neat. I learn something everyday
On Mon, Nov 3, 2008 at 11:01 AM, Claudia Beleites <[EMAIL PROTECTED]> wrote:
>> Try http://finzi.psych.upenn.edu/R/library/nlts/html/spec.lomb.html or
>> http://finzi.psych.upenn.edu/R/library/cts/html/spec.ls.html (do
>> RSiteSearch("Lomb periodogram") --
>> the
> Try http://finzi.psych.upenn.edu/R/library/nlts/html/spec.lomb.html or
> http://finzi.psych.upenn.edu/R/library/cts/html/spec.ls.html (do
> RSiteSearch("Lomb periodogram") --
> the Lomb periodogram does a discrete (although not fast) Fourier
> transform of unevenly sampled (1D/time-series) data
> Claudia Beleites units.it> wrote:
> > Searching for discrete fourier transform I found lots of information and
> > functions - but I didn't see anything that just works
> > with irregularly spaced
> > signals: all functions I found take only the signal, not its x-axis.
> >
> > Where should I l
You need to decide how you are going to interpolate the values. Look
at the zoo package- na.approx() . spectrum() take the x axis and
produces a power spectrum vs. cycles/time. you may interpret this
however it makes ssnse- if it makes sense.
On Mon, Nov 3, 2008 at 4:23 AM, Claudia Beleites <[
Dear all,
I work with (vibrational) spectra: some kind of intensity (I) over frequency
(nu), wavelength or the like.
I want to do fourier transform for interpolation, smoothing, etc.
My problem is that the spectra are often irregularly spaced in nu: the
difference between 2 neighbouring nu var
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