similar to: Help with 'spectrum'

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

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

Dear list, I apologies first for my English, hope you will understand well my question. I am working on 1/2 hour piezometric data, time unit is second. They present daily oscillation when using the spectrum() function. What I am really interested in, is to find the amplitude corresponding to this oscillation. I work with a college using Matlab, and although we apply the same methodology, our

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

I have done a fair bit of spectral analysis, and hadn't finished collecting my thoughts for a reply, so hadn't replied yet. What exactly do you mean by normalize? I have not used the functons periodogram or spectrum, however from the description for periodogram it appears that it returns the spectral density, which is already normalized by frequency, so you don't have to worry about

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

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 tête et de tenue tous les jours si vous le voulez !

Hi, I'm wondering why the spectrum() phase of quadrature couple isn't purely +/-pi. But mostly, I'm looking for a recommended way to take a 2-D spectrum and convert it into a single complex array. Kindly consider: # 10 Hz sine wave 10 seconds long sampled at 50 Hz deltaT = 1/50 t = seq(0, 10, deltaT) w = 2 * pi * 10 x = ts( sin( w * t ), deltat = deltaT ) y = ts( sin(

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. Does anyone know how to generate a power spectrum from the STFT function in package e1071? The output for this function supplies the Fourier coefficients, but I do not know how to relate these back to the power spectrum. Alternatively, if anyone is aware of other packages/functions that perform short time Fourier transforms, that would also be helpful. Thanks. -- View this message in

I am reading some documentation about Cross Spectrum Analysis as a technique to compare spectra. My understanding is that it estimates the correlation strength between quasi-periodic structures embedded in two signals. I believe it may be useful for my signals analysis. I was referred to the R functions that implement this type of analysis. I tried all the examples which generated a series of

I am puzzled by a filtering problem using fft(). I don't blame R. I have a waveform y consisting of the sum of 2 sinewaves having freqs f1 and f2. I do s = fft() of y. Remove s's spike at freq=f2 Do inverse fft on s. The resulting waveform still has a lot of f2 in it! But the filtering should have removed it all. What is going on, and how to fix?? Thanks very much for any help. Bill

Hi, I've been using Speex in my voice-over-IP program on Win32, in wideband (16kHz) mode. I just starting using VBR recently and have run into something that might be a problem within Speex: If someone hasn't spoken for a little while, and the bitrate drops to very low, sometimes the high half of the spectrum becomes frozen with a looping sound. The bottom half of the spectrum is

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 there! I'd just like to say in advance, "Thank you," for any help and/or advice. My problem is as follows: I have a dataset that is made up of percentages. I've assigned my "error" percentages a value of '-100', my "non-existent" percentages a value of '0', and all my other percentages are normal values that range from the high

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

Thank you, Andreas Engel. I downloaded the white paper of the Fraunhofer Acoustic Echo Control. http://www.iis.fraunhofer.de/bf/amm/download/whitepapers/Acoustic_Echo_Control-wp.pdf It said > "In the Fraunhofer Acoustic Echo Control, the frequency spectrum of the microphone signal is > modified so that the undesired echo components are removed from the signal transmitted to > the

I wrote a script that I anticipating seeing a spike at 10Hz with the function 10* sin(2*pi*10*t). I can't figure out why my plots do not show spikes at the frequencies I expect. Am I doing something wrong or is my expectations wrong? require(stats) layout(matrix(c(1,2,3), 3, 1, byrow = TRUE)) #SETUP n <- 256 #nextn(1001) gives next power 2 F <- 100 #Hz -50 to 50 Hz dt

Jean-Marc Valin (jean-marc.valin@hermes.usherb.ca) wrote: > > > I've been using Speex in my voice-over-IP program on Win32, in > > wideband (16kHz) mode. I just starting using VBR recently and > > have run into something that might be a problem within Speex: > > Are you turning on DTX in addition to VBR? Also, what version are you > using. As of 1.0, DTX is