Generates forecasts from forecast.ts
and adds them to the plot.
Forecasts can be modified via sending forecast specific arguments above.
GeomForecastgeom_forecast(mapping = NULL, data = NULL, stat = "forecast",
position = "identity", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE, level = c(80, 95), showgap = TRUE, model = ETS(y),
fc.args = list(), ...)
StatForecast
The data to be displayed in this layer. There are three options:
If NULL
, the default, the data is inherited from the plot data as
specified in the call to ggplot
.
A data.frame
, or other object, will override the plot data. All
objects will be fortified to produce a data frame. See fortify
for which variables will be created.
A function
will be called with a single argument, the plot data. The
return value must be a data.frame
, and will be used as the layer
data.
The stat object to use calculate the data.
Position adjustment, either as a string, or the result of a call to a position adjustment function.
If FALSE
(the default), removes missing values with a
warning. If TRUE
silently removes missing values.
logical. Should this layer be included in the legends?
NA
, the default, includes if any aesthetics are mapped. FALSE
never includes, and TRUE
always includes.
If FALSE
, overrides the default aesthetics, rather
than combining with them. This is most useful for helper functions that
define both data and aesthetics and shouldn't inherit behaviour from the
default plot specification, e.g. borders
.
A vector of numbers between 0 and 100 which define the confidence
range to be plotted. If NULL
, confidence intervals will not be plotted,
giving only the forecast line.
If showgap=FALSE
, the gap between the historical
observations and the forecasts is removed.
The time-series model used to produce the forecast. The data
must be y
(indicating aesthetic y
), and the time index for y
is determined from the
x
aesthetic.
A list of arguments to be used in the forecast
function
Additional arguments for forecast.ts
, other
arguments are passed on to layer
. These are often aesthetics,
used to set an aesthetic to a fixed value, like color = "red"
or
alpha = .5
. They may also be parameters to the paired geom/stat.
A layer for a ggplot graph.
An object of class GeomForecast
(inherits from Geom
, ggproto
, gg
) of length 7.
Multivariate forecasting is supported by having each time series on a different group.
You can also pass geom_forecast
a forecast
object to add it to
the plot.
The aesthetics required for the forecasting to work includes forecast
observations on the y axis, and the time
of the observations on the x
axis. Refer to the examples below. To automatically set up aesthetics, use
autoplot
.
# NOT RUN {
# }
# NOT RUN {
library(ggplot2)
library(tidyr)
library(tsibbledata)
UKLungDeaths %>%
gather(Gender, Deaths, -index) %>%
autoplot() +
geom_forecast()
# }
# NOT RUN {
# }
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