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Questions - next steps in GLMM analysis on nested ecological dataset

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On 10-11-07 11:14 AM, Shawn McCracken wrote:
Update: I have been working on the glmmADMB package a bit.  The
current version on R-forge installs OK on my MacOS X.6 machine. It
contains 32-bit binaries which it automatically puts in the correct
location, so that you shouldn't have to mess around with doing this
stuff manually.  Dave F. has sent me compiled 64-bit OS X binaries, but
I haven't gotten around to incorporating them yet (the 32-bit binaries
do work on my system, although presumably the 64-bit ones would be
faster in general).
  So
  install.packages("glmmADMB",repos="http://r-forge.r-project.org")
should work on MacOS.
  It would be helpful to get reports of trouble from list members who
try it.

  To follow up on some of your other questions with my own opinions:
* as I recommend on <http://glmm.wikidot.com/faq> (I have just added a
few words to make my personal opinions clearer), I would recommend
glmm.admb or glmer with individual-level random effects over the various
quasi- options.
 * glmm.admb currently only works with a single random effect, so you
can't do nested random effects that way.  You could build a more
complete model in AD Model Builder, or revert to glmer.
 * Your model specification

m1po<-lmer(count~treat+treedbh+treehgt+numepi+elevepi+hgtepi+leafepi+
(1|tree/epi),family=poisson,data=ecpad2)

  looks reasonable.  If you say
ecpad2$indiv <- 1:nrow(ecpad2)
and add +(1|indiv) to your model specification you will have an
individual-level random effect.

 * Is 'treat' your site variable?  In any case, if you are trying to do
a statistical comparison between only two sites you have a major
pseudo-replication problem (Hurlbert 1984).

  * The p-values that you get from summary(lmer) are Wald Z statistics,
they assume large data sets and are possibly unreliable for
moderate-sized data sets ...

 * Opinions differ on the value of backward stepwise model reduction. It
is standard practice in many ecological contexts and is suggested for
moderate model complexity by many respected practicing
(eco)statisticians (Bates, Wood, Zuur ...) but is vehemently decried by
others (Harrell).  I would probably base inference on your full model
rather than doing backward elimination.
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