evaluate_predictor: Evaluate component effects or expressions

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

Evaluate component effects or expressions

Description

Evaluate component effects or expressions, based on a bru model and one or several states of the latent variables and hyperparameters.

Usage

evaluate_predictor(
  model,
  state,
  data,
  effects,
  predictor,
  used = NULL,
  format = "auto",
  n_pred = NULL
)

Arguments

state	A list where each element is a list of named latent state information, as produced by evaluate_state()
data	A list, data.frame, or Spatial*DataFrame, with coordinates and covariates needed to evaluate the model.
effects	A list where each element is list of named evaluated effects, as computed by evaluate_effect_multi_state.component_list()
predictor	Either a formula or expression
used	A bru_used() object, or NULL (default)
format	character; determines the storage format of the output. Available options: "auto" If the first evaluated result is a vector or single-column matrix, the "matrix" format is used, otherwise "list". "matrix" A matrix where each column contains the evaluated predictor expression for a state. "list" A list where each column contains the evaluated predictor expression for a state. Default: "auto"
n_pred	integer. If provided, scalar predictor results are expanded to vectors of length n_pred.

Details

For each component, e.g. "name", the state values are available as name_latent, and arbitrary evaluation can be done with name_eval(...), see bru_component_eval().

Value

A list or matrix is returned, as specified by format

inlabru documentation built on April 3, 2025, 10:54 p.m.