Ffacdes1: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
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===Synopsis=== | ===Synopsis=== | ||
:desgn = ffacdes1(k,p) | :desgn = ffacdes1(k,''p'') | ||
===Description=== | ===Description=== | ||
FFACDES1 outputs a 2<sup>(k-p)</sup> fractional factorial design of experiments. The design is constructed such that the highest order interaction term is confounded. This is one way to select a fractional factorial. Input k is the total number of factors in the design and p is the number of confounded factors {default: p = 1}. Note that it is required that p < k. Output desgn is the experimental design matrix. | FFACDES1 outputs a 2<sup>(k-p)</sup> fractional factorial design of experiments. The design is constructed such that the highest order interaction term is confounded. This is one way to select a fractional factorial. Input <tt>k</tt> is the total number of factors in the design and <tt>p</tt> is the number of confounded factors {default: <tt>p</tt> = 1}. Note that it is required that <tt>p < k</tt>. Output <tt>desgn</tt> is the experimental design matrix. | ||
====Inputs==== | |||
* '''k''' = total number of factors in the design. | |||
====Optional Inputs==== | |||
* '''p''' = number of confounded factors (if omitted, <tt>p</tt>=1). | |||
====Outputs==== | |||
* '''desgn''' = experimental design matrix | |||
===See Also=== | ===See Also=== | ||
[[distslct]], [[doptimal]], [[factdes]], [[stdsslct]] | [[distslct]], [[doptimal]], [[factdes]], [[stdsslct]] | ||
Revision as of 10:30, 9 October 2008
Purpose
Output a fractional factorial design matrix.
Synopsis
- desgn = ffacdes1(k,p)
Description
FFACDES1 outputs a 2(k-p) fractional factorial design of experiments. The design is constructed such that the highest order interaction term is confounded. This is one way to select a fractional factorial. Input k is the total number of factors in the design and p is the number of confounded factors {default: p = 1}. Note that it is required that p < k. Output desgn is the experimental design matrix.
Inputs
- k = total number of factors in the design.
Optional Inputs
- p = number of confounded factors (if omitted, p=1).
Outputs
- desgn = experimental design matrix