>From reviewing the first google page result for "Non-parametric regression
R", I hope this link will prove useful:

http://socserv.mcmaster.ca/jfox/Courses/Oxford-2005/R-nonparametric-regression.html



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On Fri, Jul 9, 2010 at 11:01 AM, Ralf B <ralf.bierig@gmail.com> wrote:

> I have two data sets, each a vector of 1000 numbers, each vector
> representing a distribution (i.e. 1000 numbers each of which
> representing a frequency at one point on a scale between 1 and 1000).
> For similfication, here an short version with only 5 points.
>
>
> a <- c(8,10,8,12,4)
> b <- c(7,11,8,10,5)
>
> Leaving the obvious discussion about causality aside fro a moment, I
> would like to see how well i can predict b from a using a regression.
> Since I do not know anything about the distribution type and already
> discovered non-normality I cannot use parametric regression or
> anything GLM for that matter.
>
> How should I proceed in using non-parametric regression to model
> vector a and see how well it predicts b? Perhaps you could extend the
> given lines into a short example script to give me an idea? Are there
> any other options?
>
> Best,
> Ralf
>
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> and provide commented, minimal, self-contained, reproducible code.
>

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