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

[Christine Griffiths]

Dear R users,

Many of you may be familiar with my design as I have posted a number of 
queries before. Having consulted with someone in my department about 
estimating bias corrected confidence intervals for small sample sizes 
(rather than MCMC which Baayen et al. 2008 suggest should not be used), 
they implied that I should not be using lmer for such a small sample size 
as lmer was designed to deal with very large datasets. Is this still the 
case? If so what is regarded as a small sample size?

Below is a description of my data. I have 5/6 enclosures (replicates) per 
treatment - Aldabra/Radiata/control. Aldabra and radiata refer to two 
different tortoise species, while control lacks tortoises. The enclosures 
were assigned to a block: a block containing each of the 3 treatments, i.e. 
6 blocks in total. Each month for ten months I collected data: a repeated 
crossed design. Unfortunately, I have non-orthogonal, unbalanced data (5/6 
enclosures per treatment) as I cannot use a replicate within the aldabra 
and radiata treatments. These are however from different blocks so I am 
reluctant to axe them to achieve balanced data as this would leave me only 
4 blocks. I measured various attributes which I think that tortoises would 
have an impact on, e.g. plant count, species richness. Because my data is 
unbalanced and a repeated measures design I had chosen lmer to best model 
this.

For one other aspect, I calculate food web properties, for which I have no 
replication, i.e. only one observation per treatment per month. Would lmer 
be an acceptable way to analyse this data?

If lmer is not advised for the analyses of these data, what other analyses 
techniques should I investigate?

Baayen et al. (2008)Mixed-effects modeling with crossed random effects
for subjects and items. Journal of Memory and Language, 59, 390-412.

Many thanks,
Christine