simProc: Simulated Process
In qualityTools: Statistical Methods for Quality Science

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	simProc(x1, x2, x3, noise = TRUE)

`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

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simProc(120, 140, 1)
simProc(120, 220, 1)
simProc(160, 140, 1)

Loading required package: Rsolnp
Loading required package: MASS

Attaching package: 'qualityTools'

The following object is masked from 'package:stats':

    sigma

[1] 0.4812327
[1] -0.003401365
[1] 0.2728312