inlabru (version 2.1.1)

bru: Convenient model fitting using (iterated) INLA

Description

This method is a wrapper for 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 = y ~ Intercept, family = NULL, data = NULL, ...,
  options = list())

Arguments

components

a formula describing the latent components. See bru.components for details.

family

A string indicating the likelihood family. The default is gaussian with identity link. In addition to the likelihoods provided by inla (see inla.models()$likelihood) inlabru supports fitting Cox processes via family = "cp". The latter requires contructing a likelihood using the like function and providing it via the ... parameter list. As an alternative to bru, the lgcp function provides a convenient interface to fitting Cox processes.

data

A data.frame or SpatialPoints[DataFrame] object.

...

Additional likelihoods, each constructed by a calling like.

options

A list of name and value pairs that are either interpretable by bru.options or valid inla parameters.

Value

bru returns an object of class "bru". A bru object inherits from inla (see the inla documentation for its properties) and adds additional information stored in the sppa field.

Examples

# NOT RUN {
# 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, "gaussian", input.df)

# Obtain summary
summary(fit)

# Alternatively, we can use the like() function to construct the likelihood:

lik = like(family = "gaussian", data = input.df)
fit <- bru(y ~ x + Intercept, lik)
summary(fit)

# 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. For instance

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, E = 10000)
fit <- bru( ~ z + Intercept, lik)

# Check the result (z posterior should be around 2)
summary(fit)
# }