View source: R/xyplot.forecast.R
xyplot.forecast | R Documentation |
Plot forecasts from forecast::forecast()
. It is built mostly to resemble
the forecast::autoplot.forecast()
and forecast::plot.forecast()
functions, but in addition tries to plot the predictions on the original
scale.
## S3 method for class 'forecast'
xyplot(
x,
data = NULL,
ci = TRUE,
ci_levels = x$level,
ci_key = ci,
ci_pal = hcl(0, 0, 45:100),
ci_alpha = trellis.par.get("regions")$alpha,
...
)
x |
An object of class |
data |
Data of observations left out of the model fit, usually "future" observations. |
ci |
Plot confidence intervals for the predictions. |
ci_levels |
The prediction levels to plot as a subset of those
forecasted in |
ci_key |
Set to |
ci_pal |
Color palette for the confidence bands. |
ci_alpha |
Fill alpha for the confidence interval. |
... |
Arguments passed on to |
This function requires the zoo package.
An object of class "trellis"
. The
update
method can be used to
update components of the object and the
print
method (usually called by
default) will plot it on an appropriate plotting device.
lattice::panel.xyplot()
, forecast::forecast()
, lattice::xyplot.ts()
.
if (require(forecast)) {
train <- window(USAccDeaths, c(1973, 1), c(1977, 12))
test <- window(USAccDeaths, c(1978, 1), c(1978, 12))
fit <- arima(train, order = c(0, 1, 1),
seasonal = list(order = c(0, 1, 1)))
fcast1 <- forecast(fit, 12)
xyplot(fcast1, test, grid = TRUE, auto.key = list(corner = c(0, 0.99)),
ci_key = list(title = "PI Level"))
# A fan plot
fcast2 <- forecast(fit, 12, level = seq(0, 95, 10))
xyplot(fcast2, test, ci_pal = heat.colors(100))
}
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