similar to: setting search position

Hi A toy example is probably the least ambiguous way of explaining what I'm trying to do. > library(ts) > zz<-list(a=rnorm(100),b=rt(100,3)) > zz.spec<-lapply(zz,spectrum,plot=F) > summary(zz.spec) Length Class Mode a 15 spec list b 15 spec list I'm looking for an elegant way to fetch components of the sub-lists a and b of zz.spec; e.g. to make a matrix

There is now a preliminary version of a time series package in the R-devel snapshots, and we would welcome feedback on it. It is based in part on the packages bats (Martyn Plummer) and tseries (Adrian Trapletti) and in part on code I had or have written. (Thanks for the contributions, Martyn and Adrian!) Some of the existing ts code has been changed, for example to plot multiple time series, so

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

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 R-users, I have a question relating to the HoltWinters() function. I am trying to forecast a series using the Holt Winters methodology but I am getting some unusual results. I had previously been using R for Windows version 2.7.2 and have just started using R 2.9.1. While using version 2.7.2 I was getting reasonable results however upon changing versions I found I started to see unusual

Hi everybody, I'm a beginner R user and I'm trying to use the package "sapa" to estimate the spectral density function of several time series using the SDF function. For each time series, I want to calculate the density function at two temporal resolutions (daily and monthly). The monthly values calculated as a mean of the daily values. I first create a ts object for both series

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 $

Dear Signal Processing Expert, I would like to generate a random stationary signal of gaussian probability density function to simulate narrow band noise at the output of an IF amplifier. I know the receiver's system temperature (Ts) and IF bandwidth (B) therefore I assume that my narrow band noise mean power equals KTsB watts and therefore the power spectral density No=KTs per Hz. Do you

Hi again R People: I have the following time series: >x.ts Jan Feb Mar Apr May Jun Jul Aug Sep 2000 0.95 0.66 0.83 0.66 -1.45 -1.25 0.33 1.03 -0.48 2001 2.55 1.21 -1.10 -0.63 0.01 -2.20 -0.51 1.12 1.11 2002 -1.37 0.55 -0.63 -0.56 0.92 -1.73 0.59 0.77 0.30 2003 0.55 -0.01 -0.54 2.27 -1.29 -0.23 0.09 -0.50 -0.61 Oct Nov Dec 2000 0.84 -1.35

I used the AR modelling written for R (S) on blood pressure and heart rate signals. I used 60 one second samples and a model order of 20. I used the "ar" finction in the "ts" package. Given that blood pressure is measured in mmHg would the spectral density (on the graph displayed be [mmHg]sq/Hz ? And the heart rate is measured in Beats Per Minute (bpm) - so would the

Dear helpers I'm having a problem with function HoltWinters from package ts. I have a time series that I want to fit an Holt-Winters model and make predictions for the next values. I've already built an object of class ts to serve as input to HoltWinters. But then I get an error; I've used HoltWinters a lot of times and this never hapened > data.HW<-HoltWinters(data.ts) Error

I've just installed a new DEC Unix 4.0d machine and downloaded the binary version of samba 1.9.18p8 for DEC Unix. I am able to get it up and running, but I am having problems with passwords. I am being prompted for a password when I try to connect to a non-public share, and when I type in my password it is being rejected. I did set security=share in my smb.conf file. I also tried the

Dear list, i would like to build an own package using: R CMD INSTALL --html --build --auto-zip -l folder spectral.methods it seems to succeed as I get the following messages: * installing *source* package 'spectral.methods' ... ** R ** data Warning: empty 'data' directory ** preparing package for lazy loading ** help *** installing help indices converting help for package

Dear R Helpers, Currently I'm working with the ts package of R and created a TimeSerie from pixels extracted from satellite imagery(S10 NDVI data, 10 daily composites). I'm trying to decompose this signal in different signals (seasonal and trend). When testing out the STL method is says => Only univariate timeseries are allowed, but the current Timeserie I'm using is univariate!

Background: OS: Linux Mandrake 9.1 release: R 1.9.0 editor: Xemacs 21.4 frontend: ESS 5.1.23 --------------------------------- Colleagues I have two time series (upwelling index and water temperature) of evenly spaced, daily data over 18 months, but the upwelling index series has a gap of about 2 months right in the middle of it. I want to do the acf, pacf, ccf, and a cross-spectral analysis

Dear R Community, I am currently student at the Vienna University of Technology writing my Diploma thesis on causality in time series and doing some analyses of time series in R. I have the following questions: (1) Is there a function in R to estimate the PARTIAL spectral coherence of a multivariate time series? If yes, how does this work? Is there an test in R if the partial spectral

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 found this sentence in the Ogg format specs: http://www.xiph.org/ogg/vorbis/doc/vorbis-spec-res.html "A residue vector may represent spectral lines, spectral magnitude, spectral phase or hybrids as mixed by channel coupling." But where does the spectral phase information come from ? AFAIK MDCT doesn't provide any phase information. And in OGG-encoding, MDCT is taking place a few

Hi, Are there any functions in R that could be used to estimate the phase-shift between two semi-sinusoidal vectors? Here is what I have tried so far, using the spectrum() function -- possibly incorrectly: # generate some fake data, normalized to unit circle x <- jitter(seq(-2*pi, 2*pi, by=0.1), amount=pi/8) # functions defining two out-of-phase phenomena f1 <- function(x)

Hi, I'm Pina and I'm a student in geology. I'm working with spectral profile of sand and I have to find the similarity between one spectral profile selected by hyperspectral image anche one that I created to mix different percentage of 4 mineral component. I have to find the best mix of percentage of this 4 mineral in order to have the best likeness with the spectral profile chose by