[R] Strange Estimates from lmer and glmmPQL

[Rick Bilonick]

I'm trying to fit a generalized mixed effects model to a data set where
each subject has paired categorical responses y (so I'm trying to use a
binomial logit link). There are about 183 observations and one
explanatory factor x. I'm trying to fit something like:

(lmer(y~x+(1|subject)))

I also tried fitting the same type of model using glmmPQL from MASS. In
both cases, I get a t-statistic that is huge (in the thousands) and a
tiny p-value. (Just for comparison, if I use lrm ignoring the clustering
I get a t-statistic around 3 or so and what appears to be a reasonable
estimated coefficient which is very close to the estimated coefficient
using just one observation from each subject.

Most of the subjects have two responses and in almost all cases the
responses are identical although the explantory factor values are not
always identical for each subject.

If I use geeglm from geepack, I get reasonable estimates close to the
naive model results.

I also tried using the SAS glimmix macro to fit a generalized mixed
model and the routine does not converge.

Why does geeglm appear to work but not lmer and glmmPQL? Is this likely
to be due to my particular data set?

Rick B.