Multilevel logistic regression using lmer vs glmmPQL vs.gllamm in Stata

On Wed, 3 Aug 2005, Bernd Weiss wrote:

I am trying to replicate some multilevel models with binary outcomes
using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively.
That's not going to happen as they are not using the same criteria.
the glmmPQL and lmer both use the PQL method to do it ,so can we get the same result by setting some options to the command?
The data can be found at <http://www.uni-koeln.de/~ahf34/xerop.dta>.

The relevant Stata output can be found at  <http://www.uni-
koeln.de/~ahf34/stataoutput.txt>. First, you will find the
unconditional model, i.e. no level1- or 2-predictor variables. The
second model contains some level 1-predictor variables

My R file can be found at <http://www.uni-koeln.de/~ahf34/xerop.R>.

Beside the fact that there is a difference between the estimates of
the intercept (unconditional model: R: -2.76459 and Stata: -2.698923)
I am especially interested in the level 2 variance.

In Stata the level 2 variance is about 1.03, while in R it is  4.68.

Using glmmPQL from package MASS again gives different results for the
level 2 variance component. What is meant by "Residual"? I thought
the level 1 variance is fixed to (pi^2)/3.
Please read the book for which this is support software, as it definitely 
does not say that, and it does explain how such differences can occur.

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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2005-08-03

------
Deparment of Sociology
Fudan University

Blog:http://sociology.yculblog.com
Am 3 Aug 2005 um 18:02 hat ronggui geschrieben:
On Wed, 3 Aug 2005, Bernd Weiss wrote:

I am trying to replicate some multilevel models with binary
outcomes using R's "lmer" and "glmmPQL" and Stata's gllmm,
respectively.
[...]
the glmmPQL and lmer both use the PQL method to do it ,so can we get
the same result by setting some options to the command?

Thanks to Prof. Ripley and ronggui for their answers. 

To verify my findings I tried other datasets and simulated some data 
and compared the results between R and Stata. Everything works fine, 
no differences -- except for the xerop-dataset. 

Having a closer look to the R output I found some unusual values for 
AIC, BIC and deviance, see below:

           AIC           BIC         logLik      deviance
 1.797693e+308 1.797693e+308 -8.988466e+307 1.797693e+308    

I assume I have to change some of the lmer-parameters but have 
absolutely no idea which one. 

Again, I would appreciate any help.

Bernd