similar to: fft help

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

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

Hi List, I'm trying to perform some spectral analysis on a time series, but I've got a few data points missing, so they have NA values. Unfortunately, the fft function doesn't seem to like this, and gives a completely empty result. Other than making up values to fill in the gaps, is there any way I can get around this? Thanks in advance, Steve. PS I haven't spotted anything

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

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

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

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

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

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.

Dear R helpers,   Following is a R script I am using to run the Fast Fourier Transform. The csv files has 10 columns with titles m1, m2, m3 .....m10.     When I use the following commands, I am getting the required results. The probelm is if there are 100 columns, it is not wise to define 100 commands as fk <- ONS$mk and so on. Thus, I need some guidance to write the loop for the STEP A and

Dear R Helpers, Is there any literaure available (including R code) on Fast Fourier Transform being used in CreditRisk+? I need to learn how to apply the Fast Fourier Transform. I agree I am too vaue in my question and sincerely apologize for the same, but I am not able to understand as to where do I start for this particular assignment. I tried to search google for CRAN and Fast Fourier

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

Hello, I try to synthesize harmonic functions with a subset of frequencies determined by fast fourier transform. I wrote two different functions, a looped version and a matrix multiplication version. As an example the looped version takes 5 sec and the matrix-version takes 3-4 sec (R 1.4, Athlon 1.2 GHz, 256 MB, Win ME), but the latter needs huge amount of memory due to the matrices. So, none of

Hi, here is a practical problem we would like to solve. In a pneumatic post the acceleration of the capsule is measured and plotted over time. From the graph achieved we would like to derive some kind of statistic value that describes the stress the capsule, or what is in it, is exhibited to. The amount of stress introduced to the capsule will probably depend on two things, the maximum

This message is in MIME format --_=XFMail.1.3.p0.Linux:990422185944:4782=_ Content-Type: text/plain; charset=us-ascii R-0.64.0 on RedHat Linux 5.1 Some problems with multivariate fast fourier transform. I have attached the dump of a 30 x 3 matrix that seems to reliably reproduce these problems 1) mvfft doesn't like vectors. It complains about not having enough memory: R>

I'm doing convolutions in the frequency domain and need to know the layout of the Fourier modes returned by fft. (This is leading up to a more involved question about moment generating functions, but I need to know if I've got this part correct first.) I think in 1D the pattern is: 0 1 2 3 -2 1 (even) 0 1 2 3 -3 2 1 (odd) In 2D is it simply (for a square matrix): 0 1 2 -1 (horizontal)

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

Greetings all, I'm working on a project trying to apply fourier analysis to timestamped router logs, using R to perform the analysis. The idea is to determine if any type of traffic (say, outgoing ICMP requests) has strong periodic features because it may indicate a compromise somewhere on the network. The FFT requires all data points to be evenly spaced, but the recorded events do not

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