similar to: Spectrum confidence interval

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

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

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

Hi, everyone, I tried to use "spectrum()" or "spec.pgram()" to get a periodogram of a time series but they didn't work. Even the examples given in the help file didn't work (all with the same error message, below). And the 'ts'ibrary was loaded with "library(ts)" or "library("ts"). I also tried library(tseries) but got the same problem.

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

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,

Hello, I propose two small changes to spec.pgram to get modest speedup when dealing with input (x) having multiple columns. With plot = FALSE, I commonly see ~10-20% speedup, for a two column input matrix and the speedup increases for more columns with a maximum close to 45%. In the function as it currently exists, only the upper right triangle of pgram is necessary and pgram is not returned by

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

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))

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 list, What on earth is spec.pgram() normalized too? If you would like to skip my proof as to why it's not normed too the mean squared or sum squared amplitude of the discrete function a[], feel free too skip the rest of the message. If it is, but you know why it's not exact in spec.pgram() when it should be, skip the rest of this message. The issue I refer herein refers only too a

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

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

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] *

There is now a preliminary version of a time series package in the R-devel snapshots, and we would welcome feedback on it. It is based in part on the packages bats (Martyn Plummer) and tseries (Adrian Trapletti) and in part on code I had or have written. (Thanks for the contributions, Martyn and Adrian!) Some of the existing ts code has been changed, for example to plot multiple time series, so

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

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

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

Any Ideas to get an interactive periodogram? -- 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

well if you want to find the spectral density aka what frequencies explain most of the variance then I would suggest the spectral density. This can be implemented with spec.pgram(). This is conducted with the fast fourier transform algorithm. a<-ts(data, frequency = 1) #make the time series with 365readings/365days ?spec.pgram and you should be able to take it from here This will