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())
# S3 method for class 'bru'
print(x, ...)
Arguments
- components
A
formula
-like specification of latent components. Also used to define a default linear additive predictor. Seecomponent()
for details.- ...
Likelihoods, each constructed by a calling
like()
, or named parameters that can be passed to a singlelike()
call. Note that all the arguments will be evaluated before callinglike()
in order to detect if they arelike
objects. This means that special arguments that need to be evaluated in the context ofresponse_data
ordata
(such asNtrials
) may will only work that way in direct calls tolike()
.- 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
- x
A
bru
object to be printed
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.
Functions
bru_rerun()
: Continue the optimisation from a previously computed estimate. The estimationoptions
list can be given new values to override the original settings.
Author
Fabian E. Bachl bachlfab@gmail.com
Examples
# \donttest{
if (bru_safe_inla()) {
# 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(1), family = "gaussian", data = input.df)
# Obtain summary
fit$summary.fixed
}
#> Changing INLA option num.threads from '4:1' to '1:1'.
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> x 2.027702 0.05210915 1.923574 2.027703 2.131825 2.027703
#> Intercept 4.959784 0.03684149 4.886164 4.959785 5.033398 4.959784
#> kld
#> x 5.770443e-06
#> Intercept 5.770647e-06
if (bru_safe_inla()) {
# Alternatively, we can use the like() function to construct the likelihood:
lik <- like(family = "gaussian",
formula = y ~ x + Intercept,
data = input.df)
fit <- bru(~ x + Intercept(1), lik)
fit$summary.fixed
}
#> 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 5.766617e-06
#> Intercept 5.766822e-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()) {
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
}
#> mean sd 0.025quant 0.5quant 0.975quant mode
#> z 2.000938 0.006940269 1.987069 2.000938 2.014806 2.000938
#> Intercept 5.019976 0.036290850 4.947456 5.019976 5.092490 5.019976
#> kld
#> z 5.769379e-06
#> Intercept 5.769206e-06
# }