plot_collection {vctsfr} | R Documentation |
Create a ggplot
object associated with a time series belonging to a
collection.
Description
Apart from the time series, future values and forecasts for the
future values form part of the ggplot
object.
Usage
plot_collection(collection, number, methods = NULL, level = NULL, sdp = TRUE)
Arguments
collection |
a list with the collection of time series. Each component
of the list must have been built with the |
number |
an integer. The number of the time series. It should be a value
between 1 and |
methods |
NULL (default) or a character vector indicating the names of the forecasting methods to be displayed. |
level |
NULL (default) or a number in the interval (0, 100) indicating the level of the prediction interval to be shown. This parameter in considered only when just one forecasting method is plotted and the forecasting method has a prediction interval with the specified level. |
sdp |
logical. Should data points be shown in the plot? (default value
|
Details
The collection
parameter must be a list. Each component of the list stores
a time series and, optionally, its future values, forecasts for the future
values and prediction intervals for the forecasts. Each component should have
been created using the ts_info()
function.
In the example section you can see an example of a collection of time series.
If the collection
parameter is not specified correctly, a proper message is
shown.
Value
The ggplot
object representing the time series and its forecast.
See Also
ts_info()
function to see how to build the components of the
collection
parameter.
Examples
# create a collection of two time series and plot both time series
c <- list(ts_info(USAccDeaths), ts_info(ldeaths))
plot_collection(c, number = 1)
plot_collection(c, number = 2, sdp = FALSE)
# create a collection of one time series with future values and forecasts
if (require(forecast)) {
c <- vector(2, mode = "list")
timeS <- window(USAccDeaths, end = c(1977, 12))
f <- window(USAccDeaths, start = c(1978, 1))
ets_fit <- ets(timeS)
ets_pred <- forecast(ets_fit, h = length(f), level = 90)
mean_pred <- meanf(timeS, h = length(f), level = 90)
c[[1]] <- ts_info(timeS, future = f,
prediction_info("ES", ets_pred$mean,
pi_info(90, ets_pred$lower, ets_pred$upper)),
prediction_info("Mean", mean_pred$mean,
pi_info(90, mean_pred$lower, mean_pred$upper))
)
timeS <- ts(rnorm(30, sd = 3))
f <- rnorm(5, sd = 3)
rw <- rwf(timeS, h = length(f), level = 80)
mean <- meanf(timeS, h = length(f), level = 90)
c[[2]] <- ts_info(timeS, future = f,
prediction_info("Random Walk", rw$mean,
pi_info(80, rw$lower, rw$upper)),
prediction_info("Mean", mean$mean,
pi_info(90, mean$lower, mean$upper))
)
plot_collection(c, number = 1)
}
if (require("forecast"))
plot_collection(c, number = 2)
if (require("forecast"))
plot_collection(c, number = 2, methods = "Mean") # just plot a forecasting method
if (require("forecast"))
plot_collection(c, number = 2, methods = "Random Walk", level = 80)