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