[R] Non-parametric regression

[Joris Meys]

Just to be correct : gam is mentioned on the page Tal linked to, but
is a semi-parametric approach using maximum likelihood. It stays valid
though.

Another thing : you detect non-normality. But can you use a Poisson
distribution for example? The framework of generalized linear models
and generalized additive models allows you to deal with non-normality
of your data.

In any case, I suggest you contact a statistician nearby for guidance.

Cheers
Joris

On Fri, Jul 9, 2010 at 10:26 AM, Tal Galili <tal.galili at gmail.com> wrote:
> >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 at 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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>



-- 
Joris Meys
Statistical consultant

Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control

tel : +32 9 264 59 87
Joris.Meys at Ugent.be
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