R help - Mar 2006 - Mixed GLM methodology and execution question

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]]

Have you considered 'lmer' (split between the lme4 and Matrix 
packages)?  To learn about this, I suggest you also consult the 
vignettes in the 'mlmRev' package.

	  hope this helps,
	  spencer graves
p.s.  If you are unfamiliar with vignettes, I suggest you consult 
(http://finzi.psych.upenn.edu/R/Rhelp02a/archive/67006.html)

Ben Ridenhour wrote:
> 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]]
> 
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