
I attempted to use the package algdesign. I used the following code. However, the results were very much not matching the reference I noted (which is located at http://www2.sas.com/proceedings/sugi31/196-31.pdf). Instead of 30 design points, I received 25 and those that were returned, only half or so matched the reference. I am inexperienced with optimal designs so I dont know if I am doing something wrong, the package is not the correct one for the task or a combination. Here is the code in case anyone has any input:

#CODE:

runif(1) #for a bug in program (assumes random seed object exists)

dat<-gen.factorial(c(3,3,3,3,2),varNames=c("Intro","Duration","GOTO","Fee","Color"))
dat #show design plan

desD<-optFederov(~Intro+Duration+GOTO+Fee+Color+quad(Intro)+quad(Duration)+quad(GOTO)+quad(Fee)+Intro*Duration+Intro*GOTO+Intro*Fee+Intro*Color+Duration*GOTO+Duration*Fee+Duration*Color+GOTO*Fee+GOTO*Color+Fee*Color,dat,crit="D",maxIteration=1000,eval=TRUE)

#D
desD$D

#design
desD$design
design<-desD$design




> Subject: Re: [R]  DOE in R?
> From: cgb@datanalytics.com
> To: b_miner@live.com
> CC: r-help@r-project.org
> Date: Sun, 6 Sep 2009 14:57:36 +0200
> 
> Hello,
> 
> This is your starting point:
> 
> http://cran.r-project.org/web/views/ExperimentalDesign.html
> 
> Best regards,
> 
> Carlos J. Gil Bellosta
> http://www.datanalytics.com
> 
> 
> On Thu, 2009-09-03 at 17:38 -0700, B_miner wrote:
> > Hello!
> > 
> > 
> > This is not a topic I am well versed in but required to become well versed
> > in...I welcome any assistance!
> > 
> > Using R, I want to create an optimal design for an experiment. I'll be
> > analyzing the results with logistic regression or some generalized linear
> > model. I am thinking that the algdesign package can help (but no idea where
> > to start?).
> > 
> > I'm presenting an example here that I have seen the answer to (in SAS) in
> > order to make sure I would have gotten it *right*.
> > 
> > There are 5 factors: 4 are quantitative with three levels each and 1 is
> > qualitative with two levels.
> > 
> > Factor and levels:
> > 
> > Intro: 0, 1.99, 2.99
> > Duration: 6, 9 ,12
> > GOTO: 3.99, 4.99, 5.99
> > Fee: 0, 15, 45
> > Color: Red, White
> > 
> > 
> > In order to screen these factors, I would want to get a design where I could
> > evaluate all main effects, all first order interactions and the squared
> > terms of Intro, Duration, GOTO and FEE (for example Intro*Intro).
> > 
> > Looking for the D-optimal design.
> > 
> > Is this something that  R can provide?
> > 
> > 
> > These are, according to the SAS paper I read the following:
> > Obs intro duration goto fee color
> > 1 0.00 6 3.99 0 WHITE
> > 2 0.00 6 3.99 45 RED
> > 3 0.00 6 5.99 0 RED
> > 4 0.00 6 5.99 45 WHITE
> > 5 0.00 9 3.99 45 RED
> > 6 0.00 9 4.99 15 WHITE
> > 7 0.00 9 5.99 0 RED
> > 8 0.00 12 3.99 0 RED
> > 9 0.00 12 3.99 45 WHITE
> > 10 0.00 12 5.99 0 WHITE
> > 11 0.00 12 5.99 45 RED
> > 12 0.00 12 5.99 45 WHITE
> > 13 1.99 6 3.99 15 RED
> > 14 1.99 6 4.99 45 WHITE
> > 15 1.99 6 5.99 0 WHITE
> > 16 1.99 9 5.99 45 RED
> > 17 1.99 12 3.99 0 WHITE
> > 18 1.99 12 5.99 15 RED
> > 19 2.99 6 3.99 0 WHITE
> > 20 2.99 6 3.99 45 WHITE
> > 21 2.99 6 4.99 0 RED
> > 22 2.99 6 5.99 15 WHITE
> > 23 2.99 6 5.99 45 RED
> > 24 2.99 9 3.99 0 RED
> > 25 2.99 12 3.99 15 WHITE
> > 26 2.99 12 3.99 45 RED
> > 27 2.99 12 4.99 0 WHITE
> > 28 2.99 12 4.99 45 RED
> > 29 2.99 12 5.99 0 RED
> > 30 2.99 12 5.99 45 WHITE
> > 
> > 
> 
> 
> 
> 

_________________________________________________________________
Windows Live: Make it easier for your friends to see what you’re up to on Facebook.
http://windowslive.com/Campaign/SocialNetworking?ocid=PID23285::T:WLMTAGL:ON:WL:en-US:SI_SB_facebook:082009
	[[alternative HTML version deleted]]


