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Developer Tutorial - Handling Covariates Effectively when Designing Experiments

Published on ‎11-07-2024 03:30 PM by Staff | Updated on ‎11-07-2024 05:40 PM

  • What is a Covariate?
    • Input variable(s) we want to account for, but don't have complete control.
    • Uncontrolled, but observable ahead of time.
    • "Candidate Set" approach.
  • Why does it matter to Custom Design?
    • Coordinate-Exchange (Custom Design)
      • Start with a "random" design.
      • Go through design element-by-element, make a switch if it improves criterion.
      • Repeat.
      • No requirement for a candidate set.
    • Row-Exchange
      • PROC OPTEX (SAS)
      • Start with a candidate set of possible runs.
      • Initial design is a subset of the candidate set.
      • Consider swapping rows not in the design, making the switch if it improves criterion.
  • Prior to JMP 16 (starting with JMP 10)
    • Row-exchange on the covariates (D-optimality).
    • Coordinate-exchange on the controllable factors.
    • Covariates were fixed from the beginning and we couldn't go back to see if there's something better.
  • JMP 16
    • Hybrid approach with row-exchange or coordinate-exchange as we go through the design.
    • Allows for different optimality criteria to be considered for the covariates.
    • More flexibility for your design creation (best of both worlds).
    • Much more visible to the user.
    • How to use
      • Does not require JMP Pro.
      • Need a data table with candidate set (covariates with our possible runs).
      • Add covariates in Custom Design using Add Factor->Covariate (JMP 10+) or the "Covariates/Candidate Set" outline (JMP 16+).
  • Two typical use cases
    • Using all rows
      • We have observable, uncontrollable factors for our experimental units that we want to account for.
      • We want to force a particular structure for a subset of inputs (that may even be controllable).
    • Choosing a subset of rows
      • Pick an optimal subset (NOT a random subset).
      • Maximizing our information for our budget.
  • Example 1 - Using all Rows

    • We want 12 runs, 5 2-level factors, but factor A needs 2/3 of the levels at "L1" and 1/3 at "L2".

  • Example 2 - Using all Rows
    • Use Big Class data set, want to assign one of 2 treatments to each student.
    • We want to account for gender, height, and weight.
  • Example 3 - Using all Rows
    • Blocks of size 4, an 8-level categorical, and 4 continuous factors.
    • 56 runs total.
    • Use a balanced incomplete block design (BIBD) to start.
  • Example 4 - Using subset of Rows

    • Only want to pick 20 students from Big Class.

    • What about if there are certain students we want to ensure are chosen?

    • Add the batch label, but remove it from the model

Covariate.JPG

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Start:
Mon, Oct 25, 2021 02:00 PM EDT
End:
Mon, Oct 25, 2021 03:00 PM EDT
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