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Introduction to the OPTEX Procedure |

- use a variety of SAS software tools to generate an appropriate set of candidate runs
- use the OPTEX procedure to search the candidate set for an optimal design

proc factex; factors x1-x7; output out=can1; proc optex data=can1; model x1|x2|x3|x4|x5|x6|x7@@2; generate n=saturated; output out=design1a; run;The default search procedure quickly finds a design with a D-efficiency of 82.3%. If search time is not an issue, you can try a more powerful search technique. For example, you can specify 500 tries with the modified Fedorov method.

proc optex data=can1; model x1|x2|x3|x4|x5|x6|x7@@2; generate n=saturated method=m_fedorov iter=500; output out=design1b; run;This takes more than ten times longer to run, and the resulting design is only slightly more D-efficient.

proc factex; factors x1-x7; output out=can2; run; model resolution=4; size design=min; output out=aug2; run;Now specify AUG2 (the data set containing the design to be augmented) with the AUGMENT= option in the GENERATE statement.

proc optex data=can2; model x1|x2|x3|x4|x5|x6|x7@@; generate n=30 augment=aug2; output out=design2; run;

proc factex; factors x1-x15 / nlev=3; model resolution=3; size design=81; output out=can3; proc optex data=can3; class x1-x15; model x1-x15; generate n=saturated; output out=design3; run;

data can4; do treatmt = 1 to 7; output; end; proc optex data=can4; class treatmt; model treatmt; blocks structure=(7)3; run;The resulting design is balanced in the sense that each treatment occurs the same number number of times and each pair of treatments occur together in the same number of blocks. Balanced designs, when they exist, are known to be optimal, and the OPTEX procedure usually succeeds at finding them for small- to moderately-sized problems.

%adxgen; %adxmix; %adxinit; %adxxvert(xvt,x1 0-.5/x2/x3 proc factex; factors a / nlev=5; output out=can5 pointrep=xvt; run;Analyzing mixture designs with linear models can be problematic because of the constraint that the mixture factors sum to one; however, to generate an optimal design, you can simply drop one of the mixture factors. The following statements use the preceding candidate set to find an optimal design for fitting the main effect of A and a second-order model in the mixture factors:

proc optex data=can5; class a; model a x1|x2 x1*x1 x2*x2; run;See Example 24.10 for a more detailed example of a mixture experiment.

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