How to create a nearly orthogonal experimental design in R? Does anyone know an R package for nearly orthogonal designs?
I would like to create an experimental design, using 12 runs, up to 10 factors, and with mixed levels (e.g. a combination of 2 and 3 level factors). I would like to explore some nearly-orthogonal designs.
There are lots of packages for orthogonal fractional designs. For example, I have looked at the documentation for AlgDesign, planor, FrF2, support.CEs, DoE.Base.
In SAS, there exist a set of macros for creating orthogonal and nearly orthogonal fractional factorial experimental designs: http://support.sas.com/techsup/technote/ts723.html
Does anyone know if something similar exists in R?  The CRAN task view does not mention nearly orthogonal designs.  http://cran.r-project.org/web/views/ExperimentalDesign.html
Many thanks 
Tim
Update: The Federov algorithm implemented in AlgDesign optFederov lets you create nearly orthogonal designs for mixed factors, as shown in the documentation
 A: Update: The Federov algorithm implemented in AlgDesign optFederov lets you create nearly orthogonal designs for mixed factors, as shown in the documentation
The example in the post below is a non-orthogonal fractional factorial design
https://stackoverflow.com/questions/5044876/how-to-create-a-fractional-factorial-design-in-r
A: by going to suggested link i created below code and though it might be useful.
###Define factor and level in below code
cand.list = expand.grid(Storage = c("8 GB", "16 GB"),
                        Brand = c("Samsung", "Apple", "Nokia"),
                        RAM = c("1 GB", "2 GB"),
                        BrowseTime = c("24 hour", "36 hour"),
                        Weight = c("3.95 oz OR 111 gram", "5.04 oz OR 142gram"),
                        ScreenSize = c("4.7", "5.5", "5.7"))

###same as SPSS orthogonal design 'seed'. Can put any number. Does not matter.
set.seed(69)

###Generate 16 alternatives in an optimal orthogonal design
optFederov( ~ ., data = cand.list, nTrials = 16)

###End of code

I have a question for you all though. I got below values for design efficiency
D =0.2519353; A = 5.462121; Ge = 0.748; Dea = 0.714. Which value should we be looking at for D-Eficiency? I assume it's D and how much it should be in order for this design to be usable in a choice experiment as alternatives? is current D value of 0.2519353 good enough for use?
