

	Gus, 

	I find your approach confusing. It seems to me that if you are
trying to extract information from a dataset, and your model is
overparameterized, then the best approach is to go with a simpler
model, or to transform your data so that they are simpler. But if
you divide your data into two parts, and fit separate models to each
part, then you're going to end up estimating just as many parameters
in total, if not more. 

	One way to simplify your data might be to subtract the initial cover
present in each plot at time zero, from the cover present at time 1
and 2. That way, you'd be analyzing the change in cover relative to
Pre-treatment, and you might be able to get away without an
interaction term, as in the model

	coverchange ~ Treatment + Year + (1|Plot)

	I think you'd want to make sure Year was a categorical variable. Of
course you'd have to carefully examine dataplots, residuals, etc., to
see if this model made sense. 

	The usual caveats apply to the above advice: I'm not a guru or
statistician, just a practicing ecologist width:100%;">From: Gus
Jespersen 
To:"r-sig-mixed-models@r-project.org" 
Cc:
Sent:Mon, 28 Jan 2013 14:55:36 -0800
Subject:[R-sig-ME] Model selection process and fixed effect reporting
with three candidate models

 Greetings mixed model gurus,
 I thought I'd try this again with a more descriptive subject. I am
hoping
 you can help me determine the appropriate process for model
selection and
 interpretation. I have a number of vegetation experiments of the
following
 form

 - 12 pairs of plots, paired (blocked) according to similarity in
 topography.
 - Randomization within each pair, one plot receiving "treatment" and
the
 other the "control." There's only one level of "treatment".
 - I measured the continuous response variable (cover of a plant)
before
 and twice yearly after treatment

 In trying to include data from both post-treatment years in a single
mixed
 model, error messages indicated I didn't have enough replicates to
support
 so many parameters, thus I was advised to instead treat each
post-treatment
 year separately.

 The advice I received was to use the pre-treatment cover (Year1) as
a
 predictor of each post-treatment cover (Year 1)in the following
fashion:

mod.full=lmer(Year2Cover [1] ~ Year1Cover*Treatment + (1|Pair)) #full
model
mod.null=lmer(Year2Cover [2] ~ Year1Cover+ (1|Pair)) #null

 #Perform the LRT
 anova(mod2.2,mod1.2)

 #if Pr(>Chisq)

Links:
------
[1] http://sitemail.hostway.com/http:
[2] http://sitemail.hostway.com/http:
[3] http://sitemail.hostway.com/http:
[4] http://mod.partial
[5] http://sitemail.hostway.com/http:
[6] http://mod.null
[7] http://sitemail.hostway.com/http:
[8] http://mod.null
[9] http://sitemail.hostway.com/http:
[10] mailto:jesper@u.washington.edu
[11] mailto:R-sig-mixed-models@r-project.org
[12] https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models


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