similar to: Calculate a function repeatedly over sections of a ts object

Hi all, I have a nx1 logical array of zeros and ones and I want to calculate the individual lengths of all 1-consecutive sequences contained in it. Is there an easy quick way to do this in R? So, if I have a vector such as 111001101000011111110 I would like to get (1) 3, (2) 2, (3) 1, (4) 7 Any help would be appreciated! thanks! Jorge [[alternative HTML version deleted]]

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

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

Dear R-user and -programmer, has one R-package the ability to compute smoothed periodograms of time series using the Tukey-window and/or the Parzen-window? In the ts- and tseries-packages I have found only Daniell-smoothers. With many thanks in advance for any hint Albrecht Kauffmann -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

I thought this would be trivial, but I can't find a package or function that does this. I'm hoping someone can guide me to one. Imagine a simple case with two survival curves (e.g. treatment & control). I just want to calculate the difference in KM estimates at a specific time point (e.g. 1 year) plus the estimate's 95% CI. The former is straightforward, but the estimates not

Good afternoon! I need to evaluate the goodness-of-fit (aka calibration) for survival probability estimates from a Cox model. I tried to use 'calibrate' in the Design package but I'm not sure if it should/would produce what I need (ie a chi-sq type statistic with a table of expected vs observed probabilities). Any other functions I should be aware of? Also, has anybody come across

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

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

I am trying to load a .so file and get the following error message: > dyn.load("taper.so",local=F) Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library "/home/hpc1367/runs/taper/taper.so": ld.so.1: /usr/local/lib/R/bin/R.bin: fatal: relocation error: file /home/hpc1367/runs/taper/taper.so: symbol f90_init: referenced symbol not

I am trying to create a frequency distribution and I am a bit confused. Here are the commands I have entered: > data <- read.csv(file="40609_sortedfinal.csv",head=TRUE,sep=",") > NumberOfActionsByStatus = data$STATUS > NumberOfActionsByUser = data$ETS_LOGIN > NumberOfBidOffer = data$BID_OFFER > NumberOfActionsByUser.freq = table(NumberOfActionsByUser) >

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[,

Following is my query: 1. Removing Inf from one column of dataframe. 2. out of 10 available dates, count how many times a security is present. (repeat for each security) 3. Out of dates, the security is present, I want to read latest status of market cap. 4. Change in market cap represent by 0/1 so even if market cap status changed once, take value 1. 5. create output file containing some already

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

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

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 suspect Prof. Ripley's response suffices. However, there *is* a Parzen kernel for kernel smoothing: Parzen K(z) = { 4/3 - 8z^2 + 8|z|^3 if |z| <= 1/2 8(1 -|z|)^3/3 if 1/2 < |z| <= 1 0 otherwise If I'm not mistaken, this appeared in Parzen's original 1962 paper on kernel density estimation. I also seem to recall

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

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)

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