[R] help: advice on the structuring of ReML models foranalysing growth curves

Berton Gunter gunter.berton at gene.com
Tue Sep 5 18:14:20 CEST 2006

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Andrew Robinson
> Sent: Tuesday, September 05, 2006 7:25 AM
> To: Simon Pickett
> Cc: r-help at stat.math.ethz.ch
> Subject: Re: [R] help: advice on the structuring of ReML 
> models foranalysing growth curves
> Hi Simon,
> overall I think that lmer is a good tool for this problem.  It's
> impossible to reply definitively without the full details on the
> experimental design.
> Caveat in place, I have questions and some suggestions.  Are
> treatment1 and treatment2 distinct factors, or two levels of a
> treatment, the dietary compound?  Also, what is broodsize?
> If you want to nest chick id within brood, I think that you should
> include the interaction as a random factor.  If you'd like the age
> effects to differ between chicks then age should be on the left of id.
> Thus, start with something like ...
> model1 <- lmer(weight ~ treatment +  broodsize + sex + age
>        + (1|brood) + (age|id:brood), data=H) 

FWIW, this model can also be easily fit with the lme() function (in the nlme
package) as the random effects are strictly nested. The only advantage in
doing so is that the lme tools for examining the model are somewhat more
developed and extensive (or am I just more familiar with them?)


- Bert Gunter
Genentech Non-Clinical Statistics
South San Francisco, CA
"The business of the statistician is to catalyze the scientific learning
process."  - George E. P. Box

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