generate: Sampling based on bru posteriors

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

Sampling based on bru posteriors

Description

Takes a fitted bru object produced by the function bru() and produces samples given a new set of values for the model covariates or the original values used for the model fit. The samples can be based on any R expression that is valid given these values/covariates and the joint posterior of the estimated random effects. Called via the generics::generate() generic.

Usage

## S3 method for class 'bru'
generate(
  x,
  newdata = NULL,
  formula = NULL,
  n.samples = 100,
  seed = 0L,
  num.threads = NULL,
  used = NULL,
  options = NULL,
  ...,
  include = deprecated(),
  exclude = deprecated()
)

Arguments

x	A bru object obtained by calling bru().
newdata	A data.frame or SpatialPointsDataFrame of covariates needed for sampling.
formula	A formula where the right hand side defines an R expression to evaluate for each generated sample. If NULL, the latent and hyperparameter states are returned as named list elements. See Details for more information.
n.samples	Integer setting the number of samples to draw in order to calculate the posterior statistics. The default, 100, is rather low but provides a quick approximate result.
seed	Random number generator seed passed on to INLA::inla.posterior.sample
num.threads	Specification of desired number of threads for parallel computations. Default NULL, leaves it up to INLA. When seed != 0, overridden to "1:1:1"
used	Either NULL or a bru_used() object. Default, NULL, uses auto-detection of used variables in the formula.
options	A bru_options options object or a list of options passed on to bru_options()
...	additional, unused arguments.
include, exclude	If auto-detection of used variables fails, use used instead.

Details

In addition to the component names (that give the effect of each component evaluated for the input data), the suffix ⁠_latent⁠ variable name can be used to directly access the latent state for a component, and the suffix function ⁠_eval⁠ can be used to evaluate a component at other input values than the expressions defined in the component definition itself, e.g. field_eval(cbind(x, y)) for a component that was defined with field(coordinates, ...) (see also bru_comp_eval()).

For "iid" models with mapper = bm_index(n), rnorm() is used to generate new realisations for indices greater than n, if accessed via ⁠<name>_eval(...)⁠.

Value

The form of the value returned by generate() depends on the data class and prediction formula. Normally, a data.frame is returned, or a list of data.frames (if the prediction formula generates a list)

See Also

predict.bru()

Examples

if (
  bru_safe_inla() &&
    requireNamespace("sn", quietly = TRUE)
) {
  # Generate data for a simple linear model

  input.df <- data.frame(x = cos(1:10))
  input.df <- within(
    input.df,
    {
      y <- 5 + 2 * cos(1:10) + rnorm(10, mean = 0, sd = 0.1)
    }
  )

  # Fit the model

  fit <- bru(
    y ~ xeff(main = x, model = "linear"),
    family = "gaussian",
    data = input.df
  )
  summary(fit)

  # Generate samples for some predefined x

  df <- data.frame(x = seq(-4, 4, by = 0.1))
  smp <- generate(fit, df, ~ xeff + Intercept, n.samples = 10)

  # Plot the resulting realizations

  plot(df$x, smp[, 1], type = "l")
  for (k in 2:ncol(smp)) {
    points(df$x, smp[, k], type = "l")
  }

  # We can also draw samples form the joint posterior

  df <- data.frame(x = 1)
  smp <- generate(fit, df, ~ data.frame(xeff, Intercept), n.samples = 10)
  smp[[1]]

  # ... and plot them
  if (require(ggplot2, quietly = TRUE)) {
    plot(do.call(rbind, smp))
  }
}

inlabru documentation built on July 28, 2026, 9:07 a.m.