bru_gcpo_table: Compare block-averaged GCPO scores across multiple fitted bru...

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

Compare block-averaged GCPO scores across multiple fitted bru models

Description

Calls bru_block_gcpo() on each fit and combines the results into a data.frame with one row per block and one column per model, making it straightforward to compare GCPO scores across models block by block.

Usage

bru_gcpo_table(fits = NULL, ...)

Arguments

fits	A named list of fitted bru objects, or NULL if models are passed via ⁠\dots⁠.
...	Named fitted bru objects, used when fits is not a list.

Value

For a single-likelihood model, a data.frame with columns:

block

Block labels from response_data$BRU_block (for "cp" models) or observation indices (for other models).

one column per model

GCPO scores on the probability scale, named after the elements of fits or ⁠\dots⁠.

For multi-likelihood models, a named list of such data.frames, one per likelihood.

See Also

bru_block_gcpo()

Examples

if (bru_safe_inla()) {
  cvpart <- cv_hex(
    gorillas_sf$boundary,
    cellsize = 0.5,
    n_group = 3,
    resolution = c(95, 80)
  )
  cvpart$block_ID <- seq_len(nrow(cvpart))
  cvpart$group <- NULL

  nests <- gorillas_sf$nests
  a <- sf::st_intersects(nests, cvpart)
  nests$.block <- unlist(a)

  fit1 <- lgcp(
    geometry ~ Intercept(1),
    data = nests,
    samplers = cvpart,
    domain = list(geometry = gorillas_sf$mesh),
    control.gcpo = list(enable = TRUE, type.cv = "joint")
  )
  pcmatern <- INLA::inla.spde2.pcmatern(
    gorillas_sf$mesh,
    prior.sigma = c(1, 0.01),
    prior.range = c(0.1, 0.01)
  )
  fit2 <- lgcp(
    geometry ~ Intercept(1) + field(geometry, model = pcmatern),
    data = nests,
    samplers = cvpart,
    domain = list(geometry = gorillas_sf$mesh),
    control.gcpo = list(enable = TRUE, type.cv = "joint")
  )
  gcpo_df <- bru_gcpo_table(Model1 = fit1, Model2 = fit2)
  names(gcpo_df)
  colSums(log(gcpo_df[, -1]))
}

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