the basis functions.
BasisFunParamA list containing the computed parameters of the basis functions,
e.g., Fourier frequencies or interpolation weights.
coefficientsA matrix of coefficients for the finite-rank Gaussian process.
Each row corresponds to a realization of the latent field:
\( Z(x, t) = \sum_{i=1}^p \epsilon_i f_i(x, t) \).
hyperparamsA list of hyperparameters, including:
sigma: numeric signal standard deviation;
lengthscale: a vector of lengthscales for each input dimension.
trendA function that returns the trend of the transformed GP (not to be estimated).
logPostA numeric value representing the (unnormalized) log-posterior of the model.
Currently available only for MAP and Laplace-trained models.