similar to: LME Correlation Component using spectrum()?

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

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

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

Hi all, I am sorry for bothering the list, but I hope one of its members can help me. Currently I am doing a spectral analysis with the help of R. The spectral density I have calculated as follows: (The vector q contain some testing numbers.) > q <- c(28,28,26,19,16,24,26,24,24,29,29,27,31,26,38,23,13,14,28,19,19,17,22,2,4,5,7,8,14,14,23,23) > N <- length(q) > Fourier <-

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 List-mates, I'm trying to read a tk window menu from a file using {svWidgets} and 'menus.txt' but am receiving Warnings without seeing the desired consequences of the call. library(svWidgets) tkWinAdd("KSesnMain",title="kLab Session Manager for R", pos="+0+0") MenuRead(file="menus.txt") Warning messages: 1: Unrecognized menu type for

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

well if you want to find the spectral density aka what frequencies explain most of the variance then I would suggest the spectral density. This can be implemented with spec.pgram(). This is conducted with the fast fourier transform algorithm. a<-ts(data, frequency = 1) #make the time series with 365readings/365days ?spec.pgram and you should be able to take it from here This will

Dear List, This is my first post, and I'm a relatively new R user trying to work out a mixed effects model using lme() with random effects, and a correlation structure, and have looked over the archives, & R help on lme, corClasses, & etc extensively for clues. My programming experience is minimal (1 semester of C). My mentor, who has much more programming experience, but a comparable

Greetings, I'm working on image classification and for that I want to use the spectral slope as a feature for my classifier. For this I would prefer to calculate this feature using R, so far I've read my image and converted it's RGB representation into HSL. The spectral slope is computed over the Luminance component, so at the moment what I have is a NxN matrix of Luminance values.

Dear list mates, Are {utils} dialog box functions, and winMenuAdd... functions used to change (e.g. Console) menus, platform dependent? I'm writing a script loaded with .First that provides first time users in a lab course with the ability to select, load, and change between what I called a 'session' (more specific save that focuses on the session object I defined rather than the

Dear R-Help, I wish to simulate a process so that it has certain properties in the frequency domain. What I attempted was to generate a random time-series signal, use spec-pgram(), apply a function in the frequency domain, and then inverse transform back to the time-domain. This idea does not seem as straight forward in practice as I anticipated. e.g. x<-ts(rnorm(1000, 0,1), frequency=256)

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

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

Hello. Does anyone know how to generate a power spectrum from the STFT function in package e1071? The output for this function supplies the Fourier coefficients, but I do not know how to relate these back to the power spectrum. Alternatively, if anyone is aware of other packages/functions that perform short time Fourier transforms, that would also be helpful. Thanks. -- View this message in

This posting has nothing to do with R (except maybe that I am using R very heavily in writing the paper to which the question pertains.) I simply wish to draw upon the impressive knowledge and wisdom of the R community. Since this question is way off topic, if anybody has the urge to reply, they should probably email me directly: rolf at math.unb.ca rather than via this list. My question

Dear List-mates, I have been trying to set up a 4x8 trellis plot (that is, 4 columns, 8 rows), and would like to have the axis limits set based on the data range of rows (for ylim) and columns (for xlim). I've been using the call: foo<-xyplot(y~x|Epoch+Subject, type=c("l","r"), par.strip.text=list(cex=0.5), ...) and updating to see different effects of scale

Hi everyone, I have got some questions about Speex, I am sorry if my questions are too newbie: 1. For the LP analysis, did Speex use the AR (Autoregressive) model or the ARMA model? 2. Am I right to say that Speex use a multistage VQ (since I believe Speex employs two or more VQ consecutively - based on the manual it says that Speex uses dynamically selectable codebooks (linear

Dear Rhelpers, Is there a faster way than below to set a vector based on values from another vector? I'd like to call a pre-existing function for this, but one which can also handle an arbitrarily large number of categories. Any ideas? Cat=c('a','a','a','b','b','b','a','a','b') # Categorical variable

Dear All, I have just updated R to the version 2.6.2 on F8 (with the official F8 rpm). However, when running as root the following command: update.packages(checkBuilt=T) I get a bunch of errors like the ones below. Any ideas? Thanks in advance, Paul ----------------------------------------------- * Installing *source* package 'nlme' ... ** libs gcc -m32 -std=gnu99 -I/usr/include/R