Note: It is possible to create a factors table by entering data into an empty table, but remember to assign each column an appropriate Design Role. Do this by right-clicking on the column name in the data grid and selecting Column Properties > Design Role. In the Design Role area, select the appropriate role.
In the constraint table, the first rows contain the coefficients for each factor. The last row contains the inequality bound. Each constraint’s column contains a column property called ConstraintState that identifies the constraint as a “less than” or a “greater than” constraint. See ConstraintState in Column Properties.
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A script called DOE Simulate is saved to the design table. This script reopens the Model window, enabling you to re-simulate values or to make changes to the simulated response distribution.
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Make selections in the Model window to control the distribution of simulated response values. When you click Apply, a formula for the simulated response values is saved in a new column called <Y> Simulated, where Y is the name of the response. Clicking Apply again updates the formula and values in <Y> Simulated.
Note: You can use Simulate Responses to conduct simulation analyses using the JMP Pro Simulate feature. For more information and DOE examples, see Simulate in the Basic Analysis book.
Saves the Moments Matrix and Model Matrix to table scripts in the design data table. These scripts contain the moments and design matrices. See Save X Matrix.
Caution: For a design with nominal factors, the Model Matrix saved by the Save X Matrix option is not the coding matrix used in fitting the linear model. You can obtain the coding matrix used for fitting the model by selecting the option Save Columns > Save Coding Table in the Fit Model report that you obtain when you run the Model script.
Changes the design optimality criterion. The default criterion, Recommended, specifies D-optimality for all design types, unless you added quadratic effects using the RSM button in the Model outline. For more information about the D-, I-, and alias-optimal designs, see Optimality Criteria.
Note: You can set a preference to always use a given optimality criterion. Select File > Preferences > Platforms > DOE. Check Optimality Criterion and select your preferred criterion.
Enables you to specify the number of random starts used in constructing the design. See Number of Starts.
Maximum number of seconds spent searching for a design. The default search time is based on the complexity of the design. See Design Search Time and Number of Starts.
If the iterations of the algorithm require more than a few seconds, a Computing Design progress window appears. If you click Cancel in the progress window, the calculation stops and gives the best design found at that point. The progress window also displays D-efficiency for D-optimal designs that do not include factors with Changes set to Hard or Very Hard or with Estimability set to If Possible.
Note: You can set a preference for Design Search Time. Select File > Preferences > Platforms > DOE. Check Design Search Time and enter the maximum number of seconds. In certain situations where more time is required, JMP extends the search time.
Constrains the continuous factors in a design to a hypersphere. Specify the radius and click OK. Design points are chosen so that their distance from 0 equals the Sphere Radius. Select this option before you click Make Design.
Bayesian D- or I-optimality is used in constructing designs with If Possible factors. The default values used in the algorithm are 0 for Necessary terms, 4 for interactions involving If Possible terms, and 1 for If Possible terms. For more details, see The Alias Matrix in Technical Details and Optimality Criteria.
(Use for A-Optimal designs) Specify weights for the model parameters. This allows you to place more weight on the variance of the main effects over say 2nd order effects. For more information on parameter weights see Morgan and Stallings (2017).
For the definition of D-efficiency, see Optimality Criteria. For details about alias optimality, see Alias Optimality.