REPEATED Statement
 REPEATED factordescription < ,... ,
factordescription >< / options > ;
where a factordescription is
 factorname < $ >< levels >
and factordescriptions are separated from each other
by a comma. The $ is required for charactervalued
factors. The value of levels provides the number of
levels of the repeated measurement factor identified by a
given factorname. For only one repeated measurement
factor, levels is optional; for two or more repeated
measurement factors, it is required.
The REPEATED statement incorporates repeated
measurement factors into the model. You can use this statement whenever
there is more than one dependent variable and the keyword
_RESPONSE_ is specified in the MODEL statement. If the
dependent variables correspond to one or more repeated
measurement factors, you can use the REPEATED statement to
define _RESPONSE_ in terms of those factors. You can
specify the name, type, and number of levels of each factor,
as well as the identification of each level.
You cannot specify the REPEATED statement for an analysis
that also contains the FACTORS or LOGLIN statement since all
of them specify the same information: how to partition the
variation among the response functions within a population.
 factorname

names a repeated measurement factor
that corresponds to two or more response functions.
This name must be a valid SAS variable name, and it
should not be the same as the name of a variable
that already exists in the data set being analyzed.
 $
 indicates that the factor is charactervalued.
If the $ is omitted, then PROC CATMOD
assumes that the factor is numeric.
The type of the factor is relevant only when you use
the PROFILE= option or when the _RESPONSE_=
option specifies nestedbyvalue effects.
 levels
 specifies the number of levels of the
corresponding repeated measurement factor.
If there is only one such factor and the number is omitted,
then PROC CATMOD assumes that the number of levels is equal
to the number of response functions per population (q).
Unless you specify the PROFILE= option, the number
q must either be equal to or be a multiple of
the product of the number of levels of all the factors.
You can specify the following options in the REPEATED
statement after a slash.
 PROFILE=(matrix)

specifies the values assumed by the
factors for each response function.
There should be one column for each factor, and
the values in a given column should match the type
(character or numeric) of the corresponding factor.
Character values are restricted to 16 characters or less.
If there are q response functions per population,
then the matrix must have i rows, where q must
either be equal to or be a multiple of i.
Adjacent rows of the matrix should be separated by a comma.
The values in the PROFILE matrix are useful for specifying
models in those situations where the study design is not a
full factorial with respect to the factors. They can also
be used to specify nestedwithvalue effects in the
_RESPONSE_= option. If you specify character values in
both the PROFILE= option and the _RESPONSE_= option, then
the values must match with respect to whether or not they
are enclosed in quotes (that is, enclosed in quotes in both
places or in neither place).
 _RESPONSE_=effects

specifies design effects.
The variables named in the effects must be
factornames that appear in the REPEATED statement.
If the _RESPONSE_= option is omitted, then PROC CATMOD builds a full
factorial _RESPONSE_ effect with respect
to the repeated measurement factors.
For example, the following two statements are equivalent
in that they produce the same parameter estimates.
repeated Time 2, Treatment 2;
repeated Time 2, Treatment 2 / _response_=TimeTreatment;
However, the second statement produces tests of the
Time, Treatment, and Time*Treatment
effects in the "Analysis of Variance" table, whereas the first
statement produces a single test for the combined effects in
_RESPONSE_.
 TITLE='title'

displays the title at the top of certain pages
of output that correspond to this REPEATED statement.
For further information and numerous examples of the
REPEATED statement, see the section "Repeated Measures Analysis".
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.