Nothing
\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)
}
}
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