R help - Dec 2013 - data distribution for lme

Hi folks,

I am using the lme package of R, and am wondering if it is assumed that
the dependent factor (what we fit for; y in many relevant texts) has to
have a normal Gaussian distribution? Is there any margins where some
skewness in the data is accepted and how within R itself one could check
distribution of the data?

Thanks,
Peyman

This is not really an R question -- it is statistics.
In any case, you should do better posting this on the
R-Sig-Mixed-Models list, which concerns itself with matters like this.

However, I'll hazard a guess at an answer: maybe.  (Vague questions
elicit vague answers).

Cheers,
Bert

On Tue, Dec 10, 2013 at 6:55 AM, peyman <zirak.p at gmail.com>
wrote:
> Hi folks,
>
> I am using the lme package of R, and am wondering if it is assumed that
> the dependent factor (what we fit for; y in many relevant texts) has to
> have a normal Gaussian distribution? Is there any margins where some
> skewness in the data is accepted and how within R itself one could check
> distribution of the data?
>
> Thanks,
> Peyman
>
> ______________________________________________
> R-help at 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.


-- 

Bert Gunter
Genentech Nonclinical Biostatistics

(650) 467-7374

No - it is assumed to be conditionally normal, that is, normal conditional
on the model.  So you should be looking at the distributions of the
residuals rather than of the response variable, as an indicator for whether
or not the model assumptions are satisfied.   Skewness in the residuals may
or may not affect the outcome; it depends on what the purpose of the model
is.  Skewness in residuals may arise from a number of different problems
with the model.

I hope that this helps,

Andrew




On Wed, Dec 11, 2013 at 1:55 AM, peyman <zirak.p@gmail.com> wrote:
> Hi folks,
>
> I am using the lme package of R, and am wondering if it is assumed that
> the dependent factor (what we fit for; y in many relevant texts) has to
> have a normal Gaussian distribution? Is there any margins where some
> skewness in the data is accepted and how within R itself one could check
> distribution of the data?
>
> Thanks,
> Peyman
>
> ______________________________________________
> 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.
>


-- 
Andrew Robinson
Deputy Director, CEBRA
Senior Lecturer in Applied Statistics                      Tel:
+61-3-8344-6410
Department of Mathematics and Statistics            Fax: +61-3-8344 4599
University of Melbourne, VIC 3010 Australia
Email: a.robinson@ms.unimelb.edu.au    Website: http://www.ms.unimelb.edu.au

FAwR: http://www.ms.unimelb.edu.au/~andrewpr/FAwR/
SPuR: http://www.ms.unimelb.edu.au/spuRs/

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