It is possible to use covariate factors with custom design, but not with space filling design.
In some case, it is preferable to screen homogeneously the design space.
I tried to make a model with cubic, quartic terms to force the optimization to spread selected rows over the whole design space but the result could be better.
I also tried to use hierachical clustering using centroid criterion, then I select the closest row to the centroid of each cluster. This worked better but this is not user friendly.
Ideally, I would have enjoyed to select covariate factors, directly in the space filling plateform.
Do you have any suggestion?
I have used the test data 'prostate cancer.jmp' to illustrate my attempts. For clarity, I reduced the dimensionnality using 2D multidimensionnal scaling. The color represent 20 clusters to select the best rows. The squared markers represent the 20 rows selected by the Custom DoE plateform.
Best regards,
Florent