search for: kernappli

I have a suggestion for kernapply for ts objects. When we choose the option circular=F, the returned series don't have the correct dates. The removed dates are all at the beginning instead of half at the beginning and half at the end. It is particularly useful when we need to smooth the series (or remove a trend using a filter) before estimating a model (like in macroeconomics) or simply

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

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)

Hi, First, let me thank Jaroslaw for making this survey. I find it quite illuminating. Now the questions: * the #1 solution below (based on cumsum) is numerically unstable. Specifically if you do the runmean on a positive vector you can easily get negative numbers due to rounding errors. Does anyone see a modification which is free of this deficiency? * is it possible to optimize the

Hi, Lately I run into a problem that my code R code is spending hours performing simple moving window statistical operations. As a result I did searched archives for alternative (faster) ways of performing: mean, max, median and mad operation over moving window (size 81) on a vector with about 30K points. And performed some timing for several ways that were suggested, and few ways I come up

I have a time series object that is made up of readings at 15 minutes for two years (this is why I am not posting data series). I have made this a time series with y = ts(x, frequency=96) ninety six is the number of 15minutes in a day. I want to smooth this series with k = kernel("modified.daniell", #) y.s = kernapply(y, k) if I want to smooth it for ~month windows do I use # =

Full_Name: Luzi P. Schucan Version: R-base-0.90.1-2.i386.rpm OS: Linux Submission from: (NULL) (141.84.136.129) I just wanted to install the rpm package with rpm --v -i [file], and here is the log: (I DID run it as root!!) (the important thing is in the end: there must be a bug in the post install script. The problem is, that it does install all the files, but it doesn't correctly hang the

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