[R-sig-ME] forcing uncorrelated random effects

[Doran, Harold]

I forgot a term

lmer(Reaction ~ Days + (1|Subject) + (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 Doran, Harold
> Sent: Thursday, June 11, 2009 2:20 PM
> To: Claus Wilke; r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] 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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> > 
> 
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