proc glimmix fixed effects solution for logistic regression

[grzegorz]

Hi community,

I have question about solution of fixed effects in proc glimmix. I have data with following class variables:

Class	Levels	Values
group	3	1 2 3
method	3	A B C

and one binary variable "y". I have following code:

PROC glimmix data=tmp1.data1;

class group method;

model y=method / solution;

random int / subject=group;

run;

As the result of "solution" i have following table(here is part of it):

effect	group	estimate
intercept		0.25
method	A	0.3333
method	B	0.083333
method	C	0

My question is: Why the result of effect method: C is equal 0? Why does it estimate the intercept as the Beta_0+Beta_C, method:B as a Beta_B-Beta_C and so on..?

regards

[lvm · Accepted solution]

THis is what is supposed to happen. In GLIMMIX (also MIXED, GLM, etc.) an overparameterized model is used.This is needed to properly test for factor effects and get expected values. With three factor levels, you need a model with three parameters, but the overparameterized model has four parameters (intercept, beta_A, beta_B, and beta_C). The last one ends up as 0 in the estimation. For one factor, this means that the intercept is the expected value for the last level (C), and the other parameters are differences from C. For instance, the expected value for A is intercept + beta_A. This is described in the User's Guide.