Example of Augment Design
This example demonstrates how to use the Augment Design platform to resolve ambiguities left by a screening design. In this study, a chemical engineer investigates the effects of five factors on the percent reaction of a chemical process.
1. Select Help > Sample Data Library and open Design Experiment/Reactor 8 Runs.jmp.
2. Select DOE > Augment Design.
3. Select Percent Reacted and click Y, Response.
4. Select all other variables except Pattern and click X, Factor.
5. Click OK.
Figure 6.1 Augment Design Dialog for the Reactor Example
Note: You can check Group new runs into separate block to add a blocking factor to any design. However, the purpose of this example is to estimate all two-factor interactions in 16 runs, which cannot be done when there is the additional blocking factor in the model.
6. Click Augment.
The model shown in Figure 6.2 is defined using the Model script in the data table.
Figure 6.2 Initial Augmented Model
7. In the Model outline, select 2nd from the Interactions menu.
This adds all the two-factor interactions to the model. The Minimum number of runs given for the specified model is 16, as shown in the Design Generation text edit box.
Figure 6.3 Augmented Model with All Two-Factor Interactions
Note: Setting the Random Seed in step 8 and Number of Starts in step 9 reproduces the exact results shown in Figure 6.4. When constructing a design on your own, these steps are not necessary.
8. (Optional) From the Augment Design red triangle menu, select Set Random Seed, type 282322901, and click OK.
9. (Optional) From the Augment Design red triangle menu, select Number of Starts, type 800, and click OK.
10. Click Make Design.
JMP computes settings for a D-optimally augmented design.
Figure 6.4 D-Optimally Augmented Design
11. Click Make Table to generate a design table containing the original design and results and the D-Optimally augmented factor settings.
Analyze the Augmented Design
Suppose you have already run the experiment on the augmented data and recorded results in the Percent Reacted column of the data table.
1. Select Help > Sample Data Library and open Design Experiment/Reactor Augment Data.jmp.
You want to maximize Percent Reacted. However, the column’s Response Limits column property in this sample data table is set to Minimize.
2. Click the asterisk next to the Percent Reacted column name in the Columns panel of the data table and select Response Limits, as shown on the left in Figure 6.5.
3. In the Column Info dialog that appears, change the response limit to Maximize, as shown on the right in Figure 6.5.
Figure 6.5 Change the Response Limits Column Property for the Percent Reacted Column
You are now ready to run the analysis.
4. Click the green triangle next to the Model script.
Figure 6.6 Completed Augmented Experiment (Reactor Augment Data.jmp)
The Model script opens the Fit Model window with all main effects and two-factor interactions as effects.
5. Change the fitting personality on the Fit Model dialog from Standard Least Squares to Stepwise.
Figure 6.7 Fit Model Dialog for Stepwise Regression on Generated Model
6. Click Run.
The stepwise regression control panel appears. Click the check boxes for all the main effect terms.
Note: Select P-value Threshold from the Stopping Rule menu, Mixed from the Direction menu, and make sure Prob to Enter is 0.050 and Prob to Leave is 0.100. These are not the default values. Follow the dialog shown in Figure 6.8.
Figure 6.8 Initial Stepwise Model
7. Click Go.
This starts the stepwise regression. The process continues until all terms that meet the Prob to Enter and Prob to Leave criteria in the Stepwise Regression Control panel are entered into the model.
Figure 6.9 shows the result of this example analysis. Note that Feed Rate is out of the model while the Catalyst*Temperature, Stir Rate*Temperature, and the Temperature*Concentration interactions have entered the model.
Figure 6.9 Completed Stepwise Model
8. Click Make Model on the Stepwise control panel to generate the reduced model.
Figure 6.10 New Prediction Model Dialog
9. Click Run.
The Actual by Predicted Plot indicates that the overall model is significant (P < 0.0001). Both the Actual by Predicted Plot and the Lack of Fit Test show no evidence of model misspecification.
Figure 6.11 Prediction Model Analysis of Variance and Lack of Fit Tests
The Effect Summary report shows that Catalyst is the most significant effect. However, note that the three two-factor interactions are also significant.
