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

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

#if Pr(>Chisq)<0.05, extract the fixed effect parameters from the full
model and report those, then repeat for the next post-treatment year.

I'd like to seek your advice on the following.

1.  Does this seem a reasonable approach, given the information provided?
2.  Many of the Year1* Treatment interaction terms are significant.
However, I played around some with dropping the interaction term
(mod.partial=lmer(Year2 ~ Year1+Treatment + (1|Pair) )
and found that in some cases just their seems to be a "main effect"
Treatment parameter that is significant, but having the interaction term is
raising the p-value of the LRT.  This has made me think that I should be
pursuing a model selection process involving all three models.  The way I
have been thinking to do this was

via the LRT,

1.  Determine if the interaction term should be present at all.  Perform
anova (mod.partial, mod.full). If p<0.05, go to step 2, if p>0.05, go to
step 3
2.  Perform anova(mod.null, mod.full).  If p<0.05, report fixed effect
estimates.
3.  Perform(mod.null,mod.partial), if p<0.05 report fixed effect estimates

Alright, I await your wisdom.

Thanks,
Gus

-- 
R. Gus Jespersen
PhD Candidate
College of Forest Resources
University of Washington
Box 352100
Seattle, WA 98195-2100
(206) 543-5777
jesper@u.washington.edu

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