bru_comp_eval: Evaluate component values in predictor expressions
In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

bru_comp_eval

R Documentation

Evaluate component values in predictor expressions

Description

In predictor expressions, name_eval(...) can be used to evaluate the effect of a component called "name".

Usage

bru_comp_eval(
  main,
  group = NULL,
  replicate = NULL,
  weights = NULL,
  .state = NULL
)

Arguments

main, group, replicate, weights

Specification of where to evaluate a component. The four inputs are passed on to the joint bru_mapper for the component, as

list(core = list(
       main = main,
       group = group,
       replicate = replicate),
     scale = weights)

NOTE: If you have model component with the same name as a data variable you want to supply as input to name_eval(), you need to use .data.[["myvar"]] to access it. Otherwise, it will try to use the other component effect as input, which is ill-defined.

.state

The internal component state. Normally supplied automatically by the internal methods for evaluating inlabru predictor expressions.

Value

A vector of values for a component

Examples


if (bru_safe_inla() &&
  requireNamespace("sn", quietly = TRUE)) {
  set.seed(12345L)
  data <-
    data.frame(
      x = runif(5),
      idx = seq_len(5)
    )
  data$y <- data$x + rnorm(5, sd = 0.1)
  fit <- bru(
    y ~ 0 + x + field(idx, model = "iid", mapper = bm_index(5L)),
    family = "gaussian", data = data,
    control.family = list(
      hyper = list(prec = list(initial = 9, fixed = TRUE))
    ),
    options = list(control.inla = list(int.strategy = "eb"))
  )
  pred <- generate(
    fit,
    newdata = list(),
    formula = ~ field_eval(c(seq_len(5), 6, 7, 6)),
    n.samples = 1L
  )
}

inlabru documentation built on July 28, 2026, 9:07 a.m.