Christoph, If you take a look at journal articles related to this topic published elsewhere, you will find that the most common analysis is:
model<-lme(growth~initialsize+block+treatment+....) maybe it would be important to check if your model really needs the interactions you pointed out. I would suggest to try simpler models first....which generally work OK in analysis of growth of plants, trees, etc CM >-- Mensaje Original -- >Date: Fri, 21 Jan 2005 12:30:07 +0100 >From: Christoph Scherber <[EMAIL PROTECTED]> >To: r-help@stat.math.ethz.ch >Subject: [R] mixed effects model:how to include initial conditions > > >Dear R users, > >I am analyzing a dataset on growth of plants in response to several >factors. I am using a mixed-effects model of the following structure: > >model<-lme(growth~block*treatment*factor1*factor2, >random=~1|plot/treatment/initialsize) > >I have measured the initial size of the plants (in 2003) and thought it >might be sensible to include this (random) variation into the random >effects term of the model. > >Is that correct? Or should "initialsize" rather be included as a >covariate into the fixed effects term, as in: > >alternative<-lme(growth~block*initialsize*treatment*factor1*factor2, >random=~1|plot/treatment) > >I would very much appreciate any suggestions on how to analyze these >data correctly. > >Best regards >Chris. > >______________________________________________ >R-help@stat.math.ethz.ch mailing list >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html