glgpm_sim {RiskMap}

Simulation from Generalized Linear Gaussian Process Models

Description

Simulates data from a fitted Generalized Linear Gaussian Process Model (GLGPM) or a specified model formula and data.

Usage

glgpm_sim(
  n_sim,
  model_fit = NULL,
  formula = NULL,
  data = NULL,
  family = NULL,
  distr_offset = NULL,
  cov_offset = NULL,
  crs = NULL,
  convert_to_crs = NULL,
  scale_to_km = TRUE,
  control_mcmc = NULL,
  sim_pars = list(beta = NULL, sigma2 = NULL, tau2 = NULL, phi = NULL, sigma2_me = NULL,
    sigma2_re = NULL),
  messages = TRUE
)

Arguments

n_sim

Number of simulations to perform.

model_fit

Fitted GLGPM model object of class 'RiskMap'. If provided, overrides 'formula', 'data', 'family', 'crs', 'convert_to_crs', 'scale_to_km', and 'control_mcmc' arguments.

formula

Model formula indicating the variables of the model to be simulated.

data

Data frame or 'sf' object containing the variables in the model formula.

family

Distribution family for the response variable. Must be one of 'gaussian', 'binomial', or 'poisson'.

distr_offset

Offset for the distributional part of the GLGPM. Required for 'binomial' and 'poisson' families.

cov_offset

Offset for the covariate part of the GLGPM.

crs

Coordinate reference system (CRS) code for spatial data.

convert_to_crs

CRS code to convert spatial data if different from 'crs'.

scale_to_km

Logical; if TRUE, distances between locations are computed in kilometers; if FALSE, in meters.

control_mcmc

Control parameters for MCMC simulation if applicable.

sim_pars

List of simulation parameters including 'beta', 'sigma2', 'tau2', 'phi', 'sigma2_me', and 'sigma2_re'.

messages

Logical; if TRUE, display progress and informative messages.

Details

Generalized Linear Gaussian Process Models (GLGPMs) extend generalized linear models (GLMs) by incorporating spatial Gaussian processes to model spatial correlation. This function simulates data from GLGPMs using Markov Chain Monte Carlo (MCMC) methods. It supports Gaussian, binomial, and Poisson response families, utilizing a Matern correlation function to model spatial dependence.

The simulation process involves generating spatially correlated random effects and simulating responses based on the fitted or specified model parameters. For 'gaussian' family, the function simulates response values by adding measurement error.

Additionally, GLGPMs can incorporate unstructured random effects specified through the re() term in the model formula, allowing for capturing additional variability beyond fixed and spatial covariate effects.

Value

A list containing simulated data, simulated spatial random effects (if applicable), and other simulation parameters.

Author(s)

Emanuele Giorgi [e-mail]

Claudio Fronterre [e-mail]