## S3 method for class 'prediction'
plot(x, y = NULL, ...)

if (bru_safe_inla()) {
  # 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, data = NULL, formula = ~x_latent)

  # 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 statisics in a fashion similar to a box plot:
  ggplot() +
    gg(xpost)


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

  xipost <- predict(fit,
    data = NULL,
    formula = ~ c(
      Intercept = Intercept_latent,
      x = x_latent
    )
  )
  xipost

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

  p1 <- ggplot() +
    gg(xipost, bar = TRUE)
  p1

  # Note that gg also understands the posterior estimates generated while running INLA

  p2 <- ggplot() +
    gg(fit$summary.fixed, bar = TRUE)
  multiplot(p1, p2)

  # By default, if the prediction has more than one row, gg will plot the column 'mean' against
  # the row index. This is for instance usefuul for predicting and plotting function
  # but not very meaningful given the above example:

  ggplot() +
    gg(xipost)

  # For ease of use we can also type

  plot(xipost)

  # This type of plot will show a ribbon around the mean, which viszualizes the upper and lower
  # quantiles mentioned above (2.5 and 97.5%). Plotting the ribbon can be turned of using the
  # \code{ribbon} parameter

  ggplot() +
    gg(xipost, ribbon = FALSE)

  # Much like the other geomes produced by gg we can adjust the plot using ggplot2 style
  # commands, for instance

  ggplot() +
    gg(xipost) +
    gg(xipost, mapping = aes(y = median), ribbon = FALSE, color = "red")
}