Modeling and Multivariate Methods - SAS

Chapter 3 Fitting Standard Least Squares Models 83
Effect Screening
You also do not need scaled estimates if your factors have the Coding column property. In that case, they are
converted to uncoded form when the model is estimated and the results are already in an interpretable form
for effect sizes.
Example of Scaled Estimates
1. Open the Drug.jmp sample data table.
2. Select Analyze > Fit Model.
3. Select y and click Y.
4. Add Drug and x as the effects.
5. Click Run.
6. From the red triangle menu next to Response y, select Effect Screening > Scaled Estimates.
As noted in the report, the estimates are parameter-centered by the mean and scaled by range/2.
Figure 3.21 Scaled Estimates
Plot Options
The Normal, Bayes, and Pareto Plot options correct for scaling and for correlations among the estimates.
These three Effect Screening options point out the following:
1. The process of fitting can be thought of as converting one set of realizations of random values (the
response values) into another set of realizations of random values (the parameter estimates). If the design
is balanced with an equal number of levels per factor, these estimates are independent and identically
distributed, just as the responses are.
2. If you are fitting a screening design that has many effects and only a few runs, you expect that only a few
effects are active. That is, a few effects have a sizable impact and the rest of them are inactive (they are
estimating zeros). This is called the assumption of effect sparsity.
3. Given points 1 and 2 above, you can think of screening as a way to determine which effects are inactive
with random values around zero and which ones are outliers (not part of the distribution of inactive
effects).
Therefore, you treat the estimates themselves as a set of data to help you judge which effects are active and
which are inactive. If there are few runs, with little or no degrees of freedom for error, then there are no
classical significance tests, and this approach is especially needed.