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, I am looking for: A software to select the lag length for a lag window spectral estimator. Also, I have a small query in the reprex given below. Background for the above, from the book by Percival and Walden: 1. We are given X_1,...,X_n which is one realization of a stochastic process. 2. We may compute the periodogram using FFT, for example by the function spectrum in R. 3. The

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)