[R-sig-ME] Too small a sample size for lmer?

Ben Bolker bolker at ufl.edu
Sat Jul 18 19:18:50 CEST 2009

  Why do Baayen et al 2008 recommend against MCMC?  Do you mean mcmcsamp
 (which may or may not be unreliable in this incarnation, I don't know)
or MCMC in general?  I tried to find it in the paper -- do you mean for
variance parameters (where the zero component gets in the way)?

  Your response variables are also interesting -- unless both plant
count and species richness are large numbers, they'll probably have
non-normal distributions, which adds to complication (it is possible,
but not really really easy, to deal with overdispersed [negative
binomial / log-normal-Poisson / quasi-Poisson ] count data in glmer, and
species richness often has quite an odd distribution depending on the
characteristics of the "regional species pool" ...)

  Ben Bolker

Martin Maechler wrote:
>>>>>> "CG" == Christine Griffiths <Christine.Griffiths at bristol.ac.uk>
>>>>>>     on Sat, 18 Jul 2009 13:58:36 +0100 writes:
>     CG> Dear R users,
>     CG> Many of you may be familiar with my design as I have posted a number of 
>     CG> queries before. Having consulted with someone in my department about 
>     CG> estimating bias corrected confidence intervals for small sample sizes 
>     CG> (rather than MCMC which Baayen et al. 2008 suggest should not be used), 
>     CG> they implied that I should not be using lmer for such a small sample size 
>     CG> as lmer was designed to deal with very large datasets. Is this still the 
>     CG> case? If so what is regarded as a small sample size?
> The fact that it was designed *to be able* to deal with big data
> sets does not mean that it was not appropriate for small data
> sets as well.
> It's just that mixed effect models with large data sets an
> crossed random effects really currently can *only* be
> analyzed with lmer {no other software available, not even if you
> pay much}.
> Said all that, I think your situation looks like a case where I
> would want to use (probably a parametric) bootstrap,
> and interestingly enough, at the UseR! 2009 meeting in Rennes,
> 10 days ago, there was a nice talk on this topic:
>    Jose A. Sanchez-Espigares, Jordi Ocaña 	 
>    An R implementation of bootstrap procedures for mixed models 
> You can find the abstract *and* slides on
>   http://www.agrocampus-ouest.fr/math/useR-2009/abstracts/user_author.html
> I don't think that their R code is already publicly available,
> but I've CC'ed one of the authors, and they may be willing to
> let you use their code before release.
> Martin Maechler, ETH Zurich
>     CG> Below is a description of my data. I have 5/6 enclosures (replicates) per 
>     CG> treatment - Aldabra/Radiata/control. Aldabra and radiata refer to two 
>     CG> different tortoise species, while control lacks tortoises. The enclosures 
>     CG> were assigned to a block: a block containing each of the 3 treatments, i.e. 
>     CG> 6 blocks in total. Each month for ten months I collected data: a repeated 
>     CG> crossed design. Unfortunately, I have non-orthogonal, unbalanced data (5/6 
>     CG> enclosures per treatment) as I cannot use a replicate within the aldabra 
>     CG> and radiata treatments. These are however from different blocks so I am 
>     CG> reluctant to axe them to achieve balanced data as this would leave me only 
>     CG> 4 blocks. I measured various attributes which I think that tortoises would 
>     CG> have an impact on, e.g. plant count, species richness. Because my data is 
>     CG> unbalanced and a repeated measures design I had chosen lmer to best model 
>     CG> this.
>     CG> For one other aspect, I calculate food web properties, for which I have no 
>     CG> replication, i.e. only one observation per treatment per month. Would lmer 
>     CG> be an acceptable way to analyse this data?
>     CG> If lmer is not advised for the analyses of these data, what other analyses 
>     CG> techniques should I investigate?
>     CG> Baayen et al. (2008)Mixed-effects modeling with crossed random effects
>     CG> for subjects and items. Journal of Memory and Language, 59, 390-412.
>     CG> Many thanks,
>     CG> Christine
> _______________________________________________
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> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

Ben Bolker
Associate professor, Biology Dep't, Univ. of Florida
bolker at ufl.edu / www.zoology.ufl.edu/bolker
GPG key: www.zoology.ufl.edu/bolker/benbolker-publickey.asc

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