similar to: Time series application question

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 List-Members, I would like to draw the amplitudes of different frequencies from a time series as shown in the attached figure. Does anybody has an idea how to do it? Best wishes Thomas -------------- next part -------------- A non-text attachment was scrubbed... Name: milankovich.pdf Type: application/pdf Size: 93605 bytes Desc: not available Url :

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

Fritz just put the first version of a new time series package to the contrib section at CRAN. The package is called "tseries.tgz" and provides a library for time series analysis. It contains acf Autocorrelation Function adf.test Augmented Dickey-Fuller Test amif Auto Mutual Information Function bds.test BDS Test

Fritz just put the first version of a new time series package to the contrib section at CRAN. The package is called "tseries.tgz" and provides a library for time series analysis. It contains acf Autocorrelation Function adf.test Augmented Dickey-Fuller Test amif Auto Mutual Information Function bds.test BDS Test

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

On Nov 12, 2009, at 16:32, Fernando Alberto Marengo Rodriguez wrote: > I' m studying FLAC performance, and I'd like to know how much > compression can be achieved for different audio files. > > 1) It seems that for nontonal sound (wideband noise), the > compression factor is better than for compound sound (tones + > nontonal components), which is typically 2. The

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

Hi, I've been doing a lot of CA modeling lately and am now wanting to make some checkerboard plots in R. Let's say I have a matrix: > is.matrix(junk) [1] TRUE > junk [,1] [,2] [,3] [,4] [,5] [1,] 0 0 1 0 0 [2,] 0 1 1 1 0 [3,] 0 1 0 0 1 [4,] 0 1 1 1 1 [5,] 0 1 0 0 0 > and I want to make a

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

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

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(

Hi, I have more questions about the fft. The application in Excel is very limited. In Excel I can adjust graphs and calibrate the x and y-axis. The input and process, however, is limited compared to R. With a Dataset table where one column is the hour difference and the second are the values with hidden frequencies, how is it possible to plot a frequency spectrum with the x-axis calibrated to

There's a new module in CVS called 'masktest'. I spent the last few weeks writing this program. It can measure masking based on input from people. We need it to obtain better masking curves than we have right now (from Ehmer...). It's not finished yet, but you can get a feeling of what it's supposed to do. It will measure the masking between tone and tone, noise /tone,

I did a fourier transform on a function in time domain to get the following functions in frequency domain (in latex): $Y_1[\omega] = \frac{1}{1-\phi_1 e^{-jw}}$ $Y_2[\omega] = \frac{1}{1-(\phi_1 + \phi_2)e^{-jw} +\phi_1\phi_2e^{-2jw}}$ How do I find the spectrum of this function for given $\phi_1$ and $\phi_2$ coefficients and in the discretization interval $w = [-\pi:.1*\pi: \pi]$? Then, how

Hi, everyone, I tried to use "spectrum()" or "spec.pgram()" to get a periodogram of a time series but they didn't work. Even the examples given in the help file didn't work (all with the same error message, below). And the 'ts'ibrary was loaded with "library(ts)" or "library("ts"). I also tried library(tseries) but got the same problem.

Dear useRs, I have a curve which is a mixture of Gaussian curves (for example UV emission or absorption spectrum). Do you have any suggestions how to implement searching for optimal set of Gaussian peaks to fit the curve? I know that it is very complex problem, but maybe it is a possibility to do it? First supposement is to use a nls() with very large functions, and compare AIC value, but it is

Dear list, I' m studying FLAC performance, and I'd like to know how much compression can be achieved for different audio files. 1) It seems that for nontonal sound (wideband noise), the compression factor is better than for compound sound (tones + nontonal components), which is typically 2. The reason for this result could be the following: the LPC filter is more suitable for

Hi, I have an nxp data set of time series. For my final year project, I would like to partition this data set into a smaller number of units ( < n). Where each of the units contains time series that move closely together (i.e. in unison), and the "simmilarity" (in terms of occurence & amplitudes of peaks and troughs) between the segregated units is "low". I know

Hi, I have a matrix not unlike this: foo <- matrix(,5,5) foo[5,1] <- 1 foo[1:3,2] <- 1 foo[3:4,3] <- 1 foo[4:5,4] <- 1 foo[2:4,5] <- 1 foo [,1] [,2] [,3] [,4] [,5] [1,] NA 1 NA NA NA [2,] NA 1 NA NA 1 [3,] NA 1 1 NA 1 [4,] NA NA 1 1 1 [5,] 1 NA NA 1 NA I want to get a vector that is the column numbers as sorted