similar to: Getting started with Time Series

Is there a way to call Matlab and Gauss code in R? I see functions that work for C/FORTRAN, but not for other languages. Brian -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !)

Dear R-Users, I am doing a research on Time Series, especially on the estimation of the fractional exponent in long memory time series (for those who know). However there are three estimators already built-in the fracdiff package (GPH, Sperio, MLE) I was wondering if there is someone who had used an estimation introduced by P.M. Robinson (related paper: "Log-Periodogram regression of time

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

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

Dear Prof Ripley, First of all, unless you are an english professor, then I do not think you have any business policing language. I'm still very much a student, both in R, and regarding signal analysis. My competence on the subject as compared too your own level of expertise, or my spelling for that matter, may be a contension for you, but it would have been better had you kept that opinion

Dear R experts, beginners and everyone else, I'm calculating "cumulative periodogram" using the command "cpgram" [1] from the MASS library. Here is a short example with the "lh" (hormone level) dataset: library(MASS) plot(lh,type="l",ylab="value",xlab="time", main="Hormone Levels (lh)") spectrum(lh,

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 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

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.

Hello everyone, This is my first time posting to the list, thanks in advance. I am calculating the smoothed periodogram for the residuals of an AR model that I fit to EEG data. The autocorrelation plot of the residuals shows the series is now approximately white (i.e. ACF = 1 at lag 0, and close to 0 for all other lags). I would like to show that the spectrum of the series is also

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

I'm looking for someone to explain the difference between these procedures. The function prcomp() does principal components anaylsis, and the function cmdscale() does classical multi-dimensional scaling (also called principal coordinates analysis). My confusion stems from the fact that they give very similar results: my.d <- matrix(rnorm(50), ncol=5) rownames(my.d) <-

Hello, Please copy all replies directly to me (my account is having difficulty with receiving lists these days). This is primarily a programming question, but the specifics regard start values for a nonlinear regression (if you have suggestions on alternative ways to obtain start values they are welcome as well). I'm using nls to estimate a nonlinear time series equation of the form:

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

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 R users, I am having a problem with the main title of a plot. I have written a function returning a plot and I would like that the main title of this plot shows actually the name of the dataset I have used. So, for instance: if my dataset is called Chl, I would like that my plot to be entitled : "Raw periodogram of Chl". I know the function "paste". I suppose I would be

Hello, I have some data where a number of events (the total amount varies) occur at cumulating times, I would like to create a scatterplot (easily achieved using plot etc) of these events (the events can either be times using poxist or I can convert them into just seconds which is probably easier to work with), however I would like the events/times to re-begin plotting every 10th occurrence and

Dear all, I'm trying to finding the fundamental harmonics (ie. peaks in a periodogram) from a time series (extracted from an mp3). For example, if I look at spectrum(fdeaths, spans = c(3,3)) I'd say the fundamental harmonics are about 1, 2, 3.5 and 4.5 - but how can I extract these automatically? (preferably with some heuristic for choosing the smoothing spans too) I'm aware of

Hi, I have recently used the CTS package in order to use the Lomb-Scargle periodogram (spec.ls) function. I have noticed an issue that I hoped you may be able to explain. If a regularly spaced time series has two points removed, one at either side of a single data point (thus making an irregularly spaced time series), a spectrum with a very large peak at the highest frequencies is produced. An

Hi, Does anyone know the name of the package that includes a function for computing the lomb periodogram on irregular spaced ts data? I saw the package once ~ 1 month ago but cannot find it now ... , Rich