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.
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