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 |

qualityTools documentation built on May 19, 2017, 11:44 p.m.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs in the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.