similar to: Computing fundamental harmonics from a periodogram

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, 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

When I run the "acf()" function using the "acf(ts.union(mdeaths, fdeaths))" example, the "acf()" function calls the "acf.plot()" function to generate this plot... http://members.cox.net/ddebarr/images/acf_example.png The plot in the lower right-hand corner is labeled "fdeaths & mdeaths", but the negative lags appear to belong to "mdeaths

Greetings all, I am using Fourier analysis to search for periodicities in IP network traffic by generating periodograms and then visually examining them for large, distinct peaks. However, in many cases it is not readily apparent where there are periodicities. I have no experience with discrete maths so I've come up against a block here: How do I define what the "noise floor"

When I encode a monophonic wav file, I would expect the resulting ogg file to be at about half the bit rate specified on the command line, as stated in the "oggenc -h" help text: "The 6 modes are approximately 112, 128, 160, 192, 256, and 350 kbps (for stereo 44.1kHz input. Halve these numbers for mono input).". This doesn't seem to be happening, though. I took a 16-bit,

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

Hi All: This is really getting into the weeds, but I am hoping someone will have a solution. I am trying to use dygrahs for R, within Shiny. The situation arises when I am combining a number of dygraphs into one plot. If I am just in an RNotebook, if you look at: https://stackoverflow.com/questions/30509866/for-loop-over-dygraph-does-not-work-in-r the solution to have the plot shown from a

Full_Name: Wolfgang Koller Version: 1.0.0 OS: linux Submission from: (NULL) (137.208.7.48) > data(UKLungDeaths) > ts.union(mdeaths, fdeaths,dframe=TRUE) Error in names<-.default(*tmp*, value = nmsers) : names attribute must be the same length as the vector > cbind(mdeaths, fdeaths,dframe=TRUE) Error in names<-.default(*tmp*, value = nmsers) : names attribute

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

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

I am trying to compute the periodicity of a time series. I would like to know which function in R does it. Also, how do I plot a Enright / Chi-square periodogram using R ? ( Enright, J.T., 1965, Journal of Theoret. Biol. 8,426-468) Greatly appreciate your help. Thanks in advance, Sukhaswami Malladi *************************************************************************** The contents

nitish wrote: > > I have a dataset that goes like: dataset = > t |x > 0 |x1 > 1 |x2 > 2 |0 > 3 |0 > 4 |0 > 5 |0 > 6 |x3 > 7 |0 > 8 |0 > 9 |0 > 10 |x4 > > and so on. I wish to detect the periodicity of occurrences. t is in > seconds and x are arbitrary, whose magnitude i am not interested in. I > just wish to get a best

Dear All, Is there a function in R which can do a periodogram of Schuster ? Thanks in advance ! Guillaume Blanchet

I just finished testing and integrating Speex into our test project. Speex works excellent and sounds great for the bitrate. I have noticed, however, on cheaper microphones that Speex will produce loud static noise at high harmonics/amplitude in the sound. Is there some setting to prevent this static? I don't have this problem with various other manufacturer voice coders that I am using

Dear Tom, Short answer, if your using spec.pgram(), use the smoothing kernel to get a better estimate at the frequency centered in the bandwidth. If your frequency bin of interest is wider than the bandwidth of the kernel, average across frequencies (I think). The estimate appears to be normalized already. If you are calculating your PSD independently, then oversample (e.g. 2, perhaps 4 or more

Can someone explain me this spec.pgram effect? Code: period.6<-c(0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10 ,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10) period.5<-c(0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0,10 ,0,0,0,0,10,0,0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0,10,0,0,0,0,10,0) par(mfrow=c(2,1))

Dear list, What on earth is spec.pgram() normalized too? If you would like to skip my proof as to why it's not normed too the mean squared or sum squared amplitude of the discrete function a[], feel free too skip the rest of the message. If it is, but you know why it's not exact in spec.pgram() when it should be, skip the rest of this message. The issue I refer herein refers only too a

### First, is there a way to access a univariate time series as a matrix instead of a vector? # For example: data(UKLungDeaths) # If I do apply(mdeaths,1,cumsum) # Gives an error as mdeaths is not a matrix but a vector, although when I look at it : mdeaths # the ts object has a matrix like "appearance" # The only way of doing it I've found is: mdeaths2<-as.matrix(mdeaths)

Reading "Modern Applied Statististics with S" and trying the corresponding examples both in the book and in ../lib/R/library/MASS/script, I'm now trying chapter 4 plotting bars with the following code on a linux box with R 1.8.1: ---------------------- library(MASS) library(lattice) options(echo=T, width=65, digits=5) lung.deaths <- aggregate(ts.union(mdeaths, fdeaths), 1)

Hello List, I am working on creating periodograms from IP network traffic logs using the Fast Fourier Transform. The FFT requires all the data points to be evenly-spaced in the time domain (constant delta-T), so I have a step where I zero-pad the data. Lately I've been wondering if there is a faster way to do this. Here's what I've got: * data1 is a data frame consisting of