Description Usage Arguments Details Value Note Author(s) See Also Examples
This is a function to simulate a black box process for teaching the use of designed experiments. The optimal factor settings can be found using a sequential assembly strategy i.e. apply a 2^k factorial design first, calculate the path of the steepest ascent, again apply a 2^k factorial design and augment a star portion to find the optimal factor settings. Of course other strategies are possible.
1 |
x1 |
numeric vector containing the values for factor 1. |
x2 |
numeric vector containing the values for factor 2. |
x3 |
numeric vector containing the values for factor 3. |
noise |
logical value deciding whether noise should be added or not. Default setting is ‘FALSE’. |
factor 1 is best within [40, 250]; factor 2 within [90, 240]
simProc
returns a numeric value within the range [0,1].
For an example in context which shows the usage of the function simProc()
please read the vignette for the package qualityTools
at http://www.r-qualitytools.org/html/Improve.html
Thomas Roth thomas.roth@tu-berlin.de
facDesign
for 2^k factorial designs
rsmDesign
for response surface designs
fracDesign
for fractional factorial design
http://www.r-qualitytools.org/html/Improve.html
1 2 3 |
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