Hi @zj2000,
Welcome in the Community !
You may have deleted the main effects from your model, but you'll be able to estimate them with your design (even if "less-optimally"): You have supposed interactions and quadratic effects for each of your 7 factors (so each factor has 3 levels, min-medium-max) in the model, and you have enough degree of freedoms to estimate them.
- There are 44 independent experiments in this design, representing 43 degree of freedoms (one is used to estimate the intercept). A model containing main effects (7 terms to estimate), 2-factors interactions (21 terms to estimate) and quadratic effects (7 terms to estimate) would require 35 degree of freedoms (as seen in the capture "Analysis of Variance" from "Fit Model" with random data for one response), so you have enough DF to estimate all these terms (and 8 to estimate model error) :
You can check the power to estimate main effects (and other effects of the model) by using the Evaluate Design platform (menu DoE, Design Diagnostics, Evaluate Design), specify your factors and responses, and add main effects in your supposed model. No error message is displayed, so it is possible to estimate main effects, and you'll obtain this window :
Even if you deleted the main effects from the model in the design generation, the principle of Effect Heredity ensures that if higher order terms are present in the design (like the quadratic terms or 2-factors interactions you have added in the model), lower order terms should be present and estimable in the model (like main effects). Note that the mistake you have realized would be a great suggestion to add in the JMP Wish List about the design creation: Provide a warning (or block ?) if a user specify a model with higher order effects without adding lower order effects.
Looking at the previous snapshot, you can see that main effects still have higher power than quadratic terms even if they were not added during design generation. Most of the main effects could have an higher power if added in the model from the design generation (for example T 2, 100, ...), but you should be able to have a correct analysis and model at the end.
You can compare the design you generated with a design with the same number of runs but with main effects added (see table attached "Custom Design-Process2" to do the designs comparison) to see where this change may impact the results and analysis (lower power for main effects, higher prediction variance at the edges of the experimental space, higher standard error for estimates, ...).
Hope this answer will help you (and appease you),
Victor GUILLER
"It is not unusual for a well-designed experiment to analyze itself" (Box, Hunter and Hunter)