Similar to `glm()`

, `gam()`

and `inla()`

, `bru()`

models can be constructed via
a formula-like syntax, where each latent effect is specified. However, in
addition to the parts of the syntax compatible with `INLA::inla`

, `bru`

components offer additional functionality which facilitates modelling, and
the predictor expression can be specified separately, allowing more complex
and non-linear predictors to be defined. The formula syntax is just a way to
allow all model components to be defined in a single line of code, but the
definitions can optionally be split up into separate component definitions.
See Details for more information.

The `component`

methods all rely on the `component.character()`

method, that
defines a model component with a given label/name. The user usually
doesn't need to call these methods directly, but can instead supply a
formula expression that can be interpreted by the `component_list.formula()`

method, called inside `bru()`

.

## Usage

```
component(...)
# S3 method for character
component(
object,
main = NULL,
weights = NULL,
...,
model = NULL,
mapper = NULL,
main_layer = NULL,
main_selector = NULL,
n = NULL,
values = NULL,
season.length = NULL,
copy = NULL,
weights_layer = NULL,
weights_selector = NULL,
group = 1L,
group_mapper = NULL,
group_layer = NULL,
group_selector = NULL,
ngroup = NULL,
control.group = NULL,
replicate = 1L,
replicate_mapper = NULL,
replicate_layer = NULL,
replicate_selector = NULL,
nrep = NULL,
marginal = NULL,
A.msk = deprecated(),
.envir = parent.frame(),
envir_extra = NULL
)
```

## Arguments

- ...
Parameters passed on to other methods

- object
A character label for the component

- main
`main`

takes an R expression that evaluates to where the latent variables should be evaluated (coordinates, indices, continuous scalar (for rw2 etc)). Arguments starting with weights, group, replicate behave similarly to main, but for the corresponding features of`INLA::f()`

.- weights, weights_layer, weights_selector
Optional specification of effect scaling weights. Same syntax as for

`main`

.- model
Either one of "const" (same as "offset"), "factor_full", "factor_contrast", "linear", "fixed", or a model name or object accepted by INLA's

`f`

function. If set to NULL, then "linear" is used for vector inputs, and "fixed" for matrix input (converted internally to an iid model with fixed precision)- mapper
Information about how to do the mapping from the values evaluated in

`main`

, and to the latent variables. Auto-detects spde model objects in model and extracts the mesh object to use as the mapper, and auto-generates mappers for indexed models. (Default: NULL, for auto-determination)- main_layer, main_selector
The

`_layer`

input should evaluate to a numeric index or character name or vector of which layer/variable to extract from a covariate data object given in`main`

. (Default: NULL if`_selector`

is given. Otherwise the effect component name, if it exists in the covariate object, and otherwise the first column of the covariate data frame)The

`_selector`

value should be a character name of a variable whose contents determines which layer to extract from a covariate for each data point. (Default: NULL)- n
The number of latent variables in the model. Should be auto-detected for most or all models (Default: NULL, for auto-detection). An error is given if it can't figure it out by itself.

- values
Specifies for what covariate/index values INLA should build the latent model. Normally generated internally based on the mapping details. (Default: NULL, for auto-determination)

- season.length
Passed on to

`INLA::f()`

for model`"seasonal"`

(TODO: check if this parameter is still fully handled)- copy
character; label of other component that this component should be a copy of. If the

`fixed = FALSE`

, a scaling constant is estimated, via a hyperparameter. If`fixed = TRUE`

, the component scaling is fixed, by default to 1; for fixed scaling, it's more efficient to express the scaling in the predictor expression instead of making a copy component.- group, group_mapper, group_layer, group_selector, ngroup
Optional specification of kronecker/group model indexing.

- control.group
`list`

of kronecker/group model parameters, currently passed directly on to`INLA::f`

- replicate, replicate_mapper, replicate_layer, replicate_selector, nrep
Optional specification of indices for an independent replication model. Same syntax as for

`main`

- marginal
May specify a

`bru_mapper_marginal()`

mapper, that is applied before scaling by`weights`

.- A.msk
- .envir
Evaluation environment

- envir_extra
TODO: check/fix this parameter.

## Details

As shorthand, `bru()`

will understand basic additive formulae describing fixed effect
models. For instance, the
components specification `y ~ x`

will define the linear combination of an
effect named `x`

and an intercept to
the response `y`

with respect to the likelihood family stated when calling `bru()`

. Mathematically,
the linear predictor \(\eta\) would be written down as

$$\eta = \beta * x + c,$$

where:

\(c\) is the

*intercept*\(x \)is a

*covariate*\(\beta\) is a

*latent variable*associated with \(x\) and\(\psi = \beta * x \) is called the

*effect*of \(x\)

A problem that arises when using this kind of R formula is that it does not
clearly reflect the mathematical
formula. For instance, when providing the formula to inla, the resulting
object will refer to the random
effect \(\psi = \beta * x \) as `x`

.
Hence, it is not clear when `x`

refers to the covariate
or the effect of the covariate.

