tsdisplay: Time series display

In forecast: Forecasting Functions for Time Series and Linear Models

Time series display

Description

Plots a time series along with its acf and either its pacf, lagged scatterplot or spectrum.

Usage

ggtsdisplay(
  x,
  plot.type = c("partial", "histogram", "scatter", "spectrum"),
  points = TRUE,
  smooth = FALSE,
  lag.max,
  na.action = na.contiguous,
  theme = NULL,
  ...
)

tsdisplay(
  x,
  plot.type = c("partial", "histogram", "scatter", "spectrum"),
  points = TRUE,
  ci.type = c("white", "ma"),
  lag.max,
  na.action = na.contiguous,
  main = NULL,
  xlab = "",
  ylab = "",
  pch = 1,
  cex = 0.5,
  ...
)

Arguments

x	a numeric vector or time series of class ts.
plot.type	type of plot to include in lower right corner.
points	logical flag indicating whether to show the individual points or not in the time plot.
smooth	logical flag indicating whether to show a smooth loess curve superimposed on the time plot.
lag.max	the maximum lag to plot for the acf and pacf. A suitable value is selected by default if the argument is missing.
na.action	function to handle missing values in acf, pacf and spectrum calculations. The default is na.contiguous. Useful alternatives are na.pass and na.interp.
theme	Adds a ggplot element to each plot, typically a theme.
...	additional arguments to acf.
ci.type	type of confidence limits for ACF that is passed to acf. Should the confidence limits assume a white noise input or for lag k an MA(k-1) input?
main	Main title.
xlab	X-axis label.
ylab	Y-axis label.
pch	Plotting character.
cex	Character size.

Details

ggtsdisplay will produce the equivalent plot using ggplot graphics.

Value

None.

Author(s)

Rob J Hyndman

References

Hyndman and Athanasopoulos (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. https://otexts.com/fpp2/

See Also

plot.ts, Acf, spec.ar

Examples

library(ggplot2)
ggtsdisplay(USAccDeaths, plot.type="scatter", theme=theme_bw())

tsdisplay(diff(WWWusage))
ggtsdisplay(USAccDeaths, plot.type="scatter")

forecast documentation built on June 22, 2024, 9:20 a.m.