[R-sig-ME] Problem with binomial-normal model in lmer
nasi0009 at umn.edu
nasi0009 at umn.edu
Fri Feb 22 08:04:08 CET 2008
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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