similar to: baseline fitters

Dear all, you can find below my solution for sliding a window. Please find below the code for the two alternatives and the benchmarks.     install.packages('caTools') require(caTools) do_sliding_for_a_window_duty_cycle <- function(DataToAnalyse,  windowSize) {   data<-DataToAnalyse   out <- numeric()   elements<- numeric()   if (length(data[,1]) >= windowSize){       for

Hi, A new version of "caMassClass" package was released today. The package contain pipeline for processing and classification of protein mass spectra data. The main change is off-spinning from the library collection of generic functions into a new package "caTools". This package, which might be useful to broader group, is much smaller than "caMassClass" and has fewer

Hi, A new version of "caMassClass" package was released today. The package contain pipeline for processing and classification of protein mass spectra data. The main change is off-spinning from the library collection of generic functions into a new package "caTools". This package, which might be useful to broader group, is much smaller than "caMassClass" and has fewer

R gurus, On Aug 20, 2002, I asked in R-help about calculating a running 5-day median on a large matrix. Thanks to Martin Maechler <maechler@stat.math.ethz.ch> and Ray Brownrigg <Ray.Brownrigg@mcs.vuw.ac.nz> for responding. I ended up writing C code (and an R interface) to do it, which is about 1000x faster than the naive method! (72s became .09s on a 223 x 520 matrix). I added a

Hi, I am hoping some one can help me. I am learning to use C and would like to learn how to call c code in R. I have look at Writing R Extensions and I tried to copy the example on page 38 void convolve(double *a, int *na, double *b, int *nb, double *ab) { int i, j, nab = *na + *nb - 1; for(i = 0; i < nab; i++) ab[i] = 0.0; for(i = 0; i < *na; i++) for(j = 0; j <

Hi All, I did a google search and could not find the answer. Thus I post this message. I found runmax only work for positive numbers. x = rep(-1,10) runmax(x,3) x = rep(0, 10) runmax(x,3) for 32-bit R, i got very small numbers: 2.121996e-314 for 64-bit R, i got NaN. Is it a bug in runmax? or should this be included in the documentation that runmax only for positive numbers? however,

Hello I have a 3.5 million elements numeric vector x. I'm trying to calculate the rolling std dev of the previous 144 elements. rsd144<-runsd(x, 144, center=0, endrule="NA") this crashes R (ie on the console disappears and the Rgui.exe process is not there anymore) with smaller vectors, the crash does not occur. regards, [[alternative HTML version deleted]]

loess() should be able to do robust 2D smoothing. There's no natural ordering in 2D, so defining running medians can be tricky. I seem to recall Prof. Koenker talked about some robust 2D smoothing method at useR! 2004, but can't remember if it's available in some packages. Andy From: Vladislav Petyuk > > Hi, > Are there any multidimenstional versions of runmed() and >

Hellow everyone, This code bellow will calculate average daily wind speed(measurements are taken every three hours).Any ideas how to take the Min and Max instead of average. library(Matrix) setwd("C:\\Users\\aalyaari\\Desktop\\img") listfile<-dir() long <- file("C:\\Users\\aalyaari\\Desktop\\New folder (5)\\inra.bin", "rb") A=readBin(long, integer(),

Hello, I'm new to R and would like to know how to create a vector of "rolling sums". (I have seen the Rmetrics package and the rollMean function and I would like to do the same thing except Sum instead of Mean.) I imagine someone has done this, I just can't find it anywhere. Example: x <- somevector #where x is 'n' entries long #what I would like to do is: x1

Dear Mr. Tuszynski, I would like to request what I believe would be a beneficial update / patch to the runmean() function in the caTools package. Consider the following R>> x = 1:100 R>> is.integer(x) [1] TRUE R>> library(caTools) R>> head(runmean(x, 5, alg="exact")) [1] 8.487983e-314 1.060998e-313 1.273197e-313 1.697597e-313 2.121996e-313 2.546395e-313

Hello Lets say as an example I have a dataframe with the following attributes: rownum(1:405), colnum(1:287), year(2000:2009), daily(rownum x colnum x year) and foragePotential (0:1, by 0.01). The data is actually stored in a netcdf file and I'm trying to provide a conceptual version of the data. Ok. I need to calculate a moving mean and a moving variance for each cell on the following

Hi, I am working with a friend on a master's project. Our laboratory does a lot of statistical analysis using the R stats package and we also have a lot of under-utilised nvidia cards sitting in the back of our networked linux machines. Our idea is to coerce the linux nvidia driver to run some of our statistical analysis for us. Our first thought was to specifically code up a version of

Hello, I need to calculate a moving average and an exponentially weighted moving average over a fairly large data set (500K rows). Doing this in a for loop works nicely, but is slow. ewma <- data$col[1] N <- dim(data)[1] for(i in 2:N){ data$ewma <- alpha * data$ewma[i-1] + (1-alpha) * data$value[i] } Since the moving average "accumulates" as we move through the data,

Hi, I'm very beginner for R but I think it is a time to start as it is very useful. I have a coverage read file (illusmp454merCbed) for whole genome ~ 450 Mbp. This is head of this file. Scaffold sca_position coverage Scaffold1 1 0 Scaffold1 2 0 Scaffold1 3 0 Scaffold1 4 0 Scaffold1 5 0 Scaffold1 6 0 Scaffold1 7 1 Scaffold1 8 3 Scaffold1 9 3 I would like to plot everage coverage for every 1

Dear all, I am having a vector of around 300.000 elements and I Want to slide fast a window from the first element until the last-Windowsize what I have so far is the following for statement:  for (i in 1:(length(data[,1]) - windowSize)) {         out[i] <- mean(data[i:(i + windowSize - 1), ])         elements[i]<-length(i:(i + windowSize - 1))       } but this of course takes ages to

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

Dear R-Users, How can I compute simple moving averages from a time series in R? Note that I do not want to estimate a MA model, just compute the MA's given a lenght (as excel does). Thanks ________________________________________ Ricardo Gonçalves Silva, M. Sc. Apoio aos Processos de Modelagem Matemática Econometria & Inadimplência Serasa S.A. (11) - 6847-8889 ricardosilva@serasa.com.br

Hello everyone, I am using the na.omit() function on a 785 x 79 matrix. When I restrict the matrix to 10 columns for example it returns me the right result with the na rows deleted: 756 x 10 matrix. But, if I enter the whole matrix it returns me a 64 x 79 matrix, ununderstandable, I have no idea why...Does anyone know what it could be? thanks -- View this message in context:

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