Too small a sample size for lmer?
Sorry I meant mcmcsamp. Page 398, second column, after the table of coefficients estimated. yeah, I have had lots of problems with modeling this data and so was really wondering whether there was a better way to look at it, but maintaining the repeated design. Martin, thanks for the guidance. I have had a look at the link you suggested and will wait and see whether code is brought out to do this. Thank you Christine
--On 18 July 2009 13:18 -0400 Ben Bolker <bolker at ufl.edu> wrote:
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 Oca?a
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.ht
ml
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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-- 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