Message-ID: <42F077C8.25154.B07C1D@localhost>
Date: 2005-08-03T05:52:40Z
From: Bernd Weiss
Subject: Multilevel logistic regression using lmer vs glmmPQL vs. gllamm in Stata
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.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.
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.
Bernd