CONTRAST
'label' rowdescription<, …, rowdescription> </ options> ;
where a rowdescription is defined as follows:
effect values<, …, effect values>
The CONTRAST statement provides a mechanism for obtaining customized hypothesis tests. It is similar to the CONTRAST and ESTIMATE statements in other modeling procedures.
The CONTRAST statement enables you to specify a matrix, , for testing the hypothesis , where is the vector of intercept and slope parameters. You must be familiar with the details of the model parameterization that PROC LOGISTIC uses (for more information, see the PARAM= option in the section CLASS Statement). Optionally, the CONTRAST statement enables you to estimate each row, , of and test the hypothesis . Computed statistics are based on the asymptotic chisquare distribution of the Wald statistic.
There is no limit to the number of CONTRAST statements that you can specify, but they must appear after the MODEL statement.
The following parameters are specified in the CONTRAST statement:
identifies the contrast in the displayed output. A label is required for every contrast specified, and it must be enclosed in quotes.
identifies an effect that appears in the MODEL statement. The name INTERCEPT can be used as an effect when one or more intercepts are included in the model. You do not need to include all effects that are included in the MODEL statement.
are constants that are elements of the matrix associated with the effect. To correctly specify your contrast, it is crucial to know the ordering of parameters within each effect and the variable levels associated with any parameter. The “Class Level Information” table shows the ordering of levels within variables. The E option, described later in this section, enables you to verify the proper correspondence of values to parameters. If too many values are specified for an effect, the extra ones are ignored. If too few values are specified, the remaining ones are set to 0.
Multiple degreeoffreedom hypotheses can be tested by specifying multiple rowdescriptions; the rows of are specified in order and are separated by commas. The degrees of freedom is the number of linearly independent constraints implied by the CONTRAST statement—that is, the rank of .
More details for specifying contrasts involving effects with fullrank parameterizations are given in the section FullRank Parameterized Effects, while details for lessthanfullrank parameterized effects are given in the section LessThanFullRank Parameterized Effects.
You can specify the following options after a slash (/):
If an effect involving a CLASS variable with a fullrank parameterization does not appear in the CONTRAST statement, then all of its coefficients in the matrix are set to 0.
If you use effect coding by default or by specifying PARAM=EFFECT in the CLASS statement, then all parameters are directly estimable and involve no other parameters. For example, suppose an effectcoded
CLASS variable A
has four levels. Then there are three parameters () representing the first three levels, and the fourth parameter is represented by

To test the first versus the fourth level of A
, you would test

or, equivalently,

which, in the form , is

Therefore, you would use the following CONTRAST statement:
contrast '1 vs. 4' A 2 1 1;
To contrast the third level with the average of the first two levels, you would test

or, equivalently,

Therefore, you would use the following CONTRAST statement:
contrast '1&2 vs. 3' A 1 1 2;
Other CONTRAST statements are constructed similarly. For example:
contrast '1 vs. 2 ' A 1 1 0; contrast '1&2 vs. 4 ' A 3 3 2; contrast '1&2 vs. 3&4' A 2 2 0; contrast 'Main Effect' A 1 0 0, A 0 1 0, A 0 0 1;
When you use the lessthanfullrank parameterization (by specifying PARAM=GLM in the CLASS statement), each row is checked for estimability; see the section Estimable Functions in Chapter 3: Introduction to Statistical Modeling with SAS/STAT Software, for more information. If PROC LOGISTIC finds a contrast to be nonestimable, it displays missing values in corresponding rows in the results. PROC LOGISTIC handles missing level combinations of classification variables in the same manner as PROC GLM: parameters corresponding to missing level combinations are not included in the model. This convention can affect the way in which you specify the matrix in your CONTRAST statement. If the elements of are not specified for an effect that contains a specified effect, then the elements of the specified effect are distributed over the levels of the higherorder effect just as the GLM procedure does for its CONTRAST and ESTIMATE statements. For example, suppose that the model contains effects A and B and their interaction A*B. If you specify a CONTRAST statement involving A alone, the matrix contains nonzero terms for both A and A*B, since A*B contains A. See rule 4 in the section Construction of Least Squares Means in Chapter 42: The GLM Procedure, for more details.