bru_model: Create an inlabru model object from model components

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

Create an inlabru model object from model components

Description

The inlabru syntax for model formulae is different from what INLA::inla considers a valid. In inla most of the effects are defined by adding an f(...) expression to the formula. In inlabru the f is replaced by an arbitrary (exceptions: const and offset) string that will determine the label of the effect. See Details for further information.

Usage

bru_model(components, lhoods, options = list(), .envir = parent.frame())

## S3 method for class 'bru_model'
summary(object, ...)

## S3 method for class 'summary_bru_model'
print(x, ...)

## S3 method for class 'bru_model'
print(x, ...)

Arguments

components	A bru_comp_list object
lhoods	Either a bru_obs() object, a bru_obs_list() object, or a list of one or more bru_obs() or bru_obs_list() objects, or a list of arguments for a call to bru_obs().
options	A bru_options options object or a list of options passed on to bru_options()
.envir	The environment in which the components are evaluated.
object	Object to operate on
...	Arguments passed on to other methods
x	An object to be printed

Details

For instance

y ~ f(myspde, ...)

in INLA is equivalent to

y ~ myspde(...)

in inlabru.

A disadvantage of the inla way is that there is no clear separation between the name of the covariate and the label of the effect. Furthermore, for some models like SPDE it is much more natural to use spatial coordinates as covariates rather than an index into the SPDE vertices. For this purpose inlabru provides the main argument. For convenience, the main argument can be used like the first argument of the f function, e.g., and is the first argument of the component definition. The INLA model formula

y ~ f(temperature, model = 'linear')

is equivalent to the inlabru component and formula definition

y ~ temperature(temperature, model = 'linear') and y ~ temperature(main = temperature, model = 'linear') as well as y ~ temperature(model = 'linear') which sets main = temperature.

On the other hand, main can also be a function mapping, e.g the sp::coordinates() function:

y ~ mySPDE(coordinates, ...)

This extracts the coordinates from the data object, and maps it to the latent field via the information given in the mapper, which by default is extracted from the model object, in the case of spde model objects.

Morevover, main can be any expression that evaluates within your data as an environment. For instance, if your data has columns 'a' and 'b', you can create a fixed effect of 'sin(a+b)' by setting map in the following way:

y ~ myEffect(sin(a+b))

Value

A bru_model object

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