facDesign

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Description

Generates a 2^k full factorial design.

Usage

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facDesign(k = 3, p = 0, replicates = 1, blocks = 1, centerCube = 0)

Arguments

k

numeric value giving the number of factors. By default k is set to ‘3’.

p

numeric integer between ‘0’ and ‘7’. p is giving the number of additional factors in the response surface design by aliasing effects.
For further information see fracDesign and fracChoose.
By default p is set to ‘0’.

replicates

numeric value giving the number of replicates per factor combination. By default replicates is set to ‘1’.

blocks

numeric value giving the number of blocks. By default blocks is set to ‘1’. Blocking is only performed for k greater 2.

centerCube

numeric value giving the number of centerpoints within the 2^k design. By default centerCube is set to ‘0’.

Details

facDesign generates 2^k full factorial designs.

Value

The function facDesign returns an object of class facDesign.

Note

For an example in context which shows the usage of the function facDesign 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

fracDesign
fracChoose
rsmDesign
pbDesign
taguchiDesign
http://www.r-qualitytools.org/html/Improve.html

Examples

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#returns a 2^3 full factorial design
vp.full = facDesign(k = 3)        
#generate some random response                        
response(vp.full) = rnorm(2^3) 
#summary of the full factorial design (especially no defining relation)                            
summary(vp.full)                                           

#------------

#returns a full factorial design with 3 replications per factor combination 
#and 4 center points
vp.rep = facDesign(k = 2, replicates = 3, centerCube = 4)  
#set names
names(vp.rep) = c("Name 1", "Name 2") 
#set units                     
units(vp.rep) = c("min", "F")         
#set low and high factor values                     
lows(vp.rep) = c(20, 40, 60)                               
highs(vp.rep) = c(40, 60, 80)  
#summary of the replicated full factorial Design        
summary(vp.rep)                                                

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