[R-sig-ME] best practices and methods for fitting poisson/negative binomial glmm in R

[J. Aaron Hogan]

Dear R-sig mixed modelers,

It is a pleasure to be a part of this group.  I have a few questions
regarding fitting a glmm where the data are poisson/ negative binomially
distributed.

The data are count data from a factorial block design experiment.  3
blocks, 4 treatments (one of which is a control).

The data may or may not be zero inflated; I'm not sure if it matters for
the discussion.

I've got 5 fixed factors, 6 interactions between them and three random
factors; one for block, one for plot and one for a species effect (all
random intercepts).

I fit a model using the lme4 package using the glmer.nb() function and got
some failure to converge" errors. Those errors seemed relatively benign,
after reading some of Ben Bolkers github
https://github.com/lme4/lme4/issues/120
<https://mailtrack.io/trace/link/5ea299f7b41b8ac488434e535336fa40e0bbcb94?url=https%3A%2F%2Fgithub.com%2Flme4%2Flme4%2Fissues%2F120&userId=1227428&signature=726b3079a7674983>,
and checking the Hessian gradient with:

relgrad <- with(model.final at optinfo$derivs,solve(Hessian,gradient))
max(abs(relgrad))  # returns a very small number

Everything seems alright with that model (based on my understanding); I can
predict the fit back to simulated data. I get reasonable coefficients,
deviance etc.


My question is more of a philosophical one.  How do I know I have a solid
model?  Given the vast number of glmm packages, options etc, when do I know
that it's okay to stop modeling and start inferring on the model?

Should I try to fit the model in a different package, like 'glmmadmb' using
MCMC methods?
What is the best package and/or practice for going through the process of
fitting a glmm for poisson/negative binomially distributed count data?

What other suggestions or recommendations do the more experienced modelers
have for this case?



I do appreciate your time and help,

All the best,

-- 
J. Aaron Hogan M.Sc.
PhD Student
Florida International University
(970) 485-1412

<https://mailtrack.io/trace/link/acd41c31c2375846fae533bc9db76800cdb3a611?url=http%3A%2F%2Fgoog_657844730&userId=1227428&signature=6bca03c848fad248>
www.experiement.com/chinaroots
<https://mailtrack.io/trace/link/3b6f37ffc54fe93c2f7e5fbbe49c36519e142f53?url=http%3A%2F%2Fwww.experiement.com%2Fchinaroots&userId=1227428&signature=859c84ea4b7d42c1>

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