From: Ulrike Grömping <groemping_at_tfh-berlin.de>

Date: Tue 24 Jan 2006 - 08:22:42 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Jan 24 08:30:06 2006

Date: Tue 24 Jan 2006 - 08:22:42 EST

I think that there is an understandable wish to have the simple orthogonal plans (and be it only for non-experts to be able to analyse the results themselves). For mixed levels, there is e.g. the L36 that should be able to accomodate plans like 2x2x2x3x3x3. Unfortunately, R is not very strong in this arena.

If I had more time, I would think about writing a package on comfortably designing experiments supported e.g. by the catalogues of Chen, J., Sun, D.X., and Wu, C.F.J. (1993). (A catalogue of two-level and three-level fractional factorial designs with small runs. International Statistical Review 61, 131-145.) Such a package should also provide the analysis facilities for any design generated with it, once it has been enriched with observed data. (This is a bit different from the typical R spirit, where users are often required to be experts themselves.) If anyone is planning a project like this or wants to make a diploma student work on it I would be interested in contributing.

For the moment, if you want to implement main effects plans of the orthogonal sort (e.g. a Taguchi-plan like the L36) you have to use books or tables published on the internet, if you don't want to use expensive software like SPSS - not very comfortable, but possible. For example, you can find the L36 - which would be able to accomodate your 2x2x2x3x3x3 - in http://www.itl.nist.gov/div898/handbook/pri/section3/pri33a.htm.

With kind regards,

Ulrike

>In general, a "main effects design" need not be orthogonal -- the main

*>effects merely need to be estimable. The trick is to estimate them with good
**>efficiency, etc. I think you need to consult a local statistician for help
**>to understand what these statistical concepts mean.
**>
**>In your example you could cross the 2^(3-1) with the 3^(3-1) to produce an
**>orthogonal design to estimate main effects. But of course that's 72 runs,
**>which I don't think you would consider "small." As a previous poster
**>commented, there are orthogonal mixed level arrays ("Addleman", "Kempthorne"
**>"Youden" -designs are a couple of phrases to try googling on) which stem
**>from the 1960's. I doubt that, in general, they would satisfy your needs.
**>
**>I have not used the AlgDesign package myself. I suggest you direct questions
**>about it to the author/maintainer, Bob Wheeler.
**>
**>-- Bert Gunter
**>Genentech Non-Clinical Statistics
**>South San Francisco, CA
**>
**>"The business of the statistician is to catalyze the scientific learning
**>process." - George E. P. Box
**>
*

> -----Original Message-----

*> From: r-help-bounces at stat.math.ethz.ch
**> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of
**> statistical.model at googlemail.com
**> Sent: Monday, January 23, 2006 12:20 PM
**> To: Berton Gunter; statistical.model at googlemail.com;
**> r-help at stat.math.ethz.ch
**> Subject: [R] R: fractional factorial design in R
**>
**> > Yes, you're right. For, say, a 3 x 5 design, one can do
**> this in as few as
**> 7
**> runs -- but only in general by some version of
**> one-factor-at-a-time (OFAT)
**> designs, which are inefficient. It is easy, via, say
**> model.matrix() to
**> write a general function to produce these. But I think it's a
**> bad idea; more
**> efiicient algorithmic designs are better, IMO, which is why I
**> suggested
**> AlgDesign. You and others are free to disagree, of course.
**>
**> Hi Bert,
**> thanks for your suggestion.
**> However, let us say that i need a 2x2x2x3x3x3 design, which
**> should not be
**> too hard.
**> I've loaded AlgDesign, and i am aware now that gen.factorial
**> allows me to
**> create a full desing. But how to create a main-effects-only
**> factorial design
**> (orthogonal)?
**> I am still not able to produce what i need. The function
**> model.matrix.formula is not very clear... :(
**>
**> Could you please indicate which syntax should i use? I'd
**> really appreciate
**> your help.
**>
**> Thanks in advance,
**>
**> Roberto Furlan
**> University of Turin, Italy
**>
**>
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