I wrote a little code using Fourier filtering if you would like to
take a look at this:
library(StreamMetabolism)
library(mFilter)
x <- read.production(file.choose())
#contiguous.zoo(data.frame(x[,"RM202DO.Conc"], coredata(x[,"RM202DO.Conc"])))
#contiguous.zoo(data.frame(x[,"RM61DO.Conc"], coredata(x[,"RM61DO.Conc"])))
short <- x[42685:48535,"RM202DO.Conc"]
#short <- x[53909:59957,"RM61DO.Conc"]
short.ts <- ts(coredata(short), frequency=96)
#fourier filtering
short.fft <- fft(short.ts)
plot(Re(short.fft), xlim=c(0,10), ylim=c(-1000, 1000))
short.fft[789:5563] = 0+0i
short.ifft = fft(short.fft, inverse = TRUE)/length(short.fft)
#zoo series
filt <- zoo(coredata(Re(short.ifft)) , index(short))
par(mfrow=c(2,1))
plot(short)
plot(filt)
window.plot <- function(x, y, a, b, s, d){
par(mfrow=c(2,1))
plot(window.chron(x, a, b, s, d))
plot(window.chron(y, a, b, s, d))
}
window.plot(short, filt, "04/17/2007", "00:01:00", "04/17/2007", "23:46:00")
plot.e <- function(b, w, x, y, z){
a <- window.chron(b, w, x, y, z)
plot(a, ylim=range(a)+0.06*c(-1, 1))
lines(a*0.98, col="blue")
lines(a*1.02, col="red")
}
it may not be exactly what you want, but you will have a handle on
what spectral properties that you have removed.
On Thu, Feb 26, 2009 at 9:54 AM, Ted Harding
<ted.hard...@manchester.ac.uk> wrote:
> On 26-Feb-09 13:54:51, David Winsemius wrote:
>> I saw Gabor's reply but have a clarification to request. You say you
>> want to remove low frequency components but then you request smoothing
>> functions. The term "smoothing" implies removal of high-frequency
>> components of a series.
>
> If you produce a smoothed series, your result of course contains
> the low-frequency comsponents, with the high-frequency components
> removed.
>
> But if you then subtract that from the original series, your result
> contains the high-frequency components, with the low-frequency
> compinents removed.
>
> Moving-average is one way of smoothing (but can introduce periodic
> components which were not there to start with).
>
> Filtering a time-series is a very open-ended activity! In many
> cases a useful start is exploration of the spectral properties
> of the series, for which R has several functions. 'spectrum()'
> in the stats package (loaded bvy default) is one basic function.
> help.search("time series") will throw up a lot of functions.
>
> You might want to look at package 'ltsa' (linear time series
> analysis).
>
> Alternatively, if yuou already have good information about the
> frequency-structure of the series, or (for instance) know that
> it has a will-defined seasonal component, then you could embark
> on designing a transfer function specifically tuned to the job.
> Have a look at RSiteSearch("{transfer function}")
>
> Hoping this helps,
> Ted.
>
>
>
>> If smoothing really is your goal then additional R resource would be
>> smooth.spline, loess (or lowess), ksmooth, or using smoothing terms in
>> regressions. Venables and Ripley have quite a few worked examples of
>> such in MASS.
>>
>> --
>> David Winsemius
>>
>>
>> On Feb 26, 2009, at 7:07 AM, <mau...@alice.it> wrote:
>>
>>> I am looking for some help at removing low-frequency components from
>>> a signal, through Moving Average on a sliding window.
>>> I understand thiis is a smoothing procedure that I never done in my
>>> life before .. sigh.
>>>
>>> I searched R archives and found "rollmean", "MovingAverages {TTR}",
>>> "SymmetricMA".
>>> None of the above mantioned functions seems to accept the smoothing
>>> polynomial order and the sliding window with as input parameters.
>>> Maybe I am missing something.
>>>
>>> I wonder whether there is some building blocks in R if not even a
>>> function which does it all (I do not expect that much,though).
>>> Even some literature references and/or tutorials are very welcome.
>>>
>>> Thank you so much,
>>> Maura
>>>
>>>
>>>
>>> tutti i telefonini TIM!
>>>
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-help@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide
>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
> --------------------------------------------------------------------
> E-Mail: (Ted Harding) <ted.hard...@manchester.ac.uk>
> Fax-to-email: +44 (0)870 094 0861
> Date: 26-Feb-09 Time: 14:54:43
> ------------------------------ XFMail ------------------------------
>
> ______________________________________________
> R-help@r-project.org mailing list
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Stephen Sefick
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.
-K. Mullis
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