similar to: unable to load shared library "/home/hpc1367/runs/taper/taper.so"

Full_Name: Michael Stein Version: Version 2.1.1 OS: linux Submission from: (NULL) (128.135.149.112) The documentation for spec.taper says p: The total proportion to be tapered, either a scalar or a vector of the length of the number of series. Details: The cosine-bell taper is applied to the first and last 'p[i]/2' observations of time series 'x[,

Hi list, Does anyone know if there is a library in R that does MTM-SVD method for spectral analysis? Thanks ----- Yasir H. Kaheil Columbia University -- View this message in context: http://www.nabble.com/Spectral-analysis-with-mtm-svd-Multi-Taper-Method-Combined-with-Singular-Value-Decomposition-tp21671934p21671934.html Sent from the R help mailing list archive at Nabble.com.

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 =

Great info. I found some descriptions of st5 (md5 fp) and ffp, where I assume that "fp" is just a Taper abbreviation for "fingerprint" - or signature as it's called in the flac header. However, I could not find these utilities or source code. What I found looked like instructions for a gui-based program. I think it would be easier to support Mac if st5 were

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

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

Dear all, What is the usage of the ".Call()"? What is the meaning of the follows: S=.Call("RS_fractal_spectral_density_function_direct",x,as.vector(taper.),as.logical(center),as.logical(recenter),TRUE,as.integer(npad),COPY=rep(FALSE,6),CLASSES=c(rep("matrix",2),rep("logical",3),"integer"),PACKAGE="ifultools") And is a function

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 propose two small changes to spec.pgram to get modest speedup when dealing with input (x) having multiple columns. With plot = FALSE, I commonly see ~10-20% speedup, for a two column input matrix and the speedup increases for more columns with a maximum close to 45%. In the function as it currently exists, only the upper right triangle of pgram is necessary and pgram is not returned by

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)

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

Linux RH 7.3, Samba 2.2.7a NT 4.0 SP6 - Backup Exec 8.5 The linux box is running the Backup Exec client for Unix ('http://seer.support.veritas.com/docs/192093.htm') Occasionally, when doing a backup the entire linux system freezes. I have to do a hard reboot without a shutdown, etc. There is 40 plus gigs on the backup, so I dont think a tar and copy would be bery efficient. I am

Hello, I'm new to R. In Sapa package there's an example for function SDF like data <- as.numeric(sunspots) methods <- c("direct","wosa","multitaper","lag window") S <- lapply(methods, function(x, data) SDF(data, method=x), data) x <- attr(S[[1]], "frequency")[-1] y <- lapply(S,function(x) decibel(as.vector(x)[-1])) which

Hello all, it seems my efforts in reading the manuals and help files aren't enough so here I am. The question is, how would I go about linking a pre-compiled DLL in to my package? I have previously successfully built packages with Fortran and C source code, but now I'd like to take this ready made DLL and call its routines from R. My collegue was brave enough to simply try and put

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

Hi, I want to apply a function (in my case SDF; package ?sapa?) repeatedly over discrete sections of a daily time series object by sliding a time window of constant length (e.g. 10 consecutive years or 1825 days) over the entire ts at increments of 1 time unit (e.g. 1 year or 365 days). So for example, the first SDF would be calculated for the daily values of my variable recorded between years 1

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 use Sony (previously Sonic Foundry) Sound Forge, which allows me to save audio files in .w64 (Wave 64) format to get around the 2GB .wav file limitation. W64 was invented by Sonic Foundry, and is an open format as far as I know. The only programs I know about using the .w64 format at the moment are Sound Forge and Steinberg Nuendo, although there may be others out there. With increasing

I'm doing convolutions in the frequency domain and need to know the layout of the Fourier modes returned by fft. (This is leading up to a more involved question about moment generating functions, but I need to know if I've got this part correct first.) I think in 1D the pattern is: 0 1 2 3 -2 1 (even) 0 1 2 3 -3 2 1 (odd) In 2D is it simply (for a square matrix): 0 1 2 -1 (horizontal)