bru {inlabru} | R Documentation |
Convenient model fitting using (iterated) INLA
Description
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 |
... |
Likelihoods, each constructed by a calling |
options |
A bru_options options object or a list of options passed
on to |
.envir |
Environment for component evaluation (for when a non-formula specification is used) |
result |
A previous estimation object of class |
Details
-
bru_rerun
Continue the optimisation from a previously computed estimate.
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(s)
Fabian E. Bachl bachlfab@gmail.com
Examples
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$summary.fixed
}
if (bru_safe_inla(multicore = FALSE)) {
# Alternatively, we can use the like() function to construct the likelihood:
lik <- like(family = "gaussian", formula = y ~ x + Intercept, data = input.df)
fit <- bru(~ x + Intercept(1), lik)
fit$summary.fixed
}
# 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
}