Custom Design Options
The Custom Design red triangle menu contains options for design setup and generation.
Save Responses
Saves the information in the Responses panel to a new data table. This data table is locked to prevent unintended changes to the responses. Use this table to load the responses and their associated information into most designs. Use the Load and Edit table script to relaunch the designer to make changes to the responses or unlock the data table to manually change responses.
Load Responses
Opens a window that enables you to load your DOE responses from a specified data table. The Select Data Table window includes an option to erase the existing responses in your design. This option is checked by default, but you can uncheck the option to append responses to your design. A response data table has a row for each response and columns for each factor property. Generate a response table using the Save Responses option.
Save Factors
Saves the information in the Factors panel to a new data table. Each factor’s column contains its levels. Other information is stored as column properties. This data table is locked to prevent unintended changes to the factors. Use this table to load the factors and their associated information into most designs. Use the Load and Edit table script to relaunch the designer to make changes to the responses or unlock the data table to manually change responses.
Load Factors
Opens a window that enables you to load your DOE factors from a specified data table. The Select Data Table window includes an option to erase the existing factors in your design. This option is checked by default, but you can uncheck the option to append factors to your design. A factor data table has a column for each factor and rows for each level of the factor. Factor columns need the Design Role column property defined. Generate a factor table using the Save Factors option. If you load factors that are inconsistent with a design type, an alert appears.
Save Constraints
(Not available for some designs.) Saves factor constraints that you defined in the Define Factor Constraints or Linear Constraints outline into a data table, with a column for each constraint. You can then quickly load the constraints into most DOE windows.
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”.
Load Constraints
(Not available for some designs.) Loads factor constraints from a data table. Generate a constraints table using the Save Constraints option.
Set Random Seed
Sets the random seed that JMP uses to control actions that have a random component. These actions include one or more of the following:
– initializing search algorithms for design generation
– randomizing Run Order for design construction
– selecting a starting design for designs based on random starts
To reproduce a design, enter the random seed that generated the design before clicking Make Design.
The random seed associated with a design is included in the DOE Dialog script that is saved to the design data table.
Simulate Responses
Adds simulated response values and a column containing a simulation formula to the design table. Select this option before you click Make Table.
When you click Make Table, the following occur:
– A set of simulated response values is added to each response column.
– For each response, a new column that contains a simulation model formula is added to the design table. The formula is based on the model that is specified in the design window. For a definitive screening design the model is based on the main effects model.
– A Model window appears that enables you to set the values of the model coefficients, set model rules, and specify a distribution for the simulation. For definitive screening designs there is a section where you can add terms to the model. Three distributions are available: Normal, Binomial, or Poisson. Select the distribution based on your response (continuous, dichotomous, or count).
Note: Not all distributions are available for all design types.
– 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.
For additional details, see Simulate Responses.
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 Basic Analysis.
Save X Matrix
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.
Optimality Criterion
Specifies the design optimality criterion. The default criterion, Recommended, specifies A-optimality for two-level designs for main effects and interactions, I-optimality for full quadratic models generated with the RSM button, and D-optimality otherwise. For more information about the D-, I-, A-, 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.
Number of Starts
Enables you to specify the number of random starts used in constructing the design. See Number of Starts.
Design Search Time
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.
Sphere Radius
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.
Note: Sphere Radius constraints cannot be combined with constraints added using the Specify Linear Constraints option. Also, the option is not available when hard-to-change factors are included (split-plot designs).
Advanced Options > Mixture Sum
Set the sum of the mixture factors to any positive value. Use this option to keep a component of a mixture constant throughout an experiment.
Advanced Options > Split Plot Variance Ratio
Specify the ratio of the variance of the random whole plot and the subplot variance (if present) to the error variance. Before setting this value, you must define a hard-to-change factor for your split-plot design, or hard and very-hard-to-change factors for your split-split-plot design. Then you can enter one or two positive numbers for the variance ratios, depending on whether you have specified a split-plot or a split-split-plot design.
Advanced Options > Prior Parameter Variance
(Available only when the Model section is available.) Specify the weights that are used for factors whose Estimability is set to If Possible. The option updates to show the default weights when you click Make Design. Enter a positive number for each of the terms for which you want to specify a weight. The value that you enter is the square root of the reciprocal of the prior variance. A larger value represents a smaller variance and therefore more prior information that the effect is not active.
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. See “The Alias Matrix” and Optimality Criteria.
Advanced Options > A- Optimality Parameter Weights
(Use for A-Optimal designs.) Specify weights for the model parameters. This enables you to place more weight on the variance of the main effects over say 2nd order effects. For more information about parameter weights see Morgan and Stallings (2017).
Advanced Options > D Efficiency Weight
Specify the relative importance of D-efficiency to alias optimality in constructing the design. Select this option to balance reducing the variance of the coefficients with obtaining a desirable alias structure. Values should be between 0 and 1. Larger values give more weight to D-Efficiency. The default value is 0.5. This option has an effect only when you select Make Alias Optimal Design as your Optimality Criterion.
For the definition of D-efficiency, see Optimality Criteria. For more information about alias optimality, see Alias Optimality.
Save Script to Script Window
Creates the script for the design that you specified in the Custom Design window and places it in an open script window.