CONTRAST Statement
 CONTRAST 'label' effect values < ...
effect values > < /
options > ;
The CONTRAST statement enables you to perform custom hypothesis tests
by specifying an L vector or
matrix for testing the univariate hypothesis or the multivariate hypothesis L B M = 0.
Thus, to use this feature you must be familiar with the
details of the model parameterization that PROC GLM uses.
For more information, see the "Parameterization of PROC GLM Models" section.
All of the elements of the L vector may be
given, or if only certain portions of the L
vector are given, the remaining elements are constructed
by PROC GLM from the context (in a manner similar to rule 4
discussed in the "Construction of LeastSquares Means" section).
There is no limit to the number of CONTRAST statements you can specify,
but they must appear after the MODEL statement.
In addition, if you use a CONTRAST statement and a MANOVA,
REPEATED, or TEST statement, appropriate tests for contrasts are
carried out as part of the MANOVA, REPEATED, or TEST analysis.
If you use a CONTRAST statement and a RANDOM statement,
the expected mean square of the contrast is displayed.
As a result of these additional analyses, the
CONTRAST statement must appear before the
MANOVA, REPEATED, RANDOM, or TEST statement.
In the CONTRAST statement,
 label
 identifies the contrast on the output.
A label is required for every contrast specified.
Labels must be enclosed in quotes.
 effect
 identifies an effect that appears in the MODEL statement, or
the INTERCEPT effect.
The INTERCEPT effect can be used when
an intercept is fitted in the model.
You do not need to include all effects
that are in the MODEL statement.
 values
 are constants that are elements of the
L vector associated with the effect.
You can specify the following options in the CONTRAST
statement after a slash(/):
 E

displays the entire L vector. This option is useful in
confirming the ordering of parameters for specifying L.
 E=effect

specifies an error term, which must
be one of the effects in the model.
The procedure uses this effect as the
denominator in F tests in univariate analysis.
In addition, if you use a MANOVA or REPEATED
statement, the procedure uses the effect specified
by the E= option as the basis of the E matrix.
By default, the procedure uses the overall residual or error
mean square (MSE) as an error term.
 ETYPE=n

specifies the type (1, 2, 3, or 4, corresponding to Type I, II, III, and
IV tests, respectively) of the E= effect.
If the E= option is specified and the ETYPE= option is not, the procedure
uses the highest type computed in the analysis.
 SINGULAR=number

checking (GLM)
tunes the estimability checking.
If ABS(LLH) > C×number for any
row in the contrast, then L is declared nonestimable.
H is the (X'X)^{}X'X
matrix, and C is ABS(L) except for rows where
L is zero, and then it is 1.
The default value for the SINGULAR= option is 10^{4}.
Values for the SINGULAR= option must be between 0 and 1.
As stated previously, the CONTRAST statement
enables you to perform custom hypothesis tests.
If the hypothesis is testable in the univariate case,
SS() is computed as

(Lb)'(L(X'X)^{} L')^{1}(Lb)
where b = (X'X)^{}X'y.
This is the sum of squares displayed on the analysisofvariance table.
For multivariate testable hypotheses, the
usual multivariate tests are performed using

H = M'(LB)' (L(X'X)^{} L')^{1} (LB)M
where B = (X'X)^{}X'Y and
Y is the matrix of multivariate responses or dependent variables.
The degrees of freedom associated with the hypothesis is equal to the row
rank of L.
The sum of squares computed in this situation are equivalent to
the sum of squares computed using an L matrix with any row
deleted that is a linear combination of previous rows.
Multipledegreeoffreedom hypotheses can
be specified by separating the rows of the
L matrix with commas.
For example, for the model
proc glm;
class A B;
model Y=A B;
run;
with A at 5 levels and B at 2 levels, the parameter vector is
To test the hypothesis that the pooled A linear and A quadratic
effect is zero, you can use the following L matrix:
The corresponding CONTRAST statement is
contrast 'A LINEAR & QUADRATIC'
a 2 1 0 1 2,
a 2 1 2 1 2;
If the first level of A is a control level and you want a
test of control versus others, you can use this statement:
contrast 'CONTROL VS OTHERS' a 1 0.25 0.25 0.25 0.25;
See the following discussion of the ESTIMATE statement and
the "Specification of ESTIMATE Expressions" section for rules on specification, construction,
distribution, and estimability in the CONTRAST statement.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.