inlabru (version 2.3.1)

generate: Generate samples from fitted bru models

Description

Generic function for sampling for fitted models. The function invokes particular methods which depend on the class of the first argument.

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.

Usage

generate(object, ...)
# S3 method for bru
generate(
  object,
  data = NULL,
  formula = NULL,
  n.samples = 100,
  seed = 0L,
  num.threads = NULL,
  include = NULL,
  exclude = NULL,
  ...
)

Arguments

object

A bru object obtained by calling bru().

...

additional, unused arguments.

data

A data.frame or SpatialPointsDataFrame of covariates needed for sampling.

formula

A formula defining an R expression to evaluate for each generated sample. If NULL, the latent and hyperparameter states are returned as named list elements. In addition to the component names (that give the effect of each component evaluated for the input data), the suffix _latent can be used to directly access the latent state for a component, and the suffix _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 defined with field(coordinates, ...). For "iid" models with mapper = bru_mapper_index(n), rnorm() is used to generate new realisations for indices greater than n.

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"

include

Character vector of component labels that are needed by the predictor expression; Default: NULL (include all components that are not explicitly excluded)

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)

Value

The form of the value returned by gg depends on the class of its argument. See the documentation of the particular methods for details of what is produced by that method.

List of generated samples

See Also

predict.bru

Examples

# NOT RUN {
if (bru_safe_inla(multicore = FALSE)) {

  # 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

  plot(do.call(rbind, smp))
}
# }
# NOT RUN {
if (bru_safe_inla(multicore = FALSE)) {

  # 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

  plot(do.call(rbind, smp))
}
# }