When some model effects are random (that is, assumed to be sampled
from a normal population of effects), you can specify these effects in
the RANDOM statement in order to compute the expected values of mean
squares for various model effects and contrasts and, optionally, to
perform random effects analysis of variance tests. You can use as
many RANDOM statements as you want, provided that they appear after
the MODEL statement. If you use a CONTRAST statement with a RANDOM
statement and you want to obtain the expected mean squares for the
contrast hypothesis, you must enter the CONTRAST statement before the
- RANDOM effects < / options >
PROC GLM uses only the information pertaining to expected mean squares
when you specify the TEST option in the RANDOM statement
and, even then, only in the extra F tests produced by the RANDOM
statement. Other features in the GLM procedure -including
the results of the LSMEANS and ESTIMATE statements -assume
that all effects are fixed, so that all tests and estimability checks
for these statements are based on a fixed effects model, even when you
use a RANDOM statement. Therefore, you should use the MIXED procedure
to compute tests involving these features that take the random
account; see the section "PROC GLM versus PROC MIXED for Random Effects Analysis" and Chapter 41, "The MIXED Procedure," for more information.
When you use the RANDOM statement, by default the GLM procedure
produces the Type III expected mean squares for model effects and for
contrasts specified before the RANDOM statement in the program code.
In order to obtain expected values for other types of mean squares,
you need to specify which types of mean squares are of interest in the
See the section "Computing Type I, II, and IV Expected Mean Squares" for more information.
The list of effects in the RANDOM statement should contain one or more
of the pure classification effects specified in the MODEL statement
(that is, main effects, crossed effects, or nested effects involving
only class variables). The coefficients corresponding to each effect
specified are assumed to be normally and independently distributed
with common variance. Levels in different effects are assumed to be
You can specify the following options in the RANDOM statement after a slash:
displays all quadratic forms in the fixed
effects that appear in the expected mean squares. For some designs,
large mixed-level factorials, for example, the Q
option may generate a substantial amount of output.
performs hypothesis tests for each effect specified in the model,
using appropriate error terms as determined by the expected mean
Caution: PROC GLM does not automatically declare interactions to be random when
the effects in the interaction are declared random. For example,
random a b / test;
does not produce the same expected mean squares or tests as
random a b a*b / test;
To ensure correct tests, you need to list all random interactions and random main
effects in the RANDOM statement.
See the section "Random Effects Analysis" for more
information on the calculation of expected mean squares and tests and
on the similarities and differences between the GLM and MIXED
procedures. See Chapter 4, "Introduction to Analysis-of-Variance Procedures," and Chapter 41, "The MIXED Procedure,"
for more information on random effects.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.