Dear JvB,

I hope you will get this message Reading this thread it seems we have similar issues on how to include SQ into an efficient design, where few attributes are nominal variables (effects coded), however I am only at early stages of the research (field of forestry). First, I would appreciate very much if you could tell me how the design below worked? Me and my colleague are trying to create a design with six attributes where four are continuous variables and two are nominal:

ATR levels codes

atr1 7, 20, 34 1,2,3 (7 is a reference value)

atr2 0%, 5%, 15% 1,2,3 (0% is a reference value)

atr3 none, owner, other 0,1,2 (effects coded, 0 is a reference value)

atr4 .3%, 5%, 20% 1,2,3 (.3 is a reference value)

atr5 none, record, monitoring 0,1,2 (effects coded, 0 is a reference value)

atr6 0, 150, 300, 450, 600, 750, 900 EUR 1,2,3,4,5,6,7 (0 is a reference value)

Reference values are in SQ alternative, however reference values of all except of atr6 can also populate additional two non-SQ alternatives. This is where I see most resemblance between your and our case. (I hope I understood your research design properly).

We are planning to do a pilot study (based on sequential fractional factorial design), from where priors would hopefully be collected. Those are to be fed into a Bayesian effective design. The code we are constructing builds also on your correspondence with Michiel B. on this forum.

In addition to see how your case worked out I am especially concerned on how to implement nominal attributes in SQ alternative, as there will be no prior parameter estimate for the reference values. It is possible to estimate (n-1) parameters only for non-reference attribute levels. If I understand correctly you dealt this with adding the 'require' restriction. Am I right? How can Ngene calculate choice probabilities for SQ alternative as you do not have priors for reference values of dummy coded attributes?

I hope my question make sense ... and thank you very much for your reply.

Anže