bm_aggregate: Mapper for aggregation

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

Mapper for aggregation

Description

Constructs a mapper that aggregates elements of the input state, so it can be used e.g. for weighted summation or integration over blocks of values.

Usage

bm_aggregate(rescale = FALSE, n_block = NULL, type = NULL)

bru_mapper_aggregate(...)

Arguments

rescale	logical; For bm_aggregate and bm_logsumexp, specifies if the blockwise sums should be normalised by the blockwise weight sums or not: FALSE: (default) Straight weighted sum, no rescaling. TRUE: Divide by the sum of the weight values within each block. This is useful for integration averages, when the given weights are plain integration weights. If the weights are NULL or all ones, this is the same as dividing by the number of entries in each block.
n_block	Predetermined number of output blocks. If NULL, overrides the maximum block index in the inputs. The priority order is input$n_block, the mapper definition n_block, then max(input$block).
type	character; if non-NULL, overrides the rescale argument, and constructs an aggregation mapper of the given type instead. Supported values are "sum", "average" (for regular bm_aggregate()), "logsumexp", "logaverageexp" (for bm_logsumexp()), and "logitaverage" (for bm_logitaverage()).
...	Arguments passed on to bm_aggregate()

Value

A bm_aggregate mapper object.

See Also

bru_mapper, bru_mapper_generics

Other mappers: bm_collect(), bm_const(), bm_expr(), bm_factor(), bm_fm_mesh_1d, bm_fmesher(), bm_harmonics(), bm_index(), bm_linear(), bm_logitaverage(), bm_logsumexp(), bm_marginal(), bm_matrix(), bm_multi(), bm_pipe(), bm_reparam(), bm_repeat(), bm_scale(), bm_shift(), bm_sum(), bm_taylor(), bru_get_mapper(), bru_mapper()

Examples

m <- bm_aggregate()
ibm_eval2(m, list(block = c(1, 2, 1, 2), weights = 1:4), 11:14)
ibm_eval2(m, list(block = c(1, 2, 1, 2), weights = 1:4, n_block = 3), 11:14)

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