StatForecast | R Documentation |
Generates forecasts from forecast.ts
and adds them to the plot.
Forecasts can be modified via sending forecast specific arguments above.
StatForecast
GeomForecast
geom_forecast(
mapping = NULL,
data = NULL,
stat = "forecast",
position = "identity",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
PI = TRUE,
showgap = TRUE,
series = NULL,
...
)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The stat object to use calculate the data. |
position |
Position adjustment, either as a string, or the result of a call to a position adjustment function. |
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
PI |
If |
showgap |
If |
series |
Matches an unidentified forecast layer with a coloured object on the plot. |
... |
Additional arguments for |
An object of class StatForecast
(inherits from Stat
, ggproto
, gg
) of length 3.
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
.
A layer for a ggplot graph.
Mitchell O'Hara-Wild
forecast
, ggproto
## Not run:
library(ggplot2)
autoplot(USAccDeaths) + geom_forecast()
lungDeaths <- cbind(mdeaths, fdeaths)
autoplot(lungDeaths) + geom_forecast()
# Using fortify.ts
p <- ggplot(aes(x=x, y=y), data=USAccDeaths)
p <- p + geom_line()
p + geom_forecast()
# Without fortify.ts
data <- data.frame(USAccDeaths=as.numeric(USAccDeaths), time=as.numeric(time(USAccDeaths)))
p <- ggplot(aes(x=time, y=USAccDeaths), data=data)
p <- p + geom_line()
p + geom_forecast()
p + geom_forecast(h=60)
p <- ggplot(aes(x=time, y=USAccDeaths), data=data)
p + geom_forecast(level=c(70,98))
p + geom_forecast(level=c(70,98),colour="lightblue")
#Add forecasts to multivariate series with colour groups
lungDeaths <- cbind(mdeaths, fdeaths)
autoplot(lungDeaths) + geom_forecast(forecast(mdeaths), series="mdeaths")
## End(Not run)
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