Observation model construction for usage with bru()

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 list
like_list(object, envir = NULL, ...)

# S3 method for bru_like
like_list(..., envir = NULL)

# S3 method for bru_like
c(..., envir = NULL)

# S3 method for bru_like_list
c(..., envir = NULL)

# S3 method for 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: logical, deprecated. Use include_latent instead.
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

Author

Fabian E. Bachl bachlfab@gmail.com

Finn Lindgren finn.lindgren@gmail.com

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)
}

# }