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| Shared Concepts and Topics |
Main Effects
If a classification variable has 
levels, the GLM parameterization generates columns for its main effect in the model matrix. Each column is an indicator variable for a given level. The order of the columns is the sort order of the values of their levels and frequently can be controlled with the ORDER= option in the procedure or CLASS statement.
Table 19.4 is an example where 
denotes the intercept and A and B are classification variables with two and three levels, respectively.
Data |
I |
A |
B |
|||||||
|---|---|---|---|---|---|---|---|---|---|---|
A |
B |
|
A1 |
A2 |
B1 |
B2 |
B3 |
|||
1 |
1 |
1 |
1 |
0 |
1 |
0 |
0 |
|||
1 |
2 |
1 |
1 |
0 |
0 |
1 |
0 |
|||
1 |
3 |
1 |
1 |
0 |
0 |
0 |
1 |
|||
2 |
1 |
1 |
0 |
1 |
1 |
0 |
0 |
|||
2 |
2 |
1 |
0 |
1 |
0 |
1 |
0 |
|||
2 |
3 |
1 |
0 |
1 |
0 |
0 |
1 |
|||
Typically, there are more columns for these effects than there are degrees of freedom to estimate them. In other words, the GLM parameterization of main effects is singular.
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