search for: periodograms

...arly seen in plots versus frequency as straight lines (log power vs log frequency), but would come out as curves in plots versus period. I can see how ecological studies may indeed be more interested in the periods. However, I would be wary of using the periodogram function, for if I calculate periodograms of the same sinewave but for differing lengths of the sample, the spectral density does not come out the same. All 4 of the plots produced by the code below should overlay, and yet as the time series becomes longer there appears to be an increasing offset of the magnitudes returned. (black-brown-bl...

Dear Prof Ripley, First of all, unless you are an english professor, then I do not think you have any business policing language. I'm still very much a student, both in R, and regarding signal analysis. My competence on the subject as compared too your own level of expertise, or my spelling for that matter, may be a contension for you, but it would have been better had you kept that opinion

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

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,

...arly seen in plots versus frequency as straight lines (log power vs log frequency), but would come out as curves in plots versus period. I can see how ecological studies may indeed be more interested in the periods. However, I would be wary of using the periodogram function, for if I calculate periodograms of the same sinewave but for differing lengths of the sample, the spectral density does not come out the same. All 4 of the plots produced by the code below should overlay, and yet as the time series becomes longer there appears to be an increasing offset of the magnitudes returned. (black-brown-bl...

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

...utomatically? (preferably with some heuristic for choosing the smoothing spans too) I'm aware of heuristic methods for finding peaks for general sequences (as found by searching for peaks on R-help) but I have a feeling there's something with some theoretical justification for the case of periodograms. Could anyone point me in the right direction in terms of theory or an R package that implements an approach? Thanks, Hadley

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

Hi, Does anyone know the name of the package that includes a function for computing the lomb periodogram on irregular spaced ts data? I saw the package once ~ 1 month ago but cannot find it now ... , Rich

Greetings all, I am using Fourier analysis to search for periodicities in IP network traffic by generating periodograms and then visually examining them for large, distinct peaks. However, in many cases it is not readily apparent where there are periodicities. I have no experience with discrete maths so I've come up against a block here: How do I define what the "noise floor" is and what peaks risi...

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

I am trying to compute the periodicity of a time series. I would like to know which function in R does it. Also, how do I plot a Enright / Chi-square periodogram using R ? ( Enright, J.T., 1965, Journal of Theoret. Biol. 8,426-468) Greatly appreciate your help. Thanks in advance, Sukhaswami Malladi *************************************************************************** The contents

Dear All, Is there a function in R which can do a periodogram of Schuster ? Thanks in advance ! Guillaume Blanchet

nitish wrote: > > I have a dataset that goes like: dataset = > t |x > 0 |x1 > 1 |x2 > 2 |0 > 3 |0 > 4 |0 > 5 |0 > 6 |x3 > 7 |0 > 8 |0 > 9 |0 > 10 |x4 > > and so on. I wish to detect the periodicity of occurrences. t is in > seconds and x are arbitrary, whose magnitude i am not interested in. I > just wish to get a best

...ntity? If so, perhaps someone could indicate why that particular norming convention is used? The reason that this is so important is that the harmonic content fit by least squares used in the chi-square periodogram is normalized to the variance (I think), and so too is the Lomb periodogram. Those periodograms can be used to get p-values from chi-square and exponential distributions respectively, too assess "important" frequencies, but they're very slow transforms. The fast fourier transform and/or periodograms can be related too both methods, for fast computation (albeit, especially for th...

Dear R-Users, I am doing a research on Time Series, especially on the estimation of the fractional exponent in long memory time series (for those who know). However there are three estimators already built-in the fracdiff package (GPH, Sperio, MLE) I was wondering if there is someone who had used an estimation introduced by P.M. Robinson (related paper: "Log-Periodogram regression of time

Hello - I am currently simulating bivariate AR(1) time series data and have the following line in my code: Px=spec.pgram(ts.union(X,XX),spans=c(?,?)) The spans option is where I enter in the vector containing the Daniell smoother numbers, but I don't know what a Daniell smoother is (hence the question marks). Can somebody please tell me? Is there another option where I can simply enter in

Hello! I tried to contact author of the package, but I got no reply. That is why I write it here. This might be useful for those who were using cts for spectral analysis of non-uniformly spaced data. In file spec.ls.R from cts_1.0-1.tar.gz lines 59-60 are written as pgram[k, i, j] <- 0.5 * ((sum(x[1:length(ti)]* cos(2 * pi * freq.temp[k] * (ti - tao))))^2/sum((cos(2 * pi * freq.temp[k] *

Dear R users, I am having a problem with the main title of a plot. I have written a function returning a plot and I would like that the main title of this plot shows actually the name of the dataset I have used. So, for instance: if my dataset is called Chl, I would like that my plot to be entitled : "Raw periodogram of Chl". I know the function "paste". I suppose I would be

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 $