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

[nasi0009 at umn.edu]

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