forcing uncorrelated random effects

Here is an example. The second has no correlation.

(fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
(fm2 <- lmer(Reaction ~ Days + (Days - 1|Subject), sleepstudy))
-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org 
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf 
Of Claus Wilke
Sent: Thursday, June 11, 2009 1:58 PM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] forcing uncorrelated random effects

Dear list,

by default, lmer assumes that random effects are correlated. 
Is it possible to force them to be uncorrelated?

Specifically, assume I'm measuring cell counts in multiple 
patients over time, and want to fit the following two models:
m1 = lmer( count ~ (1|patient)+time )
m2 = lmer( count ~ (time|patient)+time )
Model 2 has two additional parameters over model 1, a 
variance of random slopes and a covariance of random slopes 
and random intercepts. How do I specify a model that has 
random slopes but no covariance between slopes and intercepts?

Thanks a lot,
  Claus

--
Claus Wilke
Section of Integrative Biology
 and Center for Computational Biology and Bioinformatics 
University of Texas at Austin
1 University Station C0930
Austin, TX 78712
cwilke at mail.utexas.edu
512 471 6028

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forcing uncorrelated random effects

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