The `component.character`

method is inlabru's equivalent to INLA's
`f`

function but adds functionality that is unique to inlabru.

Deprecated parameters:

map: Use

`main`

instead.mesh: Use

`mapper`

instead.

## Naming random effects

In INLA, the `f()`

notation is used to define more complex models, but
a simple linear effect model can also be expressed as

`formula = y ~ f(x, model = "linear")`

,

where `f()`

is the inla specific function to set up random effects of all kinds. The underlying
predictor would again be \(\eta = \beta * x + c\) but the result of fitting the model would state
`x`

as the random effect's name. bru allows rewriting this formula in order to explicitly state
the name of the random effect and the name of the associated covariate. This is achieved by replacing `f`

with an arbitrary name that we wish to assign to the effect, e.g.

`components = y ~ psi(x, model = "linear")`

.

Being able to discriminate between \(x\) and \(\psi\) is relevant because of two functionalities
bru offers. The formula parameters of both `bru()`

and the prediction method predict.bru
are interpreted in the mathematical sense. For instance, `predict`

may be used to analyze the
analytical combination of the covariate \(x\) and the intercept using

`predict(fit, data.frame(x=2)), ~ exp(psi + Intercept)`

.

which corresponds to the mathematical expression e ^{β + c}.

On the other hand, predict may be used to only look at a transformation of the latent variable \(\beta_\psi\)

`predict(fit, NULL, ~ exp(psi_latent))`

.

which corresponds to the mathematical expression e ^{β}.

## See also

Other component constructors:
`component_list()`

## Author

Fabian E. Bachl bachlfab@gmail.com and Finn Lindgren Finn.Lindgren@gmail.com

## Examples

```
# As an example, let us create a linear component. Here, the component is
# called "myLinearEffectOfX" while the covariate the component acts on is
# called "x". Note that a list of components is returned because the
# formula may define multiple components
cmp <- component_list(~ myLinearEffectOfX(main = x, model = "linear"))
summary(cmp)
#> Label: myLinearEffectOfX
#> Type: main = linear, group = exchangeable, replicate = iid
#> Input: main = x, group = 1L, replicate = 1L
#> Map: Not yet initialised
#> INLA formula:
#> ~ . + f(myLinearEffectOfX, model =
#> BRU_myLinearEffectOfX_main_model)
#> Label: Intercept
#> Type: main = linear, group = exchangeable, replicate = iid
#> Input: main = 1, group = 1L, replicate = 1L
#> Map: Not yet initialised
#> INLA formula:
#> ~ . + f(Intercept, model = BRU_Intercept_main_model)
# Equivalent shortcuts:
cmp <- component_list(~ myLinearEffectOfX(x, model = "linear"))
cmp <- component_list(~ myLinearEffectOfX(x))
# Individual component
cmp <- component("myLinearEffectOfX", main = x, model = "linear")
summary(cmp)
#> Label: myLinearEffectOfX
#> Type: main = linear, group = exchangeable, replicate = iid
#> Input: main = x, group = 1L, replicate = 1L
#> Map: Not yet initialised
#> INLA formula:
#> ~ . + f(myLinearEffectOfX, model =
#> BRU_myLinearEffectOfX_main_model)
# \donttest{
if (bru_safe_inla(quietly = TRUE)) {
# As an example, let us create a linear component. Here, the component is
# called "myEffectOfX" while the covariate the component acts on is called "x":
cmp <- component("myEffectOfX", main = x, model = "linear")
summary(cmp)
# A more complicated component:
cmp <- component("myEffectOfX",
main = x,
model = INLA::inla.spde2.matern(fm_mesh_1d(1:10))
)
# Compound fixed effect component, where x and z are in the input data.
# The formula will be passed on to MatrixModels::model.Matrix:
cmp <- component("eff", ~ -1 + x:z, model = "fixed")
summary(cmp)
}
#> Label: eff
#> Type: main = fixed, group = exchangeable, replicate = iid
#> Input: main = ~-1 + x:z, group = 1L, replicate = 1L
#> Map: Not yet initialised
#> INLA formula:
#> ~ . + f(eff, model = BRU_eff_main_model, hyper =
#> BRU_eff_main_fixed_hyper)
# }
```