The GLM Procedure

Syntax: GLM Procedure

The following statements are available in PROC GLM:

PROC GLM <options> ;: CLASS variables </ option> ;; MODEL dependent-variables=independent-effects </ options> ;; ABSORB variables ;; BY variables ;; FREQ variable ;; ID variables ;; WEIGHT variable ;; CONTRAST ’label’ effect values <...effect values> </ options> ;; ESTIMATE ’label’ effect values <...effect values> </ options> ;; LSMEANS effects </ options> ;; MANOVA <test-options></ detail-options> ;; MEANS effects </ options> ;; OUTPUT <OUT=SAS-data-set> keyword=names <...keyword=names> </ option> ;; RANDOM effects </ options> ;; REPEATED factor-specification </ options> ;; STORE <OUT=>item-store-name </ LABEL='label'> ;; TEST <H=effects> E=effect </ options> ;

Although there are numerous statements and options available in PROC GLM, many applications use only a few of them. Often you can find the features you need by looking at an example or by quickly scanning through this section.

To use PROC GLM, the PROC GLM and MODEL statements are required. You can specify only one MODEL statement (in contrast to the REG procedure, for example, which allows several MODEL statements in the same PROC REG run). If your model contains classification effects, the classification variables must be listed in a CLASS statement, and the CLASS statement must appear before the MODEL statement. In addition, if you use a CONTRAST statement in combination with a MANOVA, RANDOM, REPEATED, or TEST statement, the CONTRAST statement must be entered first in order for the contrast to be included in the MANOVA, RANDOM, REPEATED, or TEST analysis.

Table 41.2 summarizes the positional requirements for the statements in the GLM procedure.

Table 41.2 Positional Requirements for PROC GLM Statements
Statement	Must Precede...	Must Follow...
ABSORB	First RUN statement
BY	First RUN statement
CLASS	MODEL statement
CONTRAST	MANOVA, REPEATED,	MODEL statement
	or RANDOM statement
ESTIMATE		MODEL statement
FREQ	First RUN statement
ID	First RUN statement
LSMEANS		MODEL statement
MANOVA		CONTRAST or
		MODEL statement
MEANS		MODEL statement
MODEL	CONTRAST, ESTIMATE,	CLASS statement
	LSMEANS, or MEANS
	statement
OUTPUT		MODEL statement
RANDOM		CONTRAST or
		MODEL statement
REPEATED		CONTRAST, MODEL,
		or TEST statement
TEST	MANOVA or	MODEL statement
	REPEATED statement
WEIGHT	First RUN statement

Table 41.3 summarizes the function of each statement (other than the PROC statement) in the GLM procedure.

Table 41.3 Statements in the GLM Procedure
Statement	Description
ABSORB	Absorbs classification effects in a model
BY	Specifies variables to define subgroups for the analysis
CLASS	Declares classification variables
CONTRAST	Constructs and tests linear functions of the parameters
ESTIMATE	Estimates linear functions of the parameters
FREQ	Specifies a frequency variable
ID	Identifies observations on output
LSMEANS	Computes least squares (marginal) means
MANOVA	Performs a multivariate analysis of variance
MEANS	Computes and optionally compares arithmetic means
MODEL	Defines the model to be fit
OUTPUT	Requests an output data set containing diagnostics for each observation
RANDOM	Declares certain effects to be random and computes expected mean squares
REPEATED	Performs multivariate and univariate repeated measures analysis of variance
STORE	Requests that the procedure save the context and results of the statistical analysis into an item store
TEST	Constructs tests that use the sums of squares for effects and the error term you specify
WEIGHT	Specifies a variable for weighting observations

The rest of this section provides detailed syntax information for each of these statements, beginning with the PROC GLM statement. The remaining statements are covered in alphabetical order.

The STORE statement is also used by many other procedures. A summary description of functionality and syntax for the STORE statement is also shown after the PROC GLM statement in alphabetical order, but you can find full documentation about it in the section STORE Statement of Chapter 19, Shared Concepts and Topics.