This geom serves to visualize `prediction`

objects which usually results from a call to
`predict.bru()`

. Predictions objects provide summary statistics (mean, median, sd, ...) for
one or more random variables. For single variables (or if requested so by setting `bar = TRUE`

),
a boxplot-style geom is constructed to show the statistics. For multivariate predictions the
mean of each variable (y-axis) is plotted against the row number of the variable in the prediction
data frame (x-axis) using `geom_line`

. In addition, a `geom_ribbon`

is used to show
the confidence interval.

Note: `gg.bru_prediction`

also understands the format of INLA-style posterior summaries, e.g.
`fit$summary.fixed`

for an inla object `fit`

Requires the `ggplot2`

package.

## Arguments

- data
A prediction object, usually the result of a

`predict.bru()`

call.- mapping
a set of aesthetic mappings created by

`aes`

. These are passed on to`geom_line`

.- ribbon
If TRUE, plot a ribbon around the line based on the smallest and largest quantiles present in the data, found by matching names starting with

`q`

and followed by a numerical value.`inla()`

-style`numeric+"quant"`

names are converted to inlabru style before matching.- alpha
The ribbons numeric alpha (transparency) level in

`[0,1]`

.- bar
If TRUE plot boxplot-style summary for each variable.

- ...
Arguments passed on to

`geom_line`

.

## See also

Other geomes for inla and inlabru predictions:
`gg()`

,
`gg.data.frame()`

,
`gg.matrix()`

## Examples

```
# \donttest{
if (bru_safe_inla() &&
require(sn, quietly = TRUE) &&
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, 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 simultaneously estimate posteriors
# of multiple variables:
xipost <- predict(fit,
newdata = 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")
}
# }
```