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

Ben Bolker bolker at ufl.edu
Fri Feb 22 14:53:34 CET 2008

    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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