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, ...)
The correlations are returned invisibly.
Multiple time series (at least 2 columns of time series with more than 3 observations).
Maximum lag. Can be omitted. Unless \(n < 60\), defaults to \(\sqrt{n} + 10\) or at least 4 seasons if the series is seasonal.
How to handle missing data; default is na.pass
Specify limits for the all correlation axes. If NULL (default) the values are a little wider than the min and max of all values.
If TRUE (default), the diagonals (ACFs) are highlighted.
If TRUE (default), you get a wonderful graphic.
Additional arguments passed to tsplot
D.S. Stoffer
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.
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/.
acf1
, acf2
, ccf2
acfm(diff(log(econ5)), gg=TRUE)
acfm(diff(log(econ5)), 2, plot=FALSE)
Run the code above in your browser using DataLab