acfm: ACF and CCF for Multiple Time Series

In astsa: Applied Statistical Time Series Analysis

ACF and CCF for Multiple Time Series

Description

Produces a grid of plots of the sample ACF (diagonal) and CCF (off-diagonal).

Usage

acfm(series, max.lag = NULL,  na.action = na.pass, ylim = NULL,
      acf.highlight = TRUE, ...)

Arguments

series	Multiple time series (at least 2 columns of time series)
max.lag	Maximum lag. Can be omitted. Defaults to √{n} + 10 unless n < 60. If the series is seasonal, this will be at least 4 seasons by default.
na.action	How to handle missing data; default is na.pass
ylim	Specify limits for the all correlation axes. If NULL (default) the values are a little wider than the min and max of all values.
acf.highlight	If TRUE (default), the diagonals (ACFs) are highlighted.
...	Additional arguments passed to tsplot

Details

Produces a grid of plots of the sample ACF (diagonal) and CCF (off-diagonal). The plots in the grid are estimates of corr{x(t+LAG), y(t)}. Thus x leads y if LAG is positive and x lags y if LAG is negative.

Author(s)

D.S. Stoffer

References

You can find demonstrations of astsa capabilities at FUN WITH ASTSA.

The most recent version of the package can be found at https://github.com/nickpoison/astsa/.

In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.

The webpages for the texts and some help on using R for time series analysis can be found at https://nickpoison.github.io/.

Examples

acfm(diff(log(econ5)))

acfm(diff(log(econ5)), gg=TRUE, acf.highlight=FALSE)

astsa documentation built on Jan. 10, 2023, 1:11 a.m.