Simon Pickett
2006-Sep-05 14:01 UTC
[R] 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
Andrew Robinson
2006-Sep-05 14:25 UTC
[R] help: advice on the structuring of ReML models for analysing 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) 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 > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.-- 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 ms.unimelb.edu.au