This geom constructor will simply call gg.prediction for the data provided.

## Usage

# S3 method for data.frame
gg(...)

## Arguments

...

Arguments passed on to gg.prediction().

## Value

Concatenation of a geom_line value and optionally a geom_ribbon value.

## Details

Requires the ggplot2 package.

Other geomes for inla and inlabru predictions: gg.matrix(), gg.prediction(), gg(), gm()

## Examples

# \donttest{
if (bru_safe_inla() && 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, 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")
}

# }