similar to: fft help

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

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

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 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

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

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

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, 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

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

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)

Hi all, If I use a moving average, it will smooth the choppy time series, but it will lead to lagging... How do I smooth timeseries without the lagging effect? Thanks!

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

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>

Greetings, I have several sets of oscillation data and would like to estimate the parameters of a sine function to each set (and hopefully automate this). A colleague provided an excel sheet that uses solver to minimize the RSS after fitting the sine function to each data set, but this cumbersome and difficult to automate. Is there a method in R for fitting a given sine function to a

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

Hello all! I've posted the question before but I am still struggling for an answer, please help if you can;-) Suppose a discrete series of data is generated by the following equation: CF=exp(-(t^2)/2) which is the characteristic function of a random variable X with standard normal distribution, how do I *numerically* solve for its probability density function? i.e., obtain a series of data

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 R-helpers ? I have 7 data points that I want to fit a continuous curve to, that should look similar to a sine wave My data points would mark the local minima and maxima respectively. This is what I?ve got so far. And I would keep doing so, but sadly nls() then says that it has reached the maximum number of Iterations? ?