[R-sig-ME] Problem with binomial-normal model in lmer

[Ben Bolker]

    I have encountered this issue too (I wanted to fit a
lognormal-Poisson model as in Elston et al 2001, Parasitology
122:563-569), but didn't want to bother Doug Bates (having
bugged him about mcmcsamp so often in the past ...)  I too
would be curious to know whether this is something that could
be changed or a deeply rooted computational assumption.

    In the meantime, you could consider fitting a quasibinomial
model ...

     cheers
      Ben Bolker

  [trying again -- last one was rejected ... because of signature ? ]


nasi0009 at umn.edu wrote:
> I have a question about the possibility of fitting a binomial-normal model 
> with lmer. I explain my problem using notation used in "Linear mixed model 
> implementation in lme4" by Prof. Bates 
> (http://stat.ethz.ch/CRAN/doc/vignettes/lme4/Implementation.pdf).
> 
> By binomial-normal, I mean a model that another term is added to Equation 
> (29) (on page 28) of the paper, i.e. \eta=X\beta+Zb+\epsilon where \epsilon 
> is N(0,\sigma_e). I thought that by modifying Z, \epsilon can be absorbed 
> into Z. However, when I tried to test this on a simple simulated data set I 
> received an error "Error in mer_finalize(ans, verbose) : q = > n = ". 
> Basically, it seems that the basic assumption in lmer for GLMM models is 
> that Z should be a thin matrix (more rows than columns). Naturally, this 
> data-level normal error term can not be absorbed as another random effect 
> since the total number of random effects exceeds the number of 
> observations. Is there any other way around this problem? Am I doing 
> something nonsense?
> 
> I appreciate if Prof. Bates or anyone who used lmer for GLMM comments on 
> this.
> 
> Thanks,
> Ali
> 
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