contrast lme and glmmPQL and getting additional results...

From what you say, it seems like you have a linear normal (mixed) 

I have a longitudinal data analysis project.  There are 10
observations on each of 15 units, and I'm estimating this with
randomly varying intercepts along with an AR1 correction for the error
terms within units.  There is no correlation across units.  Blundering
around in R for a long time, I found that for linear/gaussian models,
I can use either the MASS method glmmPQL (thanks to Venables and
Ripley) or the lme from nlme (thanks to Pinheiro and Bates).  (I also
find that the package lme4 has GLMM, but I can't get the correlation
structure to work with that, so I gave up on that one.)

The glmmPQL and lme results are quite similar, but not identical.

Here are my questions.

1. I believe that both of these offer consistent estimates. Does one
have preferrable small sample properties?  Is the lme the preferred
method in this case because it is more narrowly designed to this
gaussian family model with an identity link?  If there's an argument
in favor of PQL, I'd like to know it, because a couple of the
Hypothesis tests based on t-statistics are affected.

2. Is there a pre-made method for calculation of the robust standard
errors?

I notice that model.matrix() command does not work for either lme or
glmmPQL results, and so I start to wonder how people calculate
sandwich estimators of the standard errors.

3. Are the AIC (or BIC) statistics comparable across models?  Can one
argue in favor of the glmmPQL results (with, say, a log link) if the
AIC is more favorable than the AIC from an lme fit?  In JK Lindsey's
Models for Repeated Measurements, the AIC is sometimes used to make
model selections, but I don't know where the limits of this
application might lie.


-- 
Paul E. Johnson                       email: pauljohn at ku.edu
Dept. of Political Science            http://lark.cc.ku.edu/~pauljohn
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