similar to: Power Spectral Sensity

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 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 All, I am looking for: A software to select the lag length for a lag window spectral estimator. Also, I have a small query in the reprex given below. Background for the above, from the book by Percival and Walden: 1. We are given X_1,...,X_n which is one realization of a stochastic process. 2. We may compute the periodogram using FFT, for example by the function spectrum in R. 3. 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

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 $

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

Dear R users Is there a function in R to make a peridogram for a spectral analysis of unevenlly sampled data?? something like spec.lomb() for S-Plus?? How to plot a vector with unequally spaced time series?? e.g day/month/year V1 03/08/82 0.34 28/08/82 1.42 12/09/82 0.28 20/09/82 0.56 03/10/82 0.85 21/10/82 1.45 thanks -- Marcelo Alexandre Bruno - Pos-graduacao Oceanografia Biologica

hello, I have been using the per function in package longmemo to obtain a simple raw periodogram. I am considering to switch to the function spec.pgram since I want to be able to do tapering. To compare both I used spec.pgram with the options as suggested in the documentation of per {longmemo} to make them correspond. Now I have found on a variety of examples that there is a shift between

well if you want to find the spectral density aka what frequencies explain most of the variance then I would suggest the spectral density. This can be implemented with spec.pgram(). This is conducted with the fast fourier transform algorithm. a<-ts(data, frequency = 1) #make the time series with 365readings/365days ?spec.pgram and you should be able to take it from here This will

Hello, I propose two small changes to spec.pgram to get modest speedup when dealing with input (x) having multiple columns. With plot = FALSE, I commonly see ~10-20% speedup, for a two column input matrix and the speedup increases for more columns with a maximum close to 45%. In the function as it currently exists, only the upper right triangle of pgram is necessary and pgram is not returned by

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

There is now a preliminary version of a time series package in the R-devel snapshots, and we would welcome feedback on it. It is based in part on the packages bats (Martyn Plummer) and tseries (Adrian Trapletti) and in part on code I had or have written. (Thanks for the contributions, Martyn and Adrian!) Some of the existing ts code has been changed, for example to plot multiple time series, so

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))

Dear R Community, I am currently student at the Vienna University of Technology writing my Diploma thesis on causality in time series and doing some analyses of time series in R. I have the following questions: (1) Is there a function in R to estimate the PARTIAL spectral coherence of a multivariate time series? If yes, how does this work? Is there an test in R if the partial spectral

Hi, I'm Pina and I'm a student in geology. I'm working with spectral profile of sand and I have to find the similarity between one spectral profile selected by hyperspectral image anche one that I created to mix different percentage of 4 mineral component. I have to find the best mix of percentage of this 4 mineral in order to have the best likeness with the spectral profile chose by

Dear Signal Processing Expert, I would like to generate a random stationary signal of gaussian probability density function to simulate narrow band noise at the output of an IF amplifier. I know the receiver's system temperature (Ts) and IF bandwidth (B) therefore I assume that my narrow band noise mean power equals KTsB watts and therefore the power spectral density No=KTs per Hz. Do you

I found this sentence in the Ogg format specs: http://www.xiph.org/ogg/vorbis/doc/vorbis-spec-res.html "A residue vector may represent spectral lines, spectral magnitude, spectral phase or hybrids as mixed by channel coupling." But where does the spectral phase information come from ? AFAIK MDCT doesn't provide any phase information. And in OGG-encoding, MDCT is taking place a few

Hello all, I would like to use the train function from the caret package to train a svm with a spectral kernel from the kernlab package. Sadly a svm with spectral kernel is not among the many methods in caret... using caret to train svmRadial: ------------------ library(caret) library(kernlab) data(iris) TrainData<- iris[,1:4] TrainClasses<- iris[,5] set.seed(2)

Hello! I would like to ask if there is in R a function that estimates the spectral density function of a stochastic series at frequency zero by the "plug-in method", advocated by Andrews in his paper "Heteroscedasticity and Autocorrelation Consistent Covariance Matrix Estimation", Econometrica, 59,817-858. I saw R has functions that employ Andrews' plug-in method using an

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.