Displaying 20 results from an estimated 7000 matches similar to: "How do I "normalise" a power spectral density"
2006 Jan 27
2
How do I "normalise" a power spectral density analysis?
Hi everyone
Can anyone tell me how I normalise a power spectral density (PSD) plot of a
periodical time-series. At present I get the graphical output of spectrum VS
frequency.
What I want to acheive is period VS spectrum? Are these the same things but the
x-axis scale needs transformed ?
Any help would be greatly appreciated
Tom
2006 Feb 02
0
How do I normalize a PSD?
Dear Tom,
Short answer, if your using spec.pgram(), use the smoothing kernel to get a
better estimate at the frequency centered in the bandwidth. If your
frequency bin of interest is wider than the bandwidth of the kernel, average
across frequencies (I think). The estimate appears to be normalized already.
If you are calculating your PSD independently, then oversample (e.g. 2,
perhaps 4 or more
2007 Jan 08
2
Simple spectral analysis
Hello world,
I am actually trying to transfer a lecture from Statistica to
R and I ran into problems with spectral analysis, I think I
just don't get it 8-(
(The posting from "FFT, frequs, magnitudes, phases" from 2005
did not enlighten me)
As a starter for the students I have a 10year data set of air
temperature with daily values and I try to
get a periodogram where the annual
2011 Jul 11
1
Spectral Coherence
Greetings,
I would like to estimate a spectral coherence between
two timeseries. The stats : spectrum() returns a coh matrix
which estimates coherence (squared).
A basic test which from which i expect near-zero coherence:
x = rnorm(500)
y = rnorm(500)
xts = ts(x, frequency = 10)
yts = ts(y, frequency = 10)
gxy = spectrum( cbind( xts, yts ) )
plot( gxy $ freq, gxy $
2007 Dec 12
2
discrepancy between periodogram implementations ? per and spec.pgram
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
2008 Mar 27
6
help! - spectral analysis - spec.pgram
Can someone explain me this spec.pgram effect?
Code:
period.6<-c(0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10
,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10)
period.5<-c(0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0,10
,0,0,0,0,10,0,0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0,10,0,0,0,0,10,0)
par(mfrow=c(2,1))
2004 Oct 15
1
power in a specific frequency band
Dear R users
I have a really simple question (hoping for a really simple answer :-):
Having estimated the spectral density of a time series "x" (heart rate
data) with:
x.pgram <- spectrum(x,method="pgram")
I would like to compute the power in a specific energy band.
Assuming that frequency(x)=4 (Hz), and that I am interested in the band
between f1 and f2, is the
1999 Jul 19
9
time series in R
Time Series functions in R
==========================
I think a good basic S-like functionality for library(ts) in base R
would include
ts class, tsp, is.ts, as.ts
plot methods
start end window frequency cycle deltat
lag diff aggregate
filter
spectrum, spec.pgram, spec.taper, cumulative periodogram, spec.ar?
ar -- at least univariate by Yule-Walker
arima -- sim, filter, mle, diag, forecast
2007 Nov 28
0
Power Spectral Sensity
I am working with a dissolved oxygen dataset. continuous readings are taken
at 15 minute intervals and we have been recording these data at 12 stations
along the savannah river for two years now. The longest set of readings
that are continuous without interuption is 53 days. I would like to look at
the power spectral density at each of these sites (most likely one day will
be the overridding
2008 Dec 03
2
Spectral Analysis of Time Series in R
Dear R Community,
I am currently student at the Vienna University of Technology writing my
Diploma thesis on causality in time series and doing some analyses of
time series in R. I have the following questions:
(1) Is there a function in R to estimate the PARTIAL spectral coherence
of a multivariate time series? If yes, how does this work? Is there an
test in R if the partial spectral
2009 Mar 31
1
Lomb periodograms
Hi,
I have recently used the CTS package in order to use the Lomb-Scargle periodogram (spec.ls) function. I have noticed an issue that I hoped you may be able to explain. If a regularly spaced time series has two points removed, one at either side of a single data point (thus making an irregularly spaced time series), a spectrum with a very large peak at the highest frequencies is produced. An
2008 Jun 09
2
using spec.pgram
Hi everyone,
first of all, I would like to say that I am a newbie in R, so I apologize in
advance if my questions seem to be too easy for you.
