> As I described in an earlier post(
> https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q1/003503.html)
> my data is actually hierarchical down to the level of fields within farms.
>
> There is more than 500 cases on 8 farms in 6 regions.
> Would you not think that gives enough power to distinguish within region
> variability vs. between regions?
>


That gives you plenty of replication to distinguish between farms. But to
distinguish between farm effects and region effects you have very little
replication - 2 or fewer replicates per region. (Unless you're saying you
have 8 farms per region, but it sounded earlier like you have 8 farms in
total). Based purely on these data - how can you determine how variable
farms within a region are when you only have 2 farms per region? You can do
it, n>1, but what I'm saying is that you don't have much power.


> Moreover I don't understand your argument that fitting random efects with
> less than 5 levels was dodgy, as often examples in the books have 3 samples
> from one beach, or 3 laboratory workers within one laboratory. These are
> less than 5 levels, are they not?
>


It can be done, but whether it should be done depends a lot on whether you
are actually interested in these random effects or whether you are just
including them to deal with "nuisance" variables. You said

"I would like to keep the region effect in the model to separate and compare
the effect size of region vs. farm"

And so I was primed to be cautious about interpreting them. Furthermore, you
have only 2 levels and sometimes 1. To fit the random effect you have to
estimate a variance and here you're trying to estimate a variance from a
sample of 2. So interpreting that variance is dodgy.


As you said in your first post, adding a region effect on top of a farm
effect does not improve the model. What I'm saying is that this is totally
to be expected because you don't have enough farms per region to separate
farm and region effects.



Andy.

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