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RE: st: Re: psedual R2 of mim:gllamm fitted models

From   "Lachenbruch, Peter" <>
To   "''" <>
Subject   RE: st: Re: psedual R2 of mim:gllamm fitted models
Date   Fri, 7 May 2010 09:52:54 -0700

In addition, the interpretation as 'explained variance' is inappropriate.  The name pseudo-R2 leads the unwary to this interpretation and we need to be cautious.  Of course, this list doesn't need this warning.


Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001

-----Original Message-----
From: [] On Behalf Of Stas Kolenikov
Sent: Friday, May 07, 2010 8:46 AM
Subject: Re: st: Re: psedual R2 of mim:gllamm fitted models

My two cents: pseudo-R^2 from logistic regression is flaky (does the
value of 1 indicate perfect prediction? Wait, perfect prediction means
that logistic regression estimator does not converge!). Pseudo-R^2
from a multilevel logistic regression is double-flaky (variance at
what level does it try to explain?). Pseudo-R^2 from a multilevel
model with imputed data is triple-flaky (does it incorporate
imputation variance?).

To compare performance of competing models, you can -gllapred- your
outcomes (preferably in a separate validation sample) and see which
model generates more accurate predictions.

On Fri, May 7, 2010 at 10:35 AM,  <> wrote:
> Hi all,
> I need to compare which of two sets of variables explain more of the
> variance of the response variable. I use mim:gllamm to fit two multilevel
> logistic regression models containing the two different sets of variables.
> Would comparing the pseudo R2 of the two models answer my question of
> which set of varaibles explain more variance? If so, how I can get the
> pseudo R2 of mim:gllamm model. The -trace- option of gllamm can obtain the
> R2 but will not give the R2 in mim:gllamm output. Thanks a lot.

Stas Kolenikov, also found at
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