Well, I'm looking for periodicity in histograms. I have histograms of
certain phenomenons and I'm asking whether a periodicity exists in these
data. So, I make a periodogram with the function spec.pgram. For instance,
if I have a histogram h, I
2005 Dec 01
1
squared coherency and cross-spectrum
Hi All,
I have two time series, each has length 354. I tried to calculate the
coherency^2 between them, but the value I got is always 1. On a website,
it says: " Note that if the ensemble averaging were to be omitted, the
coherency (squared) would be 1, independent of the data". Does any of
you know how to specify properly in R in order to get more useful
coherency? The examples in
2009 Mar 23
1
how to estimate multidimensional spectral measure of coherence
Please, does anyone know of an R packge to estimate multidimensional spectral measure of coherence within a moving time window ?
Some time ago I expeimented with a similar package that performs Cross Spectrum Analysis on the whole signal though.
Unluckily I deal with non-stationary signals whose properties change along with time. Therefore estimates can only be made over time periods
roughly
2007 Jun 06
3
Spectral analysis
Hi all,
I am dealing with paleoceanographic data and I have a C14 time serie and one other variable. I would like to perform a spectral analysis (fft or wavelet) and plot it. Unfortunately I don't know the exact script to do this. Does anybody could send me an example to perform my spectral analysis ?
I Thank you
David
Changez de tĂȘte et de tenue tous les jours si vous le voulez !
2004 Jan 22
1
spectrum
Dear R users
I have two questions about estimating the spectral power of a
time series:
1) I came across a funny thing with the following code:
data(co2)
par(mfrow=c(2,1))
co2.sp1<-spectrum(co2,detrend=T,demean=T,span=3)
co2.sp2<-spectrum(co2[1:468],detrend=T,demean=T,span=3)
The first plot displays the frequencies ranging from 0 to 6
whearas the second plot displays the same curve but
2007 Jul 09
1
When is the periodogram is consistent with white noise?
Hello everyone,
This is my first time posting to the list, thanks in advance.
I am calculating the smoothed periodogram for the residuals of an AR model
that I fit to EEG data. The autocorrelation plot of the residuals shows the
series is now approximately white (i.e. ACF = 1 at lag 0, and close to 0 for
all other lags). I would like to show that the spectrum of the series is
also
2008 Sep 09
4
Help with 'spectrum'
For the command 'spectrum' I read:
The spectrum here is defined with scaling 1/frequency(x), following S-PLUS. This makes the spectral density a density over the range (-frequency(x)/2, +frequency(x)/2], whereas a more common scaling is 2? and range (-0.5, 0.5] (e.g., Bloomfield) or 1 and range (-?, ?].
Forgive my ignorance but I am having a hard time interpreting this. Does this mean
2010 Nov 22
1
cpgram: access data, confidence bands
Dear R experts, beginners and everyone else,
I'm calculating "cumulative periodogram" using the command "cpgram"
[1] from the MASS library. Here is a short example with the "lh"
(hormone level) dataset:
library(MASS)
plot(lh,type="l",ylab="value",xlab="time", main="Hormone Levels (lh)")
spectrum(lh,
2007 Apr 10
1
Computing fundamental harmonics from a periodogram
Dear all,
I'm trying to finding the fundamental harmonics (ie. peaks in a
periodogram) from a time series (extracted from an mp3). For example,
if I look at
spectrum(fdeaths, spans = c(3,3))
I'd say the fundamental harmonics are about 1, 2, 3.5 and 4.5 - but
how can I extract these automatically? (preferably with some
heuristic for choosing the smoothing spans too)
I'm aware of