[R] Multilevel logistic regression using lmer vs glmmPQL vs. gllamm in Stata

From: Bernd Weiss <bernd.weiss_at_uni-koeln.de>
Date: Wed 03 Aug 2005 - 15:52:40 EST

Dear all,

I am trying to replicate some multilevel models with binary outcomes using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively.

The data can be found at <http://www.uni-koeln.de/~ahf34/xerop.dta>.

The relevant Stata output can be found at <http://www.unikoeln. 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.

I am a beginner in multilevel modeling so I assume I made some mistake either in interpreting the output or specifying the models.

I would appreciate any help.


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