| Details of the OPTEX Procedure |
BLOCKS Statement
- BLOCKS block-specification < options > ;
-
You use the BLOCKS statement to find a D-optimal design in the presence
of fixed covariates (for example, blocks) or covariance. The technique
is an extension of the optimal blocking technique of Cook and Nachtsheim
(1989); see "Optimal Blocking"
.
For the purposes of optimal blocking, the model for the original candidate
points is referred to as the treatment model; the candidate points for
the part of the design matrix corresponding to the treatment model form the
treatment set.
If the GENERATE statement is not specified, then
the full candidate set is used as the treatment set; otherwise, an
optimal design for the treatment model ignoring the blocks is first
generated, and the result is used as the treatment set for optimal
blocking.
The following are three mutually exclusive block-specifications
that you can provide:
- COVAR=SAS-data-set VAR=( variables )
- specifies a data set to use in providing a general covariance matrix
for the runs. The argument to VAR= names the variables in this
data set that contain the columns of the covariance matrix for the
runs. For an example, see Example 24.9.
- DESIGN=SAS-data-set
- specifies a data set to use in providing a general covariate model.
In addition to this data set, you must specify a covariate model
with the CLASS and MODEL statements.
Covariate models are specified in the same way as the treatment model;
CLASS and MODEL statements that come after a BLOCKS statement involving
the DESIGN= specification are interpreted as applying to the covariate
model. For an example, see Example 24.8.
- STRUCTURE=(b) k
- specifies a block design with b blocks of size k. For an example,
see Example 24.7.
The following options can also be used:
- INIT=RANDOM
- specifies the initialization method for constructing the starting
design. The option INIT=RANDOM specifies that the starting design
is to be constructed by
selecting candidates at random without replacement. The option INIT=CHAIN
selects candidate points in the order in which they occur in the
original data set.
- ITER=n
- specifies the number of times to repeat the search from different
initial designs. Because local optima are common in difficult search
problems, it is often a good idea to make several tries for the optimal
design with a random or partially random method of initialization (see
the preceding INIT= option). By default, n=10. You can specify
ITER=0 to evaluate the initial design itself.
- KEEP=m
- specifies that only
the best m designs are to be retained. The value m must be less
than or equal to the value n of the ITER= option; by default m=n,
so that all iterations are kept. This option is useful when you want
to make many searches to overcome the problem of local optima but you
are only interested in the results of the best m designs.
- NOEXCHANGE
- suppresses the part of the optimal blocking algorithm that exchanges
treatment design points for candidate treatment points. When this
option is specified, only interchanges between design points are
performed. Use this option when you do not want to change which
treatment points are included in the design and you only want to find
their optimal ordering.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
