Sorry, Off topic. This list deals with R programming questions, not statistical questions. Try stats.stackexchange.com for those.
Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, May 27, 2020 at 1:25 AM Rahul Chakraborty <chakrara...@gmail.com> wrote: > Dear all, > > Presently I am working on designing a questionnaire for my discrete choice > experiment. I want to generate an orthogonal fractional factorial design > for the following problem- > > The respondent has to choose one out of 4 objects (*X1, X2, X3, X4*). Each > of the 4 objects are classified by 10 different attributes. However, the > levels are not the same under each of the objects. The table below displays > the situation. > > Attributes No. of Levels Choices and values > X1 X2 X3 X4 > A 5 1 1,2,3 3,4,5 3,4,5 > B 4 1 1 1,2 3,4 > C 4 1 1 2,4 3,4 > D 5 1 1,2,3 1,2,3 1,4,5 > E 5 1,2 2,3 3,4 5 > F 2 1 1 1,2 1,2 > G 2 1 1 1,2 2 > H 2 1 1 1,2 1,2 > I 4 1 2,3,4 2,3,4 2,3,4 > J 3 1 2,3 2,3 2,3 > *X* 4 1 2 3 4 > > The last row denotes the 4 objects. > > Now I want to generate the choice sets for my questionnaire. I would like > to use *orthogonal fractional factorial design*. I kept the row with *X* in > order to sort out the redundant combinations from the choice sets. > > I have the following questions- > 1. *How to decide on the number of runs that one has to chose for > fractional factorial design?* I used *AlgDesign* to generate the full > factorial which consists of 0.768 million combinations. So, I need a modest > number of runs, but how much should I target? I do not see any document > where one explains how to choose the number of trials/experimental runs. > The papers I am following only tell that they have used N number of runs > instead of the full factorial. > > 2. Out of 0.768 million combinations in the full factorial, there will be > many which are redundant. For example- I don't want those rows where (X=X1) > and A=(2 or 3 or 4 or 5). There are many other such cases which I don't > want in my design. I have coded all levels for each attribute and that's > why they are in the full factorial. *How do I generate an orthogonal > fractional factorial so that it does not contain such redundant > combinations?* I included the X attribute with the purpose of dropping > those combinations conditioned upon specific values of X and other factors. > Should I execute that and then generate the fractional factorial using > *optFederov* from the remaining data in the dataframe? > > I would be highly obliged if you can kindly help me in this regard. I am a > student of Economics, so I do not have very deep understanding of the > statistical procedure of such algorithms. So, my question might sound > extremely naive for which I am sorry. > > > -- Regards, > Rahul Chakraborty > Research Fellow > National Institute of Public Finance and Policy > New Delhi- 110067 > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