10. Choose Optimality and Desirability > Maximize Desirability from the menu on the Prediction Profiler title bar.
The prediction profile plot in Figure 6.12 shows that maximum occurs at the high levels of Catalyst, Stir Rate, and Temperature and the low level of Concentration. At these extreme settings, the estimate of Percent Reacted increases from 65.17 to 98.38.
Figure 6.12 Maximum Percent Reacted
This example illustrates how an iterative approach to DOE can reduce costs and provide valuable information.
Augment Design Launch Window
To launch the Augment Design platform, open the data table that contains the design that you would like to augment and select DOE > Augment Design. The example in Figure 6.13 uses the Reactor 8 Runs.jmp sample data table, which is located in the Design Experiment folder.
Figure 6.13 Augment Design Launch Window
The launch window contains the following buttons:
Y, Response
Enter the response column or columns. Entering a response is required. Responses must be numeric.
X, Factor
Enter the factor columns. Factors can be of any data type or modeling type.
Augment Design Window
The initial Augment Design window consists of the Factors and Define Factor Constraints outlines, and the Augmentation Choices panel.
Figure 6.14 Initial Augment Design Window Using Reactor 8 Runs.jmp
Factors
When the Augment Design window opens, the Factors outline shows the following:
Name
All factors listed as X, Factor, in the Augment Design launch window except for factors with the Random Block design role column property.
Role
If the factor has a Design Role column property specified in the data table, that role is shown in the Role column. If the factor does not have a Design Role column property and is constant, then Constant appears in the Role column. Otherwise, the factor’s modeling type appears in the Role column.
Changes
If the factor has a Factor Changes column property specified in the data table, that value is shown in the Changes column. If the factor does not have a Factor Changes column property, then Changes is specified as Easy.
Note: If a factor has a Factor Changes column property that is set to Hard or Very Hard, then the corresponding whole plot factor must be included in the X, Factor list in the Augment Design launch window.
Values
For continuous factors, shows the minimum and maximum values. For categorical factors, shows the levels.
Tip: Factors that have a role of Categorical or Constant appear in the Name column with a down arrow icon. Click on the down arrow to add levels. If the factor is Constant and has a categorical modeling type, multiple levels can be added. If the factor is Constant and has a continuous modeling type, only one level can be added.
Define Factor Constraints
If you augment a design using the Space Filling or Augment options, you can define restrictions on the design space for the added runs.
Use Define Factor Constraints to restrict the design space. Unless you have loaded a constraint or included one as part of a script, the None option is selected. To specify constraints, select one of the other options:
Specify Linear Constraints
Specifies inequality constraints on linear combinations of factors. Only available for factors with a Role of Continuous or Mixture. See “Specify Linear Constraints”.
Note: When you save a script for a design that involves a linear constraint, the script expresses the linear constraint as a less than or equal to inequality (
).
).Use Disallowed Combinations Filter
Defines sets of constraints based on restricting values of individual factors. You can define both AND and OR constraints. See “Use Disallowed Combinations Filter”.
Use Disallowed Combinations Script
Defines disallowed combinations and other constraints as Boolean JSL expressions in a script editor box. See “Use Disallowed Combinations Script”.
Specify Linear Constraints
In cases where it is impossible to vary continuous factors independently over the design space, you can specify linear inequality constraints. Linear inequalities describe factor level settings that are allowed.
Click Add to enter one or more linear inequality constraints.
Add
Adds a template for a linear expression involving all the continuous factors in your design. Enter coefficient values for the factors and select the direction of the inequality to reflect your linear constraint. Specify the constraining value in the box to the right of the inequality. To add more constraints, click Add again.
Note: The Add option is disabled if you have already constrained the design region by specifying a Sphere Radius.
Remove Last Constraint
Removes the last constraint.
Check Constraints
Checks the constraints for consistency. This option removes redundant constraints and conducts feasibility checks. A JMP alert appears if there is a problem. If constraints are equivalent to bounds on the factors, a JMP alert indicates that the bounds in the Factors outline have been updated.
Use Disallowed Combinations Filter
This option uses an adaptation of the Data Filter to facilitate specifying disallowed combinations. For detailed information about using the Data Filter, see the JMP Reports chapter in the Using JMP book.
