similar to: fft with NA values

Hi R-users: I want to know if there is an easy way to obtain a Fourier Transform form a vector or an array (just like fft does), but with a more density base. I mean, if I have a vector of 512 of length, I want the Fourier Transform to be 1024, or 2048, etc, in length (de u domain). Or should I modify the fft C code to do that? If I want to modify the precision of the fft function, which

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

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

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-help members, To whom it may concern, our research group is conducting a study to evaluate the predictive value of 24 hour blood pressure variability. We are looking for an R routine that performs a fast Fourier transform spectral analysis (with an output of the approximation function of the Fourier and estimates the validity of the model for the various harmonics). Thanks Vittorio

Hi! I am using the fft() function the base package to transform some 1d signal. If I use this standar fucntion I get a very huge first fourier coeficient. I think this dues to the handling of the borders of the signal. Usually in fft especially in image processing the signal is simulated to be continuous by adding the signal several times periodically. My question is, is there some function

Dear all, Before I can get into serious Fourier analysis of Radon time-series I am practising with 24hour and 24.8hour sinusoids to assist with my interpretation of signals ittributed to tidal input to Radon time series. I am stuck. I have tried researching this to no avail. I am awating a book that should describe fourier transforms in detail and another one that should discuss the R

(This is almost out of topic but anyway...) It is surprising how little research effort have been put into psy-acou models for CELP. The basic problem lies in that it is not easy to alter the LP model without distroying the minimum-phase property (ie. the stability of the predictor). That leaves us with psy-acou modelling of the noise-part only. However, my own research is in constrained

The characteristic function is the inverse Fourier transform of the distribution function. The characteristic function of a normaly distributed random variable is exp(-t^2/2). x=seq(-2,2,length=100) fft(pnorm(x),inverse=T)/length(x) exp(-x^2/2) Why aren't the inverse fft and the mentioned function the same? Thanks for help, Thomas

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

Hi, A very simple question. I know that the Fourier transform of a Laplace distribution, with zero mean and variance 2b^2, is equal to 1/(1+(tb)^2). Therefore, the Fourier transform is a positive function (actually is always between 0 and 1). BUT, if I use the fft(alpha) function of R, where alpha is a vector containing 128 values of the probability density function from -2 to 2, I get negative

Hi everyone, I have got some questions about Speex, I am sorry if my questions are too newbie: 1. For the LP analysis, did Speex use the AR (Autoregressive) model or the ARMA model? 2. Am I right to say that Speex use a multistage VQ (since I believe Speex employs two or more VQ consecutively - based on the manual it says that Speex uses dynamically selectable codebooks (linear

Hello, I have calculated the fourier transform of the series enclosed at the end of this message, by doing: library(stats) x <- readLines("file1.txt") x.num <- as.numeric(x) ft.x.num <- fft(x.num) My question is: why is the first value (Real) of ft.x.num that big? (954.833870) all the other values are much smaller. Am I doing something wrong? Could you please help me to

I have a TDM card in a asterisk machine. I found that once I used it to call out, the call status changed to "connected" even the callee is still ring. How could asterisk distinguish the "ringing" and "connected" in zap channel? thanks.

Greetings, I'm working on image classification and for that I want to use the spectral slope as a feature for my classifier. For this I would prefer to calculate this feature using R, so far I've read my image and converted it's RGB representation into HSL. The spectral slope is computed over the Luminance component, so at the moment what I have is a NxN matrix of Luminance values.

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 !

I started out with a real vector b and then obtained its Fourier transform thus B<-fft(b) When I did F<-fft(B, inv=TRUE) I expected that F would be the inverse FT of B but it still has imaginary components. Should the inverse FT not be purely real? Am I missing something? Thanks, Peter.

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]]

Hi, I'm in dire need of a fast fourier transformation for me stupid biologist, i.e. I have a heartbeat signal and would like to decompose it into pure sin waves, getting three vectors, one containing the frequencies of the sin waves, one the magnitudes and one the phases (that's what I get from my data acquisition software's FFT function). I'd be very much obliged, if someone

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