You can also refer to the following papers which have proposed Rsquares for
the linear mixed model:

Vonesh, E. F., Chinchilli, V. P., and Pu, K. W. (1996), "Goodness-of-fit in
generalized nonlinear mixed-effects models," Biometrics, 52, 572-587.

Zheng, B. Y. (2000), "Summarizing the goodness of fit of generalized linear
models for longitudinal data," Statistics in Medicine, 19, 1265-1275.

Xu, R. H. (2003), "Measuring explained variation in linear mixed effects
models," Statistics in Medicine, 22, 3527-3541.

Also:

Vonesh, E. F. and Chinchilli, V. M. (1997), Linear and nonlinear models for
the analysis of repeated measurements, Marcel Dekker. (pages 419-427)

Sadly, I have found based on simulations that these pseudo-Rsquares don't
work very well.  I will be presenting a paper at JSM in August where I'm
proposing a new statistic for linear mixed models where you have conditional
independence.  The manuscript has not gone through the peer-review process
yet.

Jean G. Orelien
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Spencer Graves
Sent: Friday, March 17, 2006 11:14 PM
To: Angelo Colombo
Cc: [email protected]
Subject: Re: [R] R2 in mixed model

          The coefficient of deterimination, R^2, is defined as the percent
of 
the variance explained.  For a mixed model, we need to ask, "percent of 
WHICH variance?"  For more comments, I tried "RSiteSearch" with various 
kew words.  The most relevant comment I found was 
"http://finzi.psych.upenn.edu/R/Rhelp02a/archive/48023.html";, which I 
got from RSiteSearch("R squared with mixed effects").

          hope this helps.
          spencer graves

Angelo Colombo wrote:

> Hello.
> I have some basic question for you!.
> Has calculating R2 sense in mixed model? I think no! But i don't why!
> 
> Thank in advance for your help
> Angelo
> 
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