pred_over_grid {RiskMap}

Prediction of the random effects components and covariates effects over a spatial grid using a fitted generalized linear Gaussian process model

Description

This function computes predictions over a spatial grid using a fitted model obtained from the glgpm function. It provides point predictions and uncertainty estimates for the specified locations for each component of the model separately: the spatial random effects; the unstructured random effects (if included); and the covariates effects.

Usage

pred_over_grid(
  object,
  grid_pred,
  predictors = NULL,
  re_predictors = NULL,
  pred_cov_offset = NULL,
  control_sim = set_control_sim(),
  type = "marginal",
  messages = TRUE
)

Arguments

object

A RiskMap object obtained from the 'glgpm' function.

grid_pred

An object of class 'sfc', representing the spatial grid over which predictions are to be made. Must be in the same coordinate reference system (CRS) as the object passed to 'object'.

predictors

Optional. A data frame containing predictor variables used for prediction.

re_predictors

Optional. A data frame containing predictors for unstructured random effects, if applicable.

pred_cov_offset

Optional. A numeric vector specifying covariate offsets at prediction locations.

control_sim

Control parameters for MCMC sampling. Must be an object of class "mcmc.RiskMap" as returned by set_control_sim.

type

Type of prediction. "marginal" for marginal predictions, "joint" for joint predictions.

messages

Logical. If TRUE, display progress messages. Default is TRUE.

Value

An object of class 'RiskMap.pred.re' containing predicted values, uncertainty estimates, and additional information.

Author(s)

Emanuele Giorgi [e-mail]

Claudio Fronterre [e-mail]