facDesign: facDesign
In qualityTools: Statistical Methods for Quality Science

Description Usage Arguments Details Value Note Author(s) See Also Examples

View source: R/COM_P.R

Generates a 2^k full factorial design.

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

`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’.

facDesign generates 2^k full factorial designs.

The function facDesign returns an object of class facDesign.

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.

Thomas Roth [email protected]

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

#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)