[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
Re: [R] mixed effects model:how to include initial conditions
Christoph Scherber wrote: 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. I think you should include it as a covariate but not in the way you have written it. I would include it as a separate term, not in an interaction alternative-lme(growth~initialsize+block*treatment*factor1*factor2, random=~1|plot/treatment) I recommend that you look carefully at the number of coefficients that you need to estimate in the model as you have specified it and perhaps change to an initial model that had more additive effects and fewer interactions. __ 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
RE: [R] mixed effects model:how to include initial conditions
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 firstwhich 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