Hi all,
I have a question regarding how to properly analyze a data set and then how to perform the analysis in R.

First,
I have data that I would like to analyze using a mixed GLM (I think this is the most appropriate method, but I am unsure).  In a mixed model (y = X*beta+Z*gamma+epsilon), I would like to structure the variance matrices of gamma, G, and the error, R, to take advantage of all my information. The structure of the data is like this:

Response Variable: 
 y = continuous response variable 
 


Predictor Variables: 
 x1 =  nominal treatment 
 x2 =  nominal group 
 


Random Variables: 
 z = nominal subgroup of x2, i.e. z is nested within x2 
 


Other variables(?; I'm not sure what exactly these are) 
 z1 = first continuous property of z 
 z2 = second continuous property of z 
 z3 =  third continuous property of z 
 

Presumably all the traits z1-z3 could potentially affect y, though I'm primarily interested in the model y=x1+x2+x1*x2. My wish is to put z in as a random variable and z1-z3 in the error matrix R.

A small data sample  would be like

x1    x2     z      z1      z2         z3         y
L1   A1   S1   1.23   4.59   -1.02   100.45
L2   A1   S1   1.23   4.59   -1.02   113.09
L1   A1   S2   1.50   3.76    -0.06  119.21
L2   A1   S2   1.50   3.76    -0.06  150.44
L1   A2   S3   1.09   4.01    -1.50  109.18
L2   A2   S3   1.09   4.01    -1.50  170.23
 L1   A2   S4   1.01   3.70    -0.78  109.26
 L2   A2   S4   1.01   3.70    -0.78  99.44


What is correct way to put together my model/matrices for this situation?  How do accomplish such a task in R?

Thanks,
Ben



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