look at the spectrums before you do the cbind - I would not suggest letting
R wrap the data to fill in a data frame.  I would suggest using something
that you "know how it acts" in the frequency domain like zero.  You are
probably introducing periodicies that are not real, and I would suggest not
to go down this path.  As for finding commonalities amongst signals- it all
depends on what a commonality is in the signal of interest.  I am sure if
you refine your question an answer can be found.  I have used beam forming
to look for common peaks among dissolved oxygen signals at different miles
along the river- a physicist friend wrote the code in matlab- so I could
provide that, but I haven't looked into trying to make it and R specific
algorithm- and in reality I am not sure if my programming is to the point of
being able to do something like that.  hope this helps

On Sun, May 4, 2008 at 8:48 PM, Maura E Monville <maura.monville@gmail.com>
wrote:

> The attached picture is what I get passing the time series where the
> shorter signal is wrapped around.
>
> > s10146 <- read.table("10146-Clean-Signal.txt")
> > s45533 <- read.table("45533-Clean-Signal.txt")
> > v10146 <- as.vector(s10146[,1])
> > length(v10146)
> [1] 8133
> > v45533 <- as.vector(s45533[,1])
> > length(v45533)
> [1] 6764
> > xx <-cbind(v10146, v45533)
> > dim(xx)
> [1] 8133    2
> > v45533[1:10]
>  [1] -1.7721546 -1.7482835 -1.6964711 -1.6154405 -1.5045701 -1.3747449
>  [7] -1.2332980 -1.0912172 -0.9585821 -0.8420886
> > xx[6760:6770,]
>           v10146     v45533
>  [1,] -0.8585375 -0.6076069
>  [2,] -0.8060065 -0.5288312
>  [3,] -0.7541174 -0.4447711
>  [4,] -0.7028816 -0.3592778
>  [5,] -0.6524279 -0.2767786
>  [6,] -0.6027233 -1.7721546    # start replicating shorter signal
>  [7,] -0.5536868 -1.7482835
>  [8,] -0.5052780 -1.6964711
>  [9,] -0.4574095 -1.6154405
> [10,] -0.4097922 -1.5045701
> [11,] -0.3623641 -1.3747449
>
> > twosig <- ts(xx,deltat=0.033,start=0)  # time series
> > spectrum(twosig)
>
> I*s there any quantitative analysis, operating in the frequency domain,
> that can help me identify common pattern features in signals ?*
>
> Thank you very much.
>
> --
> Maura E.M




-- 
Let's not spend our time and resources thinking about things that are so
little or so large that all they really do for us is puff us up and make us
feel like gods. We are mammals, and have not exhausted the annoying little
problems of being mammals.

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