I'm helping a colleague with a repeated-measures analysis in which each
subject's fixed effect changes over the course of data collection.  He's
measuring a continuous, positive response variable (`Response`) in
agricultural plots (`ID`) (which themselves are grouped within `Blocks`) as
a function of `Crop` type.  The response variable is measured 4 times over
the course of the season.  The analysis would normally look something like
this:

     modelout <- lmer(Response ~ Crop + (Month|ID) + (1|Block) ,
data=cropdata, family=gamma(link="inverse"))

However, just to make things complicated, the first measurement is done
prior to planting, so it's influenced by the *previous year's* crop type.
 That is, for plot A1, Month 1's `Crop` is Wheat, but Month 2-4's `Crop`
types are Barley.  Is there any way to analyze this in a single model?

Alternatively, I see two options: 1) Analyze `Month`=1 separately from the
repeated-measures analysis for `Month`=2-4; 2) Use compound fixed effects,
e.g., Wheat-Barley, Wheat-Corn, etc.

Any suggestions?  Thanks in advance!

-- 
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Nicole L. Michel, PhD
School of Environment and Sustainability,
University of Saskatchewan
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