[R-sig-ME] Beta-binomial distributions with lmer?
ONKELINX, Thierry
Thierry.ONKELINX at inbo.be
Wed Jun 10 20:43:31 CEST 2009
Dear Ben,
Indeed. I suggested to use a LMM with the transformed data. Then I would
have a look at how the residuals behave.
Best regards,
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be
www.inbo.be
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-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] Namens Ben Bolker
Verzonden: woensdag 10 juni 2009 20:14
Aan: Christine Griffiths
CC: r-sig-mixed-models at r-project.org
Onderwerp: Re: [R-sig-ME] Beta-binomial distributions with lmer?
Christine Griffiths wrote:
> Dear Ben and Thierry,
>
> Thank you for the advice. I tried to do both suggested methods,
> however got stumped on Ben's suggestion of logit. Thierry's suggestion
> did improve the variances (e.g. 7.7e-04 to 1.94 for the residual
> variance) when I used quasipoisson family errors. Given that the
> values aren't discrete I am not sure this is correct. Ben you only
> suggest this method if it leads to "stable variance". I have tried
> searching what is meant by this term, but have not found any
> information. If you could clarify or point me in the right direction I
would gratefully appreciate the assistance.
>
> Cheers
> Christine
If you transformed the data in some significant way, then the
residual variances aren't necessarily going to be comparable, so I'm not
sure I would take that as confirmation.
I think Thierry meant to suggest a LMM (i.e., assume normal
distributions, no transformation after the initial one) rather than a
GLMM (link function/exponential-family distribution or
quasi-distribution).
You may find more on "stabilizing variance" rather than "stable
variance" -- what I meant was that the variability in the Pearson
residuals (residuals scaled by the expected standard deviation, which is
what lmer gives you) should be independent of the fitted value
-- so try plot(sqrt(residuals(model)) ~ fitted(model)) and see if the
"amplitude" appears reasonably constant (this is approximately the same
as the "scale-location" plot that plot.lm gives you for a linear model).
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