similar to: Layout of Fourier frequencies

Hi everyone, first of all, I would like to say that I am a newbie in R, so I apologize in advance if my questions seem to be too easy for you. Well, I'm looking for periodicity in histograms. I have histograms of certain phenomenons and I'm asking whether a periodicity exists in these data. So, I make a periodogram with the function spec.pgram. For instance, if I have a histogram h, I

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

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

I see some code in R to attach finalizers to external pointer references (Register[C]Finalizer). Anyone have an example of how to code the finalizer? R_RegisterCFinalizer accepts a C function, but I can't see how to get it to operate on the pointer, since the pointer is not passed to the function when its called. RegisterFinalizer takes an R closure, but how is it called from R (tried

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 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 =

I am reading some documentation about Cross Spectrum Analysis as a technique to compare spectra. My understanding is that it estimates the correlation strength between quasi-periodic structures embedded in two signals. I believe it may be useful for my signals analysis. I was referred to the R functions that implement this type of analysis. I tried all the examples which generated a series of

I'm revisiting the R/S DBI package and was wondering if there is any plan to implement package name spaces in R/S. I've taken to the habit of prepending a few characters to function names in packages to avoid collisions, e.g., dbConnect(). An alternative would be DBI::connect() (s/::/preferred/) which in some ways I find more pleasing. Not having thought about it in detail, it seems pretty

I seem to remember something about an "internal" keyword for R doc files. How do we use it? Tim -- Timothy H. Keitt Department of Ecology and Evolution State University of New York at Stony Brook Stony Brook, New York 11794 USA Phone: 631-632-1101, FAX: 631-632-7626 http://life.bio.sunysb.edu/ee/keitt/ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Its not clear from the documentation whether one should do \keyword[key1, key2} or \keyword{key1} \keyword{key2} in .Rd files. I believe the latter is correct. (At least 'R CMD check' complains when given the first version.) It would help to make this explicit in the R extension writing docs. Tim -- Timothy H. Keitt Department of Ecology and Evolution State University of New

I'm curious if any of the core R developers have considered the possibility of hosting R (v2?) on the parrot common runtime environment. Perl6 will generate byte-code for parrot, as will some future version of python. I can imagine both drawbacks and advantages. Some advantages would be fast byte-code execution and freely mixing perl, python and R modules. Anyone looked into this? Tim

I have an application where I need to preserve object attributes across calls to 'lapply'. The current definition is: lapply <- function (X, FUN, ...) { FUN <- match.fun(FUN) if (!is.list(X)) X <- as.list(X) rval <- .Internal(lapply(X, FUN)) names(rval) <- names(X) return(rval) } Would it make sense to replace names(rval) <- names(X)

Can a C function wrapped with .Call return void? Or must it return a valid SEXP? Thnx. Tim -- Timothy H. Keitt Department of Ecology and Evolution State University of New York at Stony Brook Stony Brook, New York 11794 USA Phone: 631-632-1101, FAX: 631-632-7626 http://life.bio.sunysb.edu/ee/keitt/ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing

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

I've been putting together a package that defined generic methods for database access. The packages is called "Rdbi." It borrows as much as possible from existing database packages / proposals. I'd like to start a discussion about the proposed interface. Here's what I've come up with so far: # # Rdbi: connectionMethods.R # dbConnect <- function(dbObj, ...)

Dear R-help members, To whom it may concern, our research group is conducting a study to evaluate the predictive value of 24 hour blood pressure variability. We are looking for an R routine that performs a fast Fourier transform spectral analysis (with an output of the approximation function of the Fourier and estimates the validity of the model for the various harmonics). Thanks Vittorio

I did a fourier transform on a function in time domain to get the following functions in frequency domain (in latex): $Y_1[\omega] = \frac{1}{1-\phi_1 e^{-jw}}$ $Y_2[\omega] = \frac{1}{1-(\phi_1 + \phi_2)e^{-jw} +\phi_1\phi_2e^{-2jw}}$ How do I find the spectrum of this function for given $\phi_1$ and $\phi_2$ coefficients and in the discretization interval $w = [-\pi:.1*\pi: \pi]$? Then, how

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

Hi Guys, I had a monthly time series's data of land temperature from 1980 to 2008. After plotting a scatter diagram, it seems that annually, there is a semi sinusoidal cycle. How do I run Fourier's series to the data so that I can fit model on it? I am really sorry for my question sound stupid, but I just don't know where to start. I am desperately looking for help from you guys.

For the command 'spectrum' I read: The spectrum here is defined with scaling 1/frequency(x), following S-PLUS. This makes the spectral density a density over the range (-frequency(x)/2, +frequency(x)/2], whereas a more common scaling is 2? and range (-0.5, 0.5] (e.g., Bloomfield) or 1 and range (-?, ?]. Forgive my ignorance but I am having a hard time interpreting this. Does this mean