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



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On Tue, May 11, 2010 at 10:17 AM, Ralf B <ralf.bierig@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
>
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>

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