squared coherency and cross-spectrum
I haven't seen a reply, so I will comment even though I've never used
"coherency" / "coherence" nor "spectrum". RSiteSearch("coherence")
produced 13 hits, the third of which looked like it might be relevant to
your question
(http://finzi.psych.upenn.edu/R/Rhelp02a/archive/37640.html).
RSiteSearch("coherency") produced 12 hits, at least some of which look
like they might help you. In my cursory review, it looked like at least
one of the "coherence" / "coherency" hits also mentioned the
co-spectrum. Whether that's true or not, the examples with "?spectrum"
includes the statement, "for multivariate examples see the help for
spec.pgram". If you still have a question for this listserve after
reviewing these references, PLEASE do read the posting guide!
'www.R-project.org/posting-guide.html'. I believe that people who
follow more closely that posting guide tend to receive quicker, more
useful answers than those who don't.
I hope you won't mind if I now ask you a question: What can you get
from "coherency" and "co-spectrum" that you can't get as easily from
autocorrelation and partial autocorrelation functions, including the
cross-correlations?
hope this helps.
spencer graves
Ling Jin wrote:
Hi All, I have two time series, each has length 354. I tried to calculate the coherency^2 between them, but the value I got is always 1. On a website, it says: " Note that if the ensemble averaging were to be omitted, the coherency (squared) would be 1, independent of the data". Does any of you know how to specify properly in R in order to get more useful coherency? The examples in the help do give coherencies that are not 1s, but I did not notice any special specification. Next question is on co-spectrum. When I supply "spectrum" function with multiple time series, it only gives me spectrum (smoothed periodogram) of individual time series. Is there any way I can get the cross-spectrum? I believe R has calculated it, but I could not find in the returned values. Attached is the smoothed periodogram of the two time series. Thanks a lot! Ling ------------------------------------------------------------------------ ------------------------------------------------------------------------
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