This method is a wrapper for `INLA::inla`

and provides
multiple enhancements.

Easy usage of spatial covariates and automatic construction of inla projection matrices for (spatial) SPDE models. This feature is accessible via the

`components`

parameter. Practical examples on how to use spatial data by means of the components parameter can also be found by looking at the lgcp function's documentation.Constructing multiple likelihoods is straight forward. See like for more information on how to provide additional likelihoods to

`bru`

using the`...`

parameter list.Support for non-linear predictors. See example below.

Log Gaussian Cox process (LGCP) inference is available by using the

`cp`

family or (even easier) by using the lgcp function.

## Usage

```
bru(components = ~Intercept(1), ..., options = list(), .envir = parent.frame())
bru_rerun(result, options = list())
```

## Arguments

- components
A

`formula`

-like specification of latent components. Also used to define a default linear additive predictor. See`component()`

for details.- ...
Likelihoods, each constructed by a calling

`like()`

, or named parameters that can be passed to a single`like()`

call. Note that all the arguments will be evaluated before calling`like()`

in order to detect if they are`like`

objects. This means that special arguments that need to be evaluated in the context of`response_data`

or`data`

(such as Ntrials) may will only work that way in direct calls to`like()`

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

`bru_options()`

- .envir
Environment for component evaluation (for when a non-formula specification is used)

- result
A previous estimation object of class

`bru`

## Value

bru returns an object of class "bru". A `bru`

object inherits
from `INLA::inla`

(see the inla documentation for its properties) and
adds additional information stored in the `bru_info`

field.

## Author

Fabian E. Bachl bachlfab@gmail.com

## Examples

```
# \donttest{
if (bru_safe_inla(multicore = FALSE)) {
# Simulate some covariates x and observations y
input.df <- data.frame(x = cos(1:10))
input.df <- within(input.df, y <- 5 + 2 * x + rnorm(10, mean = 0, sd = 0.1))
# Fit a Gaussian likelihood model
fit <- bru(y ~ x + Intercept, family = "gaussian", data = input.df)
# Obtain summary
fit$summary.fixed
}
#> Current num.threads is '4:1'.
#> Setting INLA option num.threads to '1:1'. Previous value '4:1'.
#> Warning: All covariate evaluations for 'Intercept' are NULL; an intercept component was likely intended.
#> Implicit latent intercept component specification is deprecated since version 2.1.14.
#> Use explicit notation '+ Intercept(1)' instead (or '+1' for '+ Intercept(1)').
#> Warning: The input evaluation 'Intercept' for 'Intercept' failed. Perhaps the data object doesn't contain the needed variables? Falling back to '1'.
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> x 2.027702 0.05210915 1.923574 2.027703 2.131825 2.027703
#> Intercept 4.959784 0.03684149 4.886164 4.959785 5.033398 4.959784
#> kld
#> x 5.770443e-06
#> Intercept 5.770644e-06
if (bru_safe_inla(multicore = FALSE)) {
# Alternatively, we can use the like() function to construct the likelihood:
lik <- like(family = "gaussian", formula = y ~ x + Intercept, data = input.df)
fit <- bru(~ x + Intercept(1), lik)
fit$summary.fixed
}
#> Current num.threads is '1:1'.
#> No num.threads change needed.
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> x 2.027702 0.05211308 1.923566 2.027703 2.131833 2.027703
#> Intercept 4.959784 0.03684427 4.886158 4.959785 5.033404 4.959784
#> kld
#> x 5.766616e-06
#> Intercept 5.766821e-06
# An important addition to the INLA methodology is bru's ability to use
# non-linear predictors. Such a predictor can be formulated via like()'s
# \code{formula} parameter. The z(1) notation is needed to ensure that
# the z component should be interpreted as single latent variable and not
# a covariate:
if (bru_safe_inla(multicore = FALSE)) {
z <- 2
input.df <- within(input.df, y <- 5 + exp(z) * x + rnorm(10, mean = 0, sd = 0.1))
lik <- like(
family = "gaussian", data = input.df,
formula = y ~ exp(z) * x + Intercept
)
fit <- bru(~ z(1) + Intercept(1), lik)
# Check the result (z posterior should be around 2)
fit$summary.fixed
}
#> Current num.threads is '1:1'.
#> No num.threads change needed.
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> z 2.000938 0.006940227 1.987069 2.000938 2.014806 2.000938
#> Intercept 5.019976 0.036290631 4.947457 5.019976 5.092490 5.019976
#> kld
#> z 5.769853e-06
#> Intercept 5.769670e-06
# }
```