Select factors from the Add Filter Factors list and click Add. Then specify the disallowed combinations by using the slider (for continuous factors) or by selecting levels (for categorical factors).
The red triangle options for the Add Filter Factors menu are those found in the Select Columns panel of many platform launch windows. See the Get Started chapter in the Using JMP book for additional details about the column selection menu.
When you click Add, the Disallowed Combinations control panel shows the selected factors and provides options for further control. Factors are represented as follows, based on their modeling types:
Continuous Factors
For a continuous factor, a double-arrow slider that spans the range of factor settings appears. An expression that describes the range using an inequality appears above the slider. You can specify disallowed settings by dragging the slider arrows or by clicking on the inequality bounds in the expression and entering your desired constraints. In the slider, a solid blue highlight represents the disallowed values.
Categorical Factor
For a categorical factor, the possible levels are displayed either as labeled blocks or, when the number of levels is large, as list entries. Select a level to disallow it. To select multiple levels, hold the Control key. The block or list entries are highlighted to indicate the levels that have been disallowed. When you add a categorical factor to the Disallowed Combinations panel, the number of levels of the categorical factor is given in parentheses following the factor name.
Disallowed Combinations Options
The control panel has the following controls:
Clear
Clears all disallowed factor level settings that you have specified. This does not clear the selected factors.
Start Over
Removes all selected factors and returns you to the initial list of factors.
AND
Opens the Add Filter Factors list. Selected factors become an AND group. Any combination of factor levels specified within an AND group is disallowed.
To add a factor to an AND group later on, click the group’s outline to see a highlighted rectangle. Select AND and add the factor.
To remove a single factor, select Delete from its red triangle menu.
OR
Opens the Add Filter Factors list. Selected factors become a separate AND group. For AND groups separated by OR, a combination is disallowed if it is specified in at least one AND group.
Red Triangle Options for Factors
A factor can appear in several OR groups. An occurrence of the factor in a specific OR group is referred to as an instance of the factor.
Delete
Removes the selected instance of the factor from the Disallowed Combinations panel.
Clear Selection
Clears any selection for that instance of the factor.
Invert Selection
Deselects the selected values and selects the values not previously selected for that instance of the factor.
Display Options
Available only for categorical factors. Changes the appearance of the display. Options include:
‒ Blocks Display shows each level as a block.
‒ List Display shows each level as a member of a list.
‒ Single Category Display shows each level.
‒ Check Box Display adds a check box next to each value.
Find
Available only for categorical factors. Provides a text box beneath the factor name where you can enter a search string for levels of the factor. Press the Enter key or click outside the text box to perform the search. Once Find is selected, the following Find options appear in the red triangle menu:
‒ Clear Find clears the results of the Find operation and returns the panel to its original state.
‒ Match Case uses the case of the search string to return the correct results.
‒ Contains searches for values that include the search string.
‒ Does not contain searches for values that do not include the search string.
‒ Starts with searches for values that start with the search string.
‒ Ends with searches for values that end with the search string.
Use Disallowed Combinations Script
Use this option to disallow particular combinations of factor levels using a JSL script. This option can be used with continuous factors or mixed continuous and categorical factors.
This option opens a script window where you insert a script that identifies the combinations that you want to disallow. The script must evaluate as a Boolean expression. When the expression evaluates as true, the specified combination is disallowed.
When forming the expression for a categorical factor, use the ordinal value of the level instead of the name of the level. If a factor’s levels are high, medium, and low, specified in that order in the Factors outline, their associated ordinal values are 1, 2, and 3. For example, suppose that you have two continuous factors, X1 and X2, and a categorical factor X3 with three levels: L1, L2, and L3, in order. You want to disallow levels where the following holds:
Enter the expression (Exp(X1) + 2*X2 < 0) & (X3 == 2) into the script window.
Figure 6.15 Expression in Script Editor
(In the figure, unnecessary parentheses were removed by parsing.) Notice that functions can be entered as part of the Boolean expression.
Augmentation Choices
The Augment Design platform requires an existing design data table. It gives the following five choices:
Replicate
replicates the design a specified number of times. See “Replicate a Design”.
