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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

bru_mapper_aggregate(rescale = FALSE, n_block = NULL)

# S3 method for class 'bru_mapper_aggregate'
ibm_n(mapper, ..., input = NULL, state = NULL, n_state = NULL)

# S3 method for class 'bru_mapper_aggregate'
ibm_n_output(mapper, input = NULL, ...)

# S3 method for class 'bru_mapper_aggregate'
ibm_values(mapper, ..., state = NULL, n_state = NULL)

# S3 method for class 'bru_mapper_aggregate'
ibm_jacobian(mapper, input, state = NULL, ...)

# S3 method for class 'bru_mapper_aggregate'
ibm_eval(mapper, input, state = NULL, ..., sub_lin = NULL)

Arguments

rescale

logical; For bru_mapper_aggregate and bru_mapper_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.

mapper

A mapper S3 object, inheriting from bru_mapper.

...

Arguments passed on to other methods

input

Data input for the mapper.

state

A vector of latent state values for the mapping, of length ibm_n(mapper, inla_f = FALSE)

n_state

integer giving the length of the state vector for mappers that have state dependent output size.

sub_lin

Internal, optional pre-computed sub-mapper information

Methods (by generic)

  • ibm_jacobian(bru_mapper_aggregate): input should be a list with elements block and weights. block should be a vector of the same length as the state, or NULL, with NULL equivalent to all-1. If weights is NULL, it's interpreted as all-1.

Examples

m <- bru_mapper_aggregate()
ibm_eval2(m, list(block = c(1, 2, 1, 2), weights = 1:4), 11:14)
#> $offset
#> [1] 50 80
#> 
#> $jacobian
#> 2 x 4 sparse Matrix of class "dgCMatrix"
#>             
#> [1,] 1 . 3 .
#> [2,] . 2 . 4
#>