[R] Mixed GLM methodology and execution question

Ben Ridenhour
Wed, 08 Mar 2006 22:57:16 -0000

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



	[[alternative HTML version deleted]]