Observation model construction for usage with `bru()`

## Usage

```
like(
formula = . ~ .,
family = "gaussian",
data = NULL,
response_data = NULL,
mesh = deprecated(),
E = NULL,
Ntrials = NULL,
weights = NULL,
scale = NULL,
samplers = NULL,
ips = NULL,
domain = NULL,
include = NULL,
exclude = NULL,
include_latent = NULL,
used = NULL,
allow_latent = deprecated(),
allow_combine = NULL,
control.family = NULL,
options = list(),
.envir = parent.frame()
)
like_list(...)
# S3 method for class 'list'
like_list(object, envir = NULL, ...)
# S3 method for class 'bru_like'
like_list(..., envir = NULL)
# S3 method for class 'bru_like'
c(..., envir = NULL)
# S3 method for class 'bru_like_list'
c(..., envir = NULL)
# S3 method for class 'bru_like_list'
x[i]
```

## Arguments

- formula
a

`formula`

where the right hand side is a general R expression defines the predictor used in the model.- family
A string identifying a valid

`INLA::inla`

likelihood family. The default is`gaussian`

with identity link. In addition to the likelihoods provided by inla (see`names(INLA::inla.models()$likelihood)`

) inlabru supports fitting latent Gaussian Cox processes via`family = "cp"`

. As an alternative to`bru()`

, the`lgcp()`

function provides a convenient interface to fitting Cox processes.- data
Likelihood-specific data, as a

`data.frame`

or`SpatialPoints[DataFrame]`

object.- response_data
Likelihood-specific data for models that need different size/format for inputs and response variables, as a

`data.frame`

or`SpatialPoints[DataFrame]`

object.- mesh
Deprecated.

- E
Exposure parameter for family = 'poisson' passed on to

`INLA::inla`

. Special case if family is 'cp': rescale all integration weights by a scalar E. For sampler specific reweighting/effort, use a`weight`

column in the`samplers`

object, see`fmesher::fm_int()`

. Default taken from`options$E`

, normally`1`

.- Ntrials
A vector containing the number of trials for the 'binomial' likelihood. Default taken from

`options$Ntrials`

, normally`1`

.- weights
Fixed (optional) weights parameters of the likelihood, so the log-likelihood

`[i]`

is changed into`weights[i] * log_likelihood[i]`

. Default value is`1`

. WARNING: The normalizing constant for the likelihood is NOT recomputed, so ALL marginals (and the marginal likelihood) must be interpreted with great care.- scale
Fixed (optional) scale parameters of the precision for several models, such as Gaussian and student-t response models.

- samplers
Integration domain for 'cp' family.

- ips
Integration points for 'cp' family. Overrides

`samplers`

.- domain
Named list of domain definitions.

- include
Character vector of component labels that are used as effects by the predictor expression; Default: the result of

`[all.vars()]`

on the predictor expression, unless the expression is not ".", in which case`include=NULL`

, to include all components that are not explicitly excluded. The`bru_used()`

methods are used to extract the variable names, followed by removal of non-component names when the components are available.- exclude
Character vector of component labels that are not used by the predictor expression. The exclusion list is applied to the list as determined by the

`include`

parameter; Default: NULL (do not remove any components from the inclusion list)- include_latent
character vector. Specifies which the latent state variables are directly available to the predictor expression, with a

`_latent`

suffix. This also makes evaluator functions with suffix`_eval`

available, taking parameters`main`

,`group`

, and`replicate`

, taking values for where to evaluate the component effect that are different than those defined in the component definition itself (see`component_eval()`

). Default`NULL`

auto-detects use of`_latent`

and`_eval`

in the predictor expression.- used
Either

`NULL`

or a`bru_used()`

object, overriding`include`

,`exclude`

, and`include_latent`

.- allow_latent
- allow_combine
logical; If

`TRUE`

, the predictor expression may involve several rows of the input data to influence the same row. Default`FALSE`

, but forced to`TRUE`

if`response_data`

is non-`NULL`

,`data`

is a`list`

, or the likelihood construction requires it.- control.family
A optional

`list`

of`INLA::control.family`

options- options
A bru_options options object or a list of options passed on to

`bru_options()`

- .envir
The evaluation environment to use for special arguments (

`E`

,`Ntrials`

,`weights`

, and`scale`

) if not found in`response_data`

or`data`

. Defaults to the calling environment.- ...
For

`like_list.bru_like`

, one or more`bru_like`

objects- object
A list of

`bru_like`

objects- envir
An optional environment for the new

`bru_like_list`

object- x
`bru_like_list`

object from which to extract element(s)- i
indices specifying elements to extract

## Value

A likelihood configuration which can be used to parameterise `bru()`

.

## Functions

`like_list()`

: Combine`bru_like`

likelihoods into a`bru_like_list`

object`like_list(list)`

: Combine a list of`bru_like`

likelihoods into a`bru_like_list`

object`like_list(bru_like)`

: Combine several`bru_like`

likelihoods into a`bru_like_list`

object`c(bru_like)`

: Combine several`bru_like`

likelihoods and/or`bru_like_list`

objects into a`bru_like_list`

object`c(bru_like_list)`

: Combine several`bru_like`

likelihoods and/or`bru_like_list`

objects into a`bru_like_list`

object

## Examples

```
# \donttest{
if (bru_safe_inla() &&
require(ggplot2, quietly = TRUE)) {
# The like function's main purpose is to set up models with multiple likelihoods.
# The following example generates some random covariates which are observed through
# two different random effect models with different likelihoods
# Generate the data
set.seed(123)
n1 <- 200
n2 <- 10
x1 <- runif(n1)
x2 <- runif(n2)
z2 <- runif(n2)
y1 <- rnorm(n1, mean = 2 * x1 + 3)
y2 <- rpois(n2, lambda = exp(2 * x2 + z2 + 3))
df1 <- data.frame(y = y1, x = x1)
df2 <- data.frame(y = y2, x = x2, z = z2)
# Single likelihood models and inference using bru are done via
cmp1 <- y ~ -1 + Intercept(1) + x
fit1 <- bru(cmp1, family = "gaussian", data = df1)
summary(fit1)
cmp2 <- y ~ -1 + Intercept(1) + x + z
fit2 <- bru(cmp2, family = "poisson", data = df2)
summary(fit2)
# A joint model has two likelihoods, which are set up using the like function
lik1 <- like("gaussian", formula = y ~ x + Intercept, data = df1)
lik2 <- like("poisson", formula = y ~ x + z + Intercept, data = df2)
# The union of effects of both models gives the components needed to run bru
jcmp <- ~ x + z + Intercept(1)
jfit <- bru(jcmp, lik1, lik2)
# Compare the estimates
p1 <- ggplot() +
gg(fit1$summary.fixed, bar = TRUE) +
ylim(0, 4) +
ggtitle("Model 1")
p2 <- ggplot() +
gg(fit2$summary.fixed, bar = TRUE) +
ylim(0, 4) +
ggtitle("Model 2")
pj <- ggplot() +
gg(jfit$summary.fixed, bar = TRUE) +
ylim(0, 4) +
ggtitle("Joint model")
multiplot(p1, p2, pj)
}
# }
```