similar to: Cross Spectrum Analysis

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

Hello everybody I'm trying to write a simple version of matlab's "quiver". The idea is that I have fluid with velocity defined on a grid. I have a matrix of x-components of velocity and a matrix of y-components and I want to see the overall flow pattern. (I work with 2D fluid mechanics problems). My first-stab function is below: quiver <- function(u,v,scale=1) # first

Dear all, This is potentially a bug in spec.pgram, when the number of samples is odd,spec.pgramreturns a different result withfast = TRUE, the example below contains the two varieties with a reference spectrum calculated manually. the number of returned spectra is also larger (50 compared to 49) whenfast = TRUE x <- rnorm( 99 ) plot(spec.pgram(x, taper = 0 , detrend = FALSE , plot =

Dear R users I have two questions about estimating the spectral power of a time series: 1) I came across a funny thing with the following code: data(co2) par(mfrow=c(2,1)) co2.sp1<-spectrum(co2,detrend=T,demean=T,span=3) co2.sp2<-spectrum(co2[1:468],detrend=T,demean=T,span=3) The first plot displays the frequencies ranging from 0 to 6 whearas the second plot displays the same curve but

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)

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 $

Hello, I'm still a newbie user and struggling to automate some analyses from SigmaPlot using R. R is a great help for me so far! But the following problem makes me go nuts. I have two spectra, both have to be fitted to reference data. Problem: the both spectra are connected in some way: the stoichiometry of coefficients "cytf.v"/"cytb.v" is 1/2. {{In the SigmaPlot

Hi, I'm trying to match peaks between chromatographic runs. I'm able to match peaks when they are chromatographed with the same method, but not when there are different methods are used and spectra comes in to play. While searching I found the ALS package which should be usefull for my application, but I couldn't figure it out. I made some dummy chroms with R, which mimic my actual

hello all, for my diploma-thesis i want to statitically analyze near-infrared-spectra. a spectrum is given by the y-values of 1038 equi-distant x-points. in nature, a spectrum is a continuous curve. for analysis, every x-point is seen as a statistical variable. now my problem: first, i read a csv-table in a data.frame called sTable via read.table. besides some meta-data there are 1038 variables

I would really appreciate it if someone can give suggestions on how to do spectra fitting in R using ordinary least square fitting and non-negativity constraints. The lm() function works well for ordinary least square fitting, but how to specify non-negativity constraints? It wouldn't make sense if the fitting coefficients coming out as negative in absorption spectra deconvolution. Thanks.

Dear all, I work with (vibrational) spectra: some kind of intensity (I) over frequency (nu), wavelength or the like. I want to do fourier transform for interpolation, smoothing, etc. My problem is that the spectra are often irregularly spaced in nu: the difference between 2 neighbouring nu varies across the spectrum, and data points may be missing. Searching for discrete fourier transform

Time Series functions in R ========================== I think a good basic S-like functionality for library(ts) in base R would include ts class, tsp, is.ts, as.ts plot methods start end window frequency cycle deltat lag diff aggregate filter spectrum, spec.pgram, spec.taper, cumulative periodogram, spec.ar? ar -- at least univariate by Yule-Walker arima -- sim, filter, mle, diag, forecast

Hi Folks... No code to troubleshoot here. I need some suggestions about the right terminology to use in further searching, and any suggestions about R pkgs that might be appropriate. I am in the planning stages of a project in which IR, NMR and other spectra (I'm a chemist) would be collected on various samples, and individual samples would be followed over time. The spectra will be feature

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

Hi, I am currently analysis Raman spectroscopic data with the hyperSpec package. I consulted the documentation on this package and I found an example work-flow dedicated to Raman spectroscopy (see the address : http://hyperspec.r-forge.r-project.org/chondro.pdf) I am currently trying to remove outliers thanks to PCA just as they did in the documentation, but I get a message error I can't

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

I am having a hard time interpreting the phase and coherency plots. x is two timeseries that occur at the same time i.e. a b 1 11.2 12.3 16 11.3 12.4 31 11.4 12.5 46 11.5 12.6 ...etc even though my example is does not show this they are oscillating at more or less the same frequency just shifted by t=x (imagine two sine waves offset with the 2nd sine

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

Hi, I am far from experienced in both R and time series hence the question. The code for spec.pgram() seems to involve a circularity of the kernel (see below) yielding new power estimates to all frequencies computed by FFT. " if (!is.null(kernel)) { for (i in 1:ncol(x)) for (j in 1:ncol(x)) pgram[, i, j] <- kernapply(pgram[, i, j], kernel, circular = TRUE)

Dear R users I have a really simple question (hoping for a really simple answer :-): Having estimated the spectral density of a time series "x" (heart rate data) with: x.pgram <- spectrum(x,method="pgram") I would like to compute the power in a specific energy band. Assuming that frequency(x)=4 (Hz), and that I am interested in the band between f1 and f2, is the