Modeling and Multivariate Methods - SAS

Chapter 8 Analyzing Screening Designs 231
Overview of the Screening Platform
Overview of the Screening Platform
Screening situations depend on effect sparsity, where most effects are assumed to be inactive. Using this
assumption, the smaller estimates can help estimate the error in the model and determine whether the larger
effects are real. Basically, if all the effects are inactive, they should vary randomly, with no effect deviating
substantially from the other effects.
When to Use the Screening Platform
If your data are all two-level and orthogonal, then all of the statistics in this platform should work well.
If you have categorical terms containing more than two levels, then the Screening platform is not
appropriate for the design. JMP treats the level numbers as a continuous regressor. The variation across the
factor is scattered across main and polynomial effects for that term.
For highly supersaturated main effect designs, the Screening platform is effective in selecting factors, but is
not as effective at estimating the error or the significance. The Monte Carlo simulation to produce p-values
uses assumptions that are valid for this case.
If your data are not orthogonal, then the constructed estimates are different from standard regression
estimates. JMP can pick out big effects, but it does not effectively test each effect. This is because later
effects are artificially orthogonalized, making earlier effects look more significant.
The Screening platform is not appropriate for mixture designs.
Comparing Screening and Fit Model
Consider Reactor Half Fraction.jmp, from the Sample Data folder. The data are derived from a design in
Box, Hunter, and Hunter (1978). We are interested in a model with main effects and two-way interactions.
This example uses a model with fifteen parameters for a design with sixteen runs.
For this example, all continuous factors, except the response factor, are selected as the screening effects, X.
Percent Reacted is selected as the response Y. JMP constructs interactions automatically, unlike Fit Model,
where the interactions are added distinctly.
Figure 8.2 shows the result of using the Fit Model platform, where a factorial to degree 2 model is specified.
This result illustrates why the Screening platform is needed.