Re: Mixed effects models and R2

["Roberts, J. Kyle" <jkrobert@bcm.tmc.edu>]

Graham,

 

Don't report any R^2 statistic for any mixed effects model.  Snijders & Bosker and Hox give lengthy discussions on why not to.  I am presenting a paper at AERA this year on possible R^2 measures for 2-level linear models, but I assure you that it is not easy, nor straight-forward.  If you look on my website at:

http://www.hlm-online.com/datasets/education/

Dataset 1 actually gives an example where adding a covariate increases the variance at level-2, which would mean negative variance explained (something not possible in OLS).  Typically, an R^2-type metric makes little sense in HLM/mixed effects models.

 

I hope that this helps,

Kyle

 

***************************************

J. Kyle Roberts, Ph.D.

Baylor College of Medicine

Center for Educational Outreach

One Baylor Plaza, MS:  BCM411

Houston, TX   77030-3411

713-798-6672 - 713-798-8201 Fax

jkrobert@bcm.edu

***************************************

 

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From: s-news-owner@lists.biostat.wustl.edu on behalf of GRAHAM LEASK
Sent: Sat 2/18/2006 5:06 AM
To: s-news@lists.biostat.wustl.edu
Subject: [S] Mixed effects models and R2

With mixed effects models in S plus is it feasible to describe models in terms of how much of the variation explained? If so how do I gain an estimate of how much of the variation is explained by the model (i.e. an R2 figure).

 

I am using S plus 7.0 but have relatively little experience in using this software.