Add Centerpoints
Adds center points. Specify how many additional runs you want to add as center points to the design. A center point is a run whose setting for each continuous factor is midway between the high and low settings. See “Center Points, Replicate Runs, and Testing” in the “Starting Out with DOE” chapter.
If a design contains both continuous and other types of factors, center points might not be balanced relative to the levels of the other factors. Augment Design chooses the center points to maximize the D-, I-, or alias efficiency of the design.
See “Add Center Points”.
Fold Over
creates a foldover design. See “Creating a Foldover Design”.
Add Axial
adds axial points together with center points to transform a screening design to a response surface design. See “Adding Axial Points”.
Space Filling
Adds additional runs to any design consisting of continuous factors. Additional runs are constructed using the fast flexible filling methodology. See “Space Filling”.
Augment
adds runs to the design (augment) using a model, which can have more terms than the original model. See “Augment”.
Replicate a Design
Replication provides a direct check on the assumption that the error variance is constant. It also reduces the variability of the regression coefficients in the presence of large process or measurement variability.
To replicate the design a specified number of times:
1. Open a data table that contains a design that you want to augment. This example uses Reactor 8 Runs.jmp from the Design Experiment sample data folder installed with JMP.
2. Select DOE > Augment Design to see the initial dialog for specifying factors and responses.
3. Select Percent Reacted and click Y, Response.
4. Select all other variables except Pattern and click X, Factor to identify the factors that you want to use for the augmented design.
5. Click OK to see the Augment Design panel shown in Figure 6.16.
6. If you want the original runs and the resulting augmented runs to be identified by a blocking factor, select Group New Runs into Separate Block on the Augment Design panel.
Figure 6.16 Choose an Augmentation Type
7. Click the Replicate button to see the dialog shown on the left in Figure 6.17. Enter the number of times you want JMP to perform each run and then click OK.
Note: Entering 2 specifies that you want each run to appear twice in the resulting design. This is the same as one replicate (Figure 6.17).
8. View the design, shown on the right in Figure 6.17.
Figure 6.17 Reactor Data Design Augmented with Two Replicates
9. In the Design Evaluation section, click the disclosure icons next to Prediction Variance Profile and Prediction Variance Surface to see the profile and surface plots shown in Figure 6.18.
Figure 6.18 Prediction Profiler and Surface Plot
10. Click Make Table to produce the design table shown in Figure 6.19.
Figure 6.19 The Replicated Design
Add Center Points
Adding center points is useful to check for curvature and reduce the prediction error in the center of the factor region. Center points are usually replicated points that allow for an independent estimate of pure error, which can be used in a lack-of-fit test.
To add center points:
1. Open a data table that contains a design that you want to augment. This example uses Reactor 8 Runs.jmp found in the Design Experiment sample data folder installed with JMP.
2. Select DOE > Augment Design.
3. In the initial Augment Design dialog, identify the response and factors that you want to use for the augmented design (see Figure 5) and click OK.
4. If you want the original runs and the resulting augmented runs to be identified by a blocking factor, check the box beside Group new runs into separate block. (Figure 6.16 shows the check box location directly under the Factors panel.)
5. Click the Add Centerpoints button and enter the number of center points that you want to add. For this example, add two center points, and click OK.
6. Click Make Table to see the data table in Figure 6.20.
The table shows two center points appended to the end of the design.
Figure 6.20 Design with Two Center Points Added
Creating a Foldover Design
A foldover design removes the confounding of two-factor interactions and main effects. This is especially useful as a follow-up to saturated or near-saturated fractional factorial or Plackett-Burman designs.
To create a foldover design:
1. Open a data table that contains a design that you want to augment. This example uses Reactor 8 Runs.jmp, found in the Design Experiment sample data folder installed with JMP.
2. Select DOE > Augment Design.
3. In the initial Augment Design dialog, identify the response and factors that you want to use for the augmented design (see Figure 5) and click OK.
4. Check the box to the left of Group new runs into separate block. (Figure 6.16 shows the check box location directly under the Factors panel.) This identifies the original runs and the resulting augmented runs with a blocking factor.
5. Click the Fold Over button. A dialog appears that lists all the design factors.
6. Select the factors to fold. The default, if you select no factors, is to fold on all design factors. If you choose a subset of factors to fold over, the remaining factors are replicates of the original runs. The example in Figure 6.21 folds on all five factors and includes a blocking factor.
7. Click Make Table. The design data table that results lists the original set of runs as block 1 and the new (foldover) runs are block 2.
Figure 6.21 Listing of a Foldover Design on All Factors
Adding Axial Points
You can add axial points together with center points, which transforms a screening design to a response surface design. Follow these steps:
1. Open a data table that contains a design that you want to augment. This example uses Reactor 8 Runs.jmp, from the Design Experiment sample data folder installed with JMP.
2. Select DOE > Augment Design.
3. In the initial Augment Design dialog, identify the response and factors that you want to use for the augmented design (see Figure 5) and click OK.
4. If you want the original runs and the resulting augmented runs to be identified by a blocking factor, check the box beside Group New Runs into Separate Block (Figure 6.16).
5. Click Add Axial.
6. Enter the axial values in units of the factors scaled from –1 to +1, and then enter the number of center points that you want. When you click OK, the augmented design includes the number of center points specified and constructs two axial points for each variable in the original design.
Figure 6.22 Entering Axial Values
7. Click Make Table. The design table appears. Figure 6.23 shows a table augmented with two center points and two axial points for five variables.
Figure 6.23 Design Augmented with Two Center and Ten Axial Points
Space Filling
The Space Filling augmentation choice adds points to a design consisting of continuous factors using the fast flexible filling method with the MaxPro criterion. The Space Filling choice accommodates constraints on the design space. You can specify linear constraints or disallowed combinations.
The algorithm that is used to augment designs begins by generating a large number of random points within the specified design region. These points are then clustered using a Fast Ward algorithm into a number of clusters that equals the Number of Additional Runs that you specify.
The final design points are obtained by optimizing the MaxPro (maximum projection) criterion over the existing and additional runs. For p factors and n equal to the number of existing and additional runs, the MaxPro criterion strives to find points in the clusters that minimize the following criterion:
The MaxPro criterion maximizes the product of the distances between design points in a way that involves all factors. This supports the goal of providing good space-filling properties on projections of factors. See Joseph et al. (2015).
Augment
A powerful use of the Augment Design platform is to add runs using a model that can have more terms than the original model. For example, you can achieve the objectives of response surface methodology by changing a linear model to a full quadratic model and adding the necessary number of runs.
D-optimal augmentation is a powerful tool for sequential design. Using this feature you can add terms to the original model and find optimal new test runs with respect to this expanded model. You can also group the two sets of experimental runs into separate blocks, which optimally blocks the second set with respect to the first.
When you select to Augment a design, Model and Alias Terms outlines appear. Use these outlines to add effects to the Model and Alias Terms lists.
Model Outline
The Model outline lists the effects that are in the Model script in the design table containing the design that you want to augment. If there is no Model script in the design table, the Model outline shows only the main effects.
Add or remove effects to specify your model for the augmented design. For details on how to add and remove effects, see “Model” in the “Evaluate Designs” chapter.
Alias Outline
The Alias Terms outline contains all two-factor interactions that are not in the Model outline. When you generate your augmented design, a Design Evaluation outline is provided. The effects in Alias Terms list control the calculations in the Alias Matrix and Color Map on Correlations outlines under Design Evaluation. See “Alias Matrix” in the “Evaluate Designs” chapter and “Color Map on Correlations” in the “Evaluate Designs” chapter.
Add or remove effects to compare your designs for effects that may be active. For details on how to add and remove effects, see “Model” in the “Evaluate Designs” chapter.
Example of Using Augment
This example illustrates how to add new runs and model terms to the original model:
1. Select Help > Sample Data Library and open Design Experiment/Reactor Augment Data.jmp.
2. Select DOE > Augment Design.
3. Select Percent Reacted and click Y, Response.
4. Select Feed Rate, Catalyst, Stir Rate, Temperature, and Concentration, and click X, Factor.
5. Click OK.
Notice the check Group New Runs into Separate