TEST Statement
 TEST < H=effects >
E=effect < / options >
;
Although an F value is computed for all sums of squares in the analysis
using the residual MS as an error term, you may request
additional F tests using other effects as error terms.
You need a TEST statement when a nonstandard
error structure (as in a splitplot design) exists.
Note, however, that this may not be appropriate if the design is
unbalanced, since in most unbalanced designs with nonstandard
error structures, mean squares are not necessarily independent with
equal expectations under the null hypothesis.
Caution: The GLM procedure does not check any of the
assumptions underlying the F statistic.
When you specify a TEST statement, you
assume sole responsibility for the validity
of the F statistic produced.
To help validate a test, you can use the RANDOM
statement and inspect the expected mean squares, or
you can use the TEST option of the RANDOM statement.
You may use as many TEST statements as you want,
provided that they appear after the MODEL statement.
You can specify the following terms in the TEST statement.
 H=effects

specifies which effects in the preceding model
are to be used as hypothesis (numerator) effects.
 E=effect

specifies one, and only one, effect to
use as the error (denominator) term.
The E= specification is required.
By default, the sum of squares type for all hypothesis sum of squares and
error sum of squares is the highest type computed in the model.
If the hypothesis type or error type is to be another
type that was computed in the model, you should
specify one or both of the following options after a slash.
 ETYPE=n

specifies the type of sum of squares to use for the error term.
The type must be a type computed
in the model (n=1, 2, 3, or 4 ).
 HTYPE=n

specifies the type of sum of squares to use for the hypothesis.
The type must be a type computed
in the model (n=1, 2, 3, or 4).
This example illustrates the TEST
statement with a splitplot model:
proc glm;
class a b c;
model y=a b(a) c a*c b*c(a);
test h=a e=b(a)/ htype=1 etype=1;
test h=c a*c e=b*c(a) / htype=1 etype=1;
run;
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.