R help - Sep 2006 - help: advice on the structuring of ReML models for analysing growth curves

Hi R experts,
I am interested on the effects of two dietry compunds on the growth of
chicks. Rather than extracting linear growth functions for each chick and
using these in an analysis I thought using ReML might provide a neater and
better way of doing this. (I have read the pdf vignette("MlmSoftRev")
and
"Fitting linear mixed models in R" by Douglas Bates but I am not
entirely
sure that I have the right solution).

Basically I fed chicks in nest boxes over a period of time and weighed
them each time I fed them. I presume that "chick id" should be a
random
factor and should be nested within "nest box number"? (Chicks were not
moved around so this should make things more simple). Also since the
chicks were measured repeatedly over time I presume that this should be a
random factor? Growth is not linear exactly (more quadratic), so I thought
rather than put time in the fixed model I want to control for the effects
of time as a random factor....
The resulting model is this
where id=chick identity and brood=nest box
model1<-lmer(weight~treatment1*treatment2*brood
size*sex+(id|brood)+(1|brood)+(1|age), data=H)

Is this the "right" approach or am I barking up the wrong tree?
Any suggestions much appreciated,
Simon


Simon Pickett
PhD student
Centre For Ecology and Conservation
Tremough Campus
University of Exeter in Cornwall
TR109EZ
Tel 01326371852

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) 

You might also like to consider a quadratic term in age, if you think
that growth is quadratic. 

Cheers

Andrew

ps spaces enhance legibility :)

On Tue, Sep 05, 2006 at 03:01:30PM +0100, Simon Pickett
wrote:
> Hi R experts,
> I am interested on the effects of two dietry compunds on the growth of
> chicks. Rather than extracting linear growth functions for each chick and
> using these in an analysis I thought using ReML might provide a neater and
> better way of doing this. (I have read the pdf
vignette("MlmSoftRev") and
> "Fitting linear mixed models in R" by Douglas Bates but I am not
entirely
> sure that I have the right solution).
> 
> Basically I fed chicks in nest boxes over a period of time and weighed
> them each time I fed them. I presume that "chick id" should be a
random
> factor and should be nested within "nest box number"? (Chicks
were not
> moved around so this should make things more simple). Also since the
> chicks were measured repeatedly over time I presume that this should be a
> random factor? Growth is not linear exactly (more quadratic), so I thought
> rather than put time in the fixed model I want to control for the effects
> of time as a random factor....
> The resulting model is this
> where id=chick identity and brood=nest box
> model1<-lmer(weight~treatment1*treatment2*brood
> size*sex+(id|brood)+(1|brood)+(1|age), data=H)
> 
> Is this the "right" approach or am I barking up the wrong tree?
> Any suggestions much appreciated,
> Simon
> 
> 
> Simon Pickett
> PhD student
> Centre For Ecology and Conservation
> Tremough Campus
> University of Exeter in Cornwall
> TR109EZ
> Tel 01326371852
> 
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-- 
Andrew Robinson  
Department of Mathematics and Statistics            Tel: +61-3-8344-9763
University of Melbourne, VIC 3010 Australia         Fax: +61-3-8344-4599
Email: a.robinson at ms.unimelb.edu.au         http://www.ms.unimelb.edu.au