inlabru: generate – R documentation

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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 class '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

Examples

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))
}


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))
}

inlabru

Bayesian Latent Gaussian Modelling using INLA and Extensions

v2.3.1

GPL (>= 2)

Authors

Finn Lindgren [aut, cre, cph] (<https://orcid.org/0000-0002-5833-2011>, Finn Lindgren continued development of the main code), Fabian E. Bachl [aut, cph] (Fabian Bachl wrote the main code), David L. Borchers [ctb, dtc, cph] (David Borchers wrote code for Gorilla data import and sampling, multiplot tool), Daniel Simpson [ctb, cph] (Daniel Simpson wrote the basic LGCP sampling method), Lindesay Scott-Howard [ctb, dtc, cph] (Lindesay Scott-Howard provided MRSea data import code), Seaton Andy [ctb] (Andy Seaton provided testing and bugfixes)

Initial release