acfm function

ACF and CCF for Multiple Time Series

Produces a grid of plots of the sample ACF (diagonal) and CCF (off-diagonal). The values are returned invisibly.

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

Arguments

• series: Multiple time series (at least 2 columns of time series)
• max.lag: Maximum lag. Can be omitted. Defaults to $\sqrt{n} + 10$ 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.
• plot: If TRUE (default), you get a wonderful graphic.
• ...: Additional arguments passed to tsplot

Returns

The correlations are returned invisibly.

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. If plot is FALSE, then there is no graphic.

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/.

Author(s)

D.S. Stoffer

Examples

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

( acfm(diff(log(econ5)), 2, plot=FALSE) )

See Also

acf1, acf2, ccf2