Dear JMP community,
currently we are setting up DoE models based on two experimental blocks.
The first block was a Custom DoE Design with 22 runs containing main effects and quadratic effects from 7 factors. The second block was an augmented design containing main effects, quadratic effects and all possible two factor interactions with 16 runs (for a total of 38 runs separated in 2 blocks).
In the second block, for two of the factors the factor levels were wider than in the first block.
Additionally, 7 two factor interactions that were rated less likely to occur were set to “if possible” in the model dialog. All other factors were kept at “necessary”.
Usually, we set up the models with the Fit Model Platform considering only the model effects coming from the design by clicking “run” (personality: standard least squares, emphasis: effect screening) and then manually removing the non-significant model effects from the models.
We encountered the following situtation: in some cases we get an error message saying “The model is missing an effect”.
Stepwise evaluation (personality: stepwise) via forward direction with either AICc or BIC both are yielding models with significant effects.
Our questions to you would be
- In which form is the “if possible” rating considered for the stepwise models? If we see an significant effect that was previously rated as “if possible”, that means the power of the design was good enough to elucidate it. But if we do not see an “if possible” effect, where from do we know that it was considered?
- What is the reason that the manual way is reporting issues whereas the stepwise ways are working?
- The BIC criterium is yielding much larger models than the AICc criterium, what would be the reason to trust the much smaller AICc models?
Many thanks in advance to all of you, best regards,
Claudia and Andreas
