Summary of Designs

Details of the FACTEX Procedure

Summary of Designs

Table 15.5 summarizes basic design types that you can construct with the FACTEX procedure by providing example code for each type.

Table 15.5: Basic Designs Constructed by the FACTEX Procedure

Design Type Example Statements

A full factorial design in three factors, each at two levels coded as -1 and +1. proc factex;
factors pressure temp time ;
examine design;
run;

A full factorial design in three factors, each at three levels coded as -1, 0, and +1. proc factex;
factors pressure temp time /nlev= 3 ;
examine design;
run;

A full factorial design in three factors, each at two levels. The entire design is replicated twice, and the design with recoded factor levels is saved in a SAS data set. proc factex;
factors pressure temp time ;
output out= savdesgn designrep= 2
pressure cvals=( 'low' 'high' )
temp nvals=( 200 300 )
time nvals=( 10 20 );
run;

A full factorial design in three factors, each at two levels coded as -1 and +1. Each run in the design is replicated three times, and the replicated design is randomized and saved in a SAS data set. proc factex;
factors pressure temp time ;
output out= savdesgn
pointrep=3 randomize;
run;

A full factorial design in three control factors, each at two levels coded as -1 and +1. A noise factor design (outer array) read from a SAS data set is replicated for each run in the control factor design (inner array), and the product design is saved in a SAS data set. proc factex;
factors pressure temp time ;
output out = savdesgn
pointrep= outarray ;
run;

A full factorial blocked design in three factors, each at two levels coded as -1 and +1. The design is arranged in two blocks and saved in a SAS data set. By default, the block variable is named BLOCK and the two block levels are numbered 1 and 2. proc factex;
factors pressure temp time ;
blocks nblocks= 2 ;
output out= savdesgn ;
run;

A full factorial blocked design in three factors, each at two levels coded as -1 and +1. Each block contains four runs; the block variable is renamed and the block levels of character type are recoded. The design is saved in a SAS data set. proc factex;
factors pressure temp time ;
blocks size= 4 ;
output out= savdesgn
blockname= machine cvals=( 'A' 'B' );
run;

A fractional factorial design of resolution 4 in four factors, each at two levels coded as -1 and +1. The size of the design is eight runs. proc factex;
factors pressure temp time catalyst ;
size design= 8 ;
model resolution= 4 ;
examine design;
run;

A one-half fraction of a factorial design in four factors, each at two levels coded as -1 and +1. The design is of maximum resolution. The design points, the alias structure, and the confounding rules are listed. proc factex;
factors pressure temp time catalyst ;
size fraction= 2 ;
model resolution=maximum;
examine design aliasing confounding;
run;

A one-quarter fraction of a factorial design in six factors, each at two levels coded as -1 and +1. Main effects are estimated, and some two-factor interactions are considered nonnegligible. The design is saved in a SAS data set. proc factex;
factors x1-x6 ;
size fraction= 4 ; r model estimate=( x1 x2 x3 x4 x5 x6 )
nonneg =( x1*x5 x1*x6 x5*x6 );
output out = savdesgn ;
run;

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Design Type	Example Statements
A full factorial design in three factors, each at two levels coded as -1 and +1.	`proc factex;` `factors` pressure temp time `;` `examine design;` `run;`

A full factorial design in three factors, each at three levels coded as -1, 0, and +1.	`proc factex;` `factors` pressure temp time `/nlev=` 3 `;` `examine design;` `run;`

A full factorial design in three factors, each at two levels. The entire design is replicated twice, and the design with recoded factor levels is saved in a SAS data set.	`proc factex;` `factors` pressure temp time `;` `output out=` savdesgn `designrep=` 2 pressure `cvals=(` 'low' 'high' `)` temp `nvals=(` 200 300 `)` time `nvals=(` 10 20 `);` `run;`

A full factorial design in three factors, each at two levels coded as -1 and +1. Each run in the design is replicated three times, and the replicated design is randomized and saved in a SAS data set.	`proc factex;` `factors` pressure temp time `;` `output out=` savdesgn `pointrep=`3 `randomize;` `run;`

A full factorial design in three control factors, each at two levels coded as -1 and +1. A noise factor design (outer array) read from a SAS data set is replicated for each run in the control factor design (inner array), and the product design is saved in a SAS data set.	`proc factex;` `factors` pressure temp time `;` `output out =` savdesgn `pointrep=` outarray `;` `run;`

A full factorial blocked design in three factors, each at two levels coded as -1 and +1. The design is arranged in two blocks and saved in a SAS data set. By default, the block variable is named BLOCK and the two block levels are numbered 1 and 2.	`proc factex;` `factors` pressure temp time `;` `blocks nblocks=` 2 `;` `output out=` savdesgn `;` `run;`

A full factorial blocked design in three factors, each at two levels coded as -1 and +1. Each block contains four runs; the block variable is renamed and the block levels of character type are recoded. The design is saved in a SAS data set.	`proc factex;` `factors` pressure temp time `;` `blocks size=` 4 `;` `output out=` savdesgn `blockname=` machine `cvals=(` 'A' 'B' `);` `run;`

A fractional factorial design of resolution 4 in four factors, each at two levels coded as -1 and +1. The size of the design is eight runs.	`proc factex;` `factors` pressure temp time catalyst `;` `size design=` 8 `;` `model resolution=` 4 `;` `examine design;` `run;`

A one-half fraction of a factorial design in four factors, each at two levels coded as -1 and +1. The design is of maximum resolution. The design points, the alias structure, and the confounding rules are listed.	`proc factex;` `factors` pressure temp time catalyst `;` `size fraction=` 2 `;` `model resolution=maximum;` `examine design aliasing confounding;` `run;`

A one-quarter fraction of a factorial design in six factors, each at two levels coded as -1 and +1. Main effects are estimated, and some two-factor interactions are considered nonnegligible. The design is saved in a SAS data set.	`proc factex;` `factors` x1-x6 `;` `size fraction=` 4 `;` r `model estimate=(` x1 x2 x3 x4 x5 x6 `)` `nonneg =(` x1x5 x1x6 x5x6 `);`* `output out =` savdesgn `;` `run;`