inst/examples/prediction.bru.R

In inlabru-org/inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

\donttest{
if (bru_safe_inla(multicore = FALSE) &&
    require(ggplot2, quietly = TRUE)) {

  # Generate some data
  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 a model with fixed effect 'x' and intercept 'Intercept'

  fit <- bru(y ~ x, family = "gaussian", data = input.df)

  # Predict posterior statistics of 'x'

  xpost <- predict(fit, formula = ~x)

  # The result is a data.frame inheriting from class 'prediction'

  class(xpost)

  # The statistics include mean, standard deviation, the 2.5% quantile, the median,
  # the 97.5% quantile, minimum and maximum sample drawn from the posterior as well as
  # the coefficient of variation and the variance.

  xpost

  # For a single variable like 'x' the default plotting method invoked by gg() will
  # show these statistics in a fashion similar to a box plot:

  ggplot() +
    gg(xpost)


  # The predict function can also be used to simultaneously estimate posteriors
  # of multiple variables:

  xipost <- predict(fit, formula = ~ data.frame(post = c(x, Intercept)))
  xipost

  # If we still want a plot in the previous style we have to set the 'bar' parameter to TRUE:

  rownames(xipost) <- c("x", "Intercept")
  ggplot() +
    gg(xipost, bar = TRUE)
}
}