similar to: Computing Spectral Slope

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

Hi I'm fairly new to R and am trying to analyse some large spectral datasets using stepwise regression (fairly standard in this area). I have a field sampled dataset, of which a proportion has been held back for validation. I gather than step() needs to be fed a regression model and lm() can produce a multiple regression. I had thought something like: spectra.lm <-

Dear list, I've recently become interested in comparing the spectral estimates using the different methods ("pgram" and "ar") in the spectrum() function in the stats package. With many thanks to the authors of these complicated functions, I would like to point out what looks to me like a bit of an inconsistency -- but I would not be surprised if there is good reasoning

Dear R users, a new release (v1.5) of Spectrino package is on CRAN. For those of you not familiar with Spectrino: Spectrino is a spectra preparation utility for R. It has specialized organization and visualization features for spectra. Spectrino has visual interface - as Windows application with rich set of features to set a structure and manipulate visually spectra and it is accessible from

Dear R users, a new release (v1.5) of Spectrino package is on CRAN. For those of you not familiar with Spectrino: Spectrino is a spectra preparation utility for R. It has specialized organization and visualization features for spectra. Spectrino has visual interface - as Windows application with rich set of features to set a structure and manipulate visually spectra and it is accessible from

I wonder whether any of you know of an efficient way to calculate the approximate degrees of freedom of a gamm() fit. Calculating the smoother/projection matrix S: y -> \hat y and then its trace by sum(eigen(S))$values is what I've been doing so far- but I was hoping there might be a more efficient way than doing the spectral decomposition of an NxN-matrix. The degrees of freedom

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

Hi, I wrote some simple rpanel package script for visual spectral data comparison. At this example i have a three samples and i want to zoom through x and y axis to compare differences between samples. With my script below I can zoom to some data region and add some other spetra to the plot, through text input field. But I can't figure out, how to change axis scaling for all displayed

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 collegues, I´ ve worked with near infrared (NIR) spectroscopy to assess chemical, physical, mechanical and anatomical properties of wood. I use "The Unscrambler" software to correlate the matrix of dependent variables (Y) with the matrix of spectral data (X) and I would like to migrate to R. The matrix of spectral variables is very large (2345 columns and n lines, where n =