[R] question in using nlme and lme4 for unbalanced data

[Ben Bolker]

Chi Yuan <cyuan <at> email.arizona.edu> writes
> 
> Hello:
>  I need some help about using mixed for model for unbalanced data. I
> have an two factorial random block design. It's a ecology
> experiment. My two factors are, guild removal and enfa removal. Both
> are two levels, 0 (no removal), 1 (removal). I have 5 blocks. But
> within each block, it's unbalanced at plot level because I have 5
> plots instead of 4 in each block. Within each block, I have 1 plot
> with only guild removal, 1 plot with only enfa removal, 1 plot for
> control with no removal, 2 plots for both guild and enfa removal. I am
> looking at how these treatment affect the enfa mortality rate. I
> decide to use mixed model to treat block as random effect. So I try
> both nlme and lme4. But I don't know whether they take the unbalanced
> data properly. So my question is, does lme in nlme and lmer in lme4
> take unbalanced data? How do I know it's analysis in a proper way?

  Didn't Bert Gunter and I already provide answers to this question
last week? Can you please clarify what about those answers you didn't
understand?

> Another question is about p values.
> I kind of heard the P value does not matter that much in the mixed
> model because it's not calculate properly. Is there any other way I can
>  tell whether the treatment has a effect not? I know AIC is for model
> comparison,
> do I report this in formal publication?

  It is indeed hard to compute p-values, but ... if you use AIC,
you are essentially making the same assumption as if you assumed
that the denominator degrees of freedom were infinite in an F test
(or if you used the likelihood ratio test).

[snip results]

  Ben Bolker