I am looking for more technical details (beyond what is stated in the JMP documentation) about how Variable Importance is calculated (available under the Prediction Profiler options).
I have read the two reference papers listed in the documentation (Sobol (2001) and Saltelli (2002)). I think I get the general idea, but I what I want to know is more of the gritty details on how the monte carlo simulation is done (just for independent uniform inputs)
- how is the monte carlo simulation setup and executed? From what I see in the papers, you do different kinds of random input combinations based on subsets of the input variables ? This is the part I am most confused about and that is least explained in the documentation.
- how many monte carlo runs are performed. It appears to be based on the size of the data set, but it is unclear and not described in the documentation.
- How are the Main Effect and Total Effect metrics for variable importance calculated from the simulation results.
This is more than just "wanting to know the math". What I need to do is to compare this approach to other variable importance/impact assessments, which is becoming more and more popular in pharma QbD approaches to determine process parameter criticality.
