Hi Ralf, I can't offer you many resources, but the few I came across are: 1) loess (or the older version: lowess) 2) smooth 3) rollapply (from the zoo pacakge)
I used a combination of 1 and 3 when creating an R implementaion for a (simplistic) quantile loess, you might find the code useful: http://www.r-statistics.com/2010/04/quantile-loess-combining-a-moving-quantile-window-with-loess-r-function/ Best, Tal ----------------Contact Details:------------------------------------------------------- Contact me: tal.gal...@gmail.com | 972-52-7275845 Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) | www.r-statistics.com (English) ---------------------------------------------------------------------------------------------- On Tue, May 11, 2010 at 10:17 AM, Ralf B <ralf.bie...@gmail.com> wrote: > R Friends, > > I have data from which I would like to learn a more general > (smoothened) trend by applying data smoothing methods. Data points > follow a positive stepwise function. > > > | x > x > | xxxxxxxx xxxxxxxx > | x x > |xxxx xxx xxxx > | xxxxxxxxxxxxxxxxx > | > | > xxxxxxx xxxx > |__________________________________________________________ > > > Data points from each step should not be interacting with any other > step. The outliers I want to to remove are spikes as shown in the > diagram. These spikes do not have more than one or two points. I > consider larger groups as relevant and want to keep them in. I > sometimes have less than 5 points for each step, and up to 50 at max. > Given these conditions would you suggest using one of the moving > averages (e.g. SMA, EMA, DEMA, ...) or the locally linear regression > (lowress) method. Are there any other options? Does anybody know a > good site that overviews all methods without going to much into > mathematical details but rather focusing on the requirements and > underlying assumptions of each method? Is there perhaps even a package > that runs and visualizes a comparison on the data similar to packages > like 'party' ? (with 1000s of active packages, one can always hope for > that) > > Thanks in advance! > Ralf > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
