bru_compute_linearisation: Compute inlabru model linearisation information

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

Compute inlabru model linearisation information

Description

Compute inlabru model linearisation information

Usage

bru_compute_linearisation(...)

## S3 method for class 'bru_comp'
bru_compute_linearisation(
  cmp,
  model,
  lhood_expr,
  data,
  data_extra,
  input,
  state,
  comp_simple,
  effects,
  pred0,
  used,
  allow_combine,
  eps,
  n_pred = NULL,
  ...
)

## S3 method for class 'bru_obs'
bru_compute_linearisation(
  lhood,
  model,
  data,
  input,
  state,
  comp_simple,
  eps,
  ...
)

## S3 method for class 'bru_obs_list'
bru_compute_linearisation(
  lhoods,
  model,
  input,
  state,
  comp_simple,
  eps = 1e-05,
  ...
)

## S3 method for class 'bru_model'
bru_compute_linearisation(model, lhoods, input, state, comp_simple, ...)

Arguments

...	Parameters passed on to other methods
cmp	A bru_comp object
model	A bru_model object
lhood_expr	A predictor expression
data	Input data
data_extra	Additional data for the predictor
input	Precomputed component inputs from bru_input()
state	The state information, as a list of named vectors
comp_simple	Component evaluation information For bru_comp: A bm_taylor object For bru_obs: A bm_list object for the components in the likelihood For bru_obs_list: A list of bm_list objects
effects	For bru_comp: Precomputed effect list for all components involved in the likelihood expression
pred0	Precomputed predictor for the given state
used	A bru_used() object for the predictor expression
allow_combine	logical; If TRUE, the predictor expression may involve several rows of the input data to influence the same row.
eps	The finite difference step size
n_pred	The length of the predictor expression. If not NULL, scalar predictor evaluations are expanded to vectors of length n_pred.
lhood	A bru_obs object
lhoods	A bru_obs_list object

inlabru documentation built on Aug. 9, 2025, 1:08 a.m.