Modeling and Multivariate Methods - SAS

566 Visualizing, Optimizing, and Simulating Response Surfaces Chapter 24
The Profiler
Figure 24.11 Factor Settings Window
Output Random Table prompts for a number of runs and creates an output table with that many rows,
with random factor settings and predicted values over those settings. This is equivalent to (but much
simpler than) opening the Simulator, resetting all the factors to a random uniform distribution, then
simulating output. This command is similar to Output Grid Table, except it results in a random table
rather than a sequenced one.
The prime reason to make uniform random factor tables is to explore the factor space in a multivariate
way using graphical queries. This technique is called Filtered Monte Carlo.
Suppose you want to see the locus of all factor settings that produce a given range to desirable response
settings. By selecting and hiding the points that don’t qualify (using graphical brushing or the Data
Filter), you see the possibilities of what is left: the opportunity space yielding the result you want.
Alter Linear Constraints allows you to add, change, or delete linear constraints. The constraints are
incorporated into the operation of Prediction Profiler. See “Linear Constraints” on page 575.
Save Linear Constraints allows you to save existing linear constraints to a Table Property/Script called
Constraint. See“Linear Constraints” on page 575.
Default N Levels allows you to set the default number of levels for each continuous factor. This option is
useful when the Profiler is especially large. When calculating the traces for the first time, JMP measures
how long it takes. If this time is greater than three seconds, you are alerted that decreasing the Default N
Levels speeds up the calculations.
Conditional Predictions appears when random effects are included in the model. The random effects
predictions are used in formulating the predicted value and profiles.
Simulator launches the Simulator. The Simulator enables you to create Monte Carlo simulations using
random noise added to factors and predictions for the model. A typical use is to set fixed factors at their
optimal settings, and uncontrolled factors and model noise to random values and find out the rate that
the responses are outside the specification limits. For details see “The Simulator” on page 592.
Interaction Profiler brings up interaction plots that are interactive with respect to the profiler values.
This option can help visualize third degree interactions by seeing how the plot changes as current values
for the terms are changed. The cells that change for a given term are the cells that do not involve that
term directly.