inlabru: iinla – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

iinla

Iterated INLA

Description

This is an internal wrapper for iterated runs of INLA::inla. For nonlinear models, a linearisation is done with bru_compute_linearisation, with a line search method between each iteration. The INLA::inla.stack information is setup by bru_make_stack().

Usage

iinla(model, lhoods, initial = NULL, options)

Arguments

`model`	A bru_model object
`lhoods`	A list of likelihood objects from `like()`
`initial`	A previous `bru` result or a list of named latent variable initial states (missing elements are set to zero), to be used as starting point, or `NULL`. If non-null, overrides `options$bru_initial`
`options`	A `bru_options` object.
`data`	A data.frame

Value

An iinla object that inherits from INLA::inla, with an added field bru_iinla with elements

log: The diagnostic log messages produced by the run
states: The list of linearisation points, one for each inla run
inla_stack: The inla.stack object from the final inla run
track: A list of convergence tracking vectors

If an inla run is aborted by an error, the returned object also contains an element error with the error object.

inlabru

Bayesian Latent Gaussian Modelling using INLA and Extensions

v2.3.1

GPL (>= 2)

Authors

Finn Lindgren [aut, cre, cph] (<https://orcid.org/0000-0002-5833-2011>, Finn Lindgren continued development of the main code), Fabian E. Bachl [aut, cph] (Fabian Bachl wrote the main code), David L. Borchers [ctb, dtc, cph] (David Borchers wrote code for Gorilla data import and sampling, multiplot tool), Daniel Simpson [ctb, cph] (Daniel Simpson wrote the basic LGCP sampling method), Lindesay Scott-Howard [ctb, dtc, cph] (Lindesay Scott-Howard provided MRSea data import code), Seaton Andy [ctb] (Andy Seaton provided testing and bugfixes)

Initial release