similar to: coherency and phase plots

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 $

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

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] *

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

Hello everybody I'm trying to write a simple version of matlab's "quiver". The idea is that I have fluid with velocity defined on a grid. I have a matrix of x-components of velocity and a matrix of y-components and I want to see the overall flow pattern. (I work with 2D fluid mechanics problems). My first-stab function is below: quiver <- function(u,v,scale=1) # first

Hi, I'm trying to determine the phase of irregularly sampled data. Is there any particular reason why both spec.pgram and spec.ls return phase<-NULL for vectors? Thank you. Lisandro xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxx Lisandro Benedetti-Cecchi Associate Professor in Ecology Department of Biology - University of Pisa Via

Hello all, I''m having fun again with the arima function. This time I read in: http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm <<It has recently been suggested (by a reliable source) that using xreg in arima() does NOT fit an ARMAX model [insert slap head icon here]. This will be investigated as soon as time permits.>> (by R.H. Shumway & D.S. Stoffer)

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 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

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))

Hi, I am far from experienced in both R and time series hence the question. The code for spec.pgram() seems to involve a circularity of the kernel (see below) yielding new power estimates to all frequencies computed by FFT. " if (!is.null(kernel)) { for (i in 1:ncol(x)) for (j in 1:ncol(x)) pgram[, i, j] <- kernapply(pgram[, i, j], kernel, circular = TRUE)

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 list, I've recently become interested in comparing the spectral estimates using the different methods ("pgram" and "ar") in the spectrum() function in the stats package. With many thanks to the authors of these complicated functions, I would like to point out what looks to me like a bit of an inconsistency -- but I would not be surprised if there is good reasoning

Dear all, This is potentially a bug in spec.pgram, when the number of samples is odd,spec.pgramreturns a different result withfast = TRUE, the example below contains the two varieties with a reference spectrum calculated manually. the number of returned spectra is also larger (50 compared to 49) whenfast = TRUE x <- rnorm( 99 ) plot(spec.pgram(x, taper = 0 , detrend = FALSE , plot =

Dear R-Help, I wish to simulate a process so that it has certain properties in the frequency domain. What I attempted was to generate a random time-series signal, use spec-pgram(), apply a function in the frequency domain, and then inverse transform back to the time-domain. This idea does not seem as straight forward in practice as I anticipated. e.g. x<-ts(rnorm(1000, 0,1), frequency=256)

Hi, Are there any functions in R that could be used to estimate the phase-shift between two semi-sinusoidal vectors? Here is what I have tried so far, using the spectrum() function -- possibly incorrectly: # generate some fake data, normalized to unit circle x <- jitter(seq(-2*pi, 2*pi, by=0.1), amount=pi/8) # functions defining two out-of-phase phenomena f1 <- function(x)

Hello All, I wanted to perform a fourier transform on high frequency financial data. I have searched and have not found much on this topic for R. I was wondering if anyone has used any libraries for it or have come across any papers I may read. Many Thanks, Neil Gupta [[alternative HTML version deleted]]

Dear useRs, I'd like to plot a confidence interval on a periodogram. My problem is that spec.pgram(sunspots,ci=0.95,log="yes") gives me a blue error bar on the plot, but spec.pgram(sunspots,ci=0.95,log="no") does not. My questions are: 1. how should I plot the confidence interval with log="no"? 2. how should I get the min and max values of the confidence

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

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