Simulated Process

Description

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.

Usage

1
simProc(x1, x2, x3, noise = TRUE)

Arguments

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’.

Details

factor 1 is best within [40, 250]; factor 2 within [90, 240]

Value

simProc returns a numeric value within the range [0,1].

Note

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

Author(s)

Thomas Roth thomas.roth@tu-berlin.de

See Also

facDesign for 2^k factorial designs
rsmDesign for response surface designs
fracDesign for fractional factorial design
http://www.r-qualitytools.org/html/Improve.html

Examples

1
2
3
simProc(120, 140, 1)
simProc(120, 220, 1)
simProc(160, 140, 1)

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