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st: xtmelogit: comparing models


From   Luca Campanelli <l.campanelli@yahoo.it>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: xtmelogit: comparing models
Date   Fri, 5 Oct 2012 16:50:32 +0100 (BST)

Dear Stata users, 
I’d like to fit and compare mixed effects logistic regression models with crossed random effects using the function xtmelogit (Stata 12IC for Windows). 

For example (“group” has 2 levels[0,1] and “condition” has 3 levels[1,2,3]): 
(1) xtmelogit resp i.group i.condition , || _all: R.item, covariance(id)  || sbj: , covariance(id)
(2) xtmelogit resp i.group i.condition i.group#i.condition , || _all: R.item, covariance(id)  || sbj: , covariance(id)

In comparing two models, I found a big discrepancy between lrtest on one side, and AIC-BIC on the other side. lrtest was highly significant, indicating that (2) was better than (1), while AIC and BIC values were clearly smaller for model (1). 
Which should I trust? 

Does this apply to my case http://www.stata.com/support/faqs/statistics/likelihood-ratio-test/ ; ? 
If yes, how can I do the Wald test? 
Would it be: 
test 1.group#2.condition 1.group#3.condition

Is it correct? I saw others using testparm or lincom. 
I would appreciate any help to understand what the appropriate thing to do is. 

thank you, 
Luca

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