[R] Two-level Poisson model with cross classified random effects

[Shige Song]

Dear Renaud,

Thank you very much! So now lme4 has both laplace and adaptive
quadrature? Wow, that's really impressive!

Shige

On 8/17/05, Renaud Lancelot <renaud.lancelot at cirad.fr> wrote:
> Shige Song a écrit :
> > Dear All,
> >
> > I have two-level data with individual as level-1, birth cohort and
> > community as level-2. All the level-2 covariates are generated from
> > the level-1 covariates by cross-classifying by cohort and community.
> >
> >>From what I read, an ordinary three-level model with individual nesed
> > within birth cohort nested within community, or individual nested
> > within community nested within birth cohort do not work well, neither
> > do model with individual nested within community by cohort. The right
> > way to go is to estimate a two-level model with two separate random
> > effects: within cohort and within community. The question I want to
> > ask is: how to do this using lmer?
> >
> > I tried the following for a simple unconditioal model:
> >
> > m1 <- lmer(count ~ offset(log(total)) + (1|comm) + (1|cohort), data, poisson)
> >
> > where "count" is the dependent variable, "total" is the exposure
> > variable, "comm" is the community ID, and "cohort" is the birth cohort
> > ID. Will this be suffice? I got really smalle randome intercept
> > (5.0000e-10 for community and 4.4226e-05 for cohort), which got me a
> > bit nervous.
> >
> > Thanks!
> >
> > Best,
> > Shige
> >
> > ______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
> >
> 
> The default method to fit generalized mixed-effect models with lme4 is
> "PQL" which is fast but not very accurate for the random effects (they
> might be underestimated). Try other methods ("Laplace" or "AGQ") to see
> if you get different results.
> 
> Best,
> 
> Renaud
> 
> --
> Dr Renaud Lancelot, vétérinaire
> Projet FSP régional épidémiologie vétérinaire
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>