On Wed, Apr 17, 2013 at 1:14 PM, Boulanger, Yan <
Yan.Boulanger@rncan-nrcan.gc.ca> wrote:

> Hi folks,
>
> This seems a very (I mean very...) basic mixed model question but I would
> like to have your feeling about this. I start from scratch with mixed
> models. I'm fitting this very simple mixed model:
>
> fm1 <- lmer(dbh_tree ~ log(age_tree) + (1|plot_name), PICE.MAR_tree)
>
> where dbh_tree is the diameter at breast height of a tree, age_tree the
> age of the tree at bh, plot_name is the plot where the tree was sampled and
> PICE.MAR_tree, my dataset. This is not an experimental setup where a fixed
> number of trees was sampled per plot. Indeed, some plots have as high as
> 150 trees (very few...) whereas others has only 1... At that is the (well
> one of the...) problem. How may I fit a mixed model where, in several
> cases, well, maybe 50%, there is only 1 tree per level of the random factor
> ? So, no variation within the random factor... I could forget the random
> factor but of course, this would lead to "partial" pseudoreplication. On
> the other hand, I could drop all plots with only one tree but this would
> discard about half of the plots. Am I right when I say that the
> coefficients (for the fixed variables) are unbiased when not considering
> the random factor ? Indeed, I'm not interested in CI but "only" to fixed
> variable coefficients.
>

I think you will find that there is very little difference in the model
fits whether you include or exclude the single-observation groups.  Try it
and see.

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