similar to: autoregressive spectral density estimate by andrews' plug-in method?

Hi all, I am sorry for bothering the list, but I hope one of its members can help me. Currently I am doing a spectral analysis with the help of R. The spectral density I have calculated as follows: (The vector q contain some testing numbers.) > q <- c(28,28,26,19,16,24,26,24,24,29,29,27,31,26,38,23,13,14,28,19,19,17,22,2,4,5,7,8,14,14,23,23) > N <- length(q) > Fourier <-

Dear Friends, I am looking for an R version of the structural break test in Andrews (2003). The excellent strucchange package does not include this test (yet?). Is this test available in another package? If not, might there already be a function written to do this test? Thank you very much. Citation: Andrews, D.W.K. (2003), End-of-Sample Instability Tests. Econometrica, 71: 1661–1694.

Please, does anyone know of an R packge to estimate multidimensional spectral measure of coherence within a moving time window ? Some time ago I expeimented with a similar package that performs Cross Spectrum Analysis on the whole signal though. Unluckily I deal with non-stationary signals whose properties change along with time. Therefore estimates can only be made over time periods roughly

Does anyone know of a package that includes the Modified Sims test [Gewerke, 1983, Sims, 1972]? This test is used in econometrics and is a kind of alternative to the Granger test [Granger, 1969], which is in the package lmtest. Thanks in advance, chris Refernces: Gewerke, J., R. Meese, and W. Dent (1983), "Comparing Alternative Tests of Causality in Temporal Systems: Analytic Results and

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

Dear R users, I need to to the the following. Let a= 1 2 3 4 5 6 and b= -1 -2 -3 be (2x3) matrices. -4 -5 -6 I need to combine the two matrices into a new (2x6) matrix like this: ab = ( 1 -1 2 -2 3 -3 ) 4 -4 5 -5 6 -6 How can this be done in R? ----------------------------------------------------------------- ?????? ???

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)

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.

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 $

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

I am working with a dissolved oxygen dataset. continuous readings are taken at 15 minute intervals and we have been recording these data at 12 stations along the savannah river for two years now. The longest set of readings that are continuous without interuption is 53 days. I would like to look at the power spectral density at each of these sites (most likely one day will be the overridding

Hi everybody, I'm a beginner R user and I'm trying to use the package "sapa" to estimate the spectral density function of several time series using the SDF function. For each time series, I want to calculate the density function at two temporal resolutions (daily and monthly). The monthly values calculated as a mean of the daily values. I first create a ts object for both series

Hi list, Does anyone know if there is a library in R that does MTM-SVD method for spectral analysis? Thanks ----- Yasir H. Kaheil Columbia University -- View this message in context: http://www.nabble.com/Spectral-analysis-with-mtm-svd-Multi-Taper-Method-Combined-with-Singular-Value-Decomposition-tp21671934p21671934.html Sent from the R help mailing list archive at Nabble.com.

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 !

All of my resources for numerical analysis show that the spectral decomposition is A = CBC' Where C are the eigenvectors and B is a diagonal matrix of eigen values. Now, using the eigen function in R # Original matrix aa <- matrix(c(1,-1,-1,1), ncol=2) ss <- eigen(aa) # This results yields back the original matrix according to the formula above ss$vectors %*% diag(ss$values) %*%

Dear R users, I am using the vars package to calculate the impulse response functions and the forecast error variance decomposition of a VAR model. Unfortunately I do not know whether these functions assume unit or one standard deviation shocks. I tried to look into the code of these functions, but in vain: neither irf, nor vars::irf, nor vars:::irf output the code of the functions. Does someone

Dear all, Is there any spectral analisys functionality available for R version 3.4.3? Series() functionality doesn't work in this version. Regards Danniel [[alternative HTML version deleted]]