Hi
I'm sorry, but I don't think that coherence is the same as the cross
spectrum. People use coherence since it is much easier to deal with. I know
how by using R to plot and calculate the coherence and phase, but what I
didn't know is how to calculate the cross spectrum by using R.
Regards
Aft
On Sep 2, 2010, at 1:00 AM, aftar wrote:
Hi
Is anyone know how could I find the cross spectrum?
from the spectrum function, it only give the spectrum for each
individual
series.
I read the manual very differently. It is telling me that you _do_ get
cross spectrum results in the "coh"
Hi
Is anyone know how could I find the cross spectrum?
from the spectrum function, it only give the spectrum for each individual
series.
Thanks
Aftar
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R function "spectrum" expects a time series as input. I have attached a
compressed archive with two detrended and denoised signals (txt format)
whose spectra I would like to compare.
I start out trying to generate a multivariate time series.
Please, notice the different signals length. Moreover, R
is this a problem? are there error messages? if so could you provide
them. Try
as.matrix(yourdata). One thing you could do is create a moving average that
reduces the signals to the lowest common denominator. Could you provide
reproducable code with maybe a toy data set so anybody could have a
In the case of muitivariate, from the documentation it looks like I can
compare more than two signals at a time.
Each column of the input matix seem to accommodate a signal.
The problem is that my signals do NOT have the same number of samples
(length).
They were all collected at 30Hz so the sampli
$names
[1] "freq" "spec" "coh" "phase" "kernel""df"
[7] "bandwidth" "n.used""orig.n""series""snames""method"
[13] "taper" "pad" "detrend" "demean"
$freq and $spec are used to plot the power spectrum. freq is the x-axis and
spec is the y-axis.
I am reading some documentation about Cross Spectrum Analysis as a technique
to compare spectra.
My understanding is that it estimates the correlation strength between
quasi-periodic structures embedded in two signals. I believe it may be
useful for my signals analysis.
I was referred to the R
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