There are several definitions of a periodgram. Note that
> log(2*pi)
[1] 1.837877
See the comments in ?spectrum about scalings.
I think the comments in ?per incorrectly ignore the scaling issues: per()
does not take the base frequency into account and has an extra divisor of
2*pi. E.g.
> x <- rnorm(64)
> spec.pgram(x, taper=0, detrend=F)$spec/per(x)[-1]
[1] 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185
[9] 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185
[17] 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185
[25] 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185 6.283185
On Wed, 12 Dec 2007, Lieven Desmet wrote:
> hello,
>
> I have been using the per function in package longmemo to obtain a
> simple raw periodogram.
> I am considering to switch to the function spec.pgram since I want to be
> able to do tapering.
> To compare both I used spec.pgram with the options as suggested in the
> documentation of per {longmemo} to make them correspond.
>
> Now I have found on a variety of examples that there is a shift between
> the log of the periodogram with per and that with spec.pgram. This
> vertical shift amounts to approx. 1.8 on the log scale (the graph of
> spec.pgram being above the one from per).
>
> Is there some explanation for this ? Is the one from spec.pgram the
> better one as suggested in the documentation of per {longmemo}? Finally
> how are these related to an estimate of the spectral density obtained
> from spec.arima ?
What is spec.arima? If you meant spec.ar, that is on the same scale as
spec.pgram for series with base frequency 1 (and for all series for R >=
2.7.0).
> Many thanks for help and clarification.
>
> Lieven Desmet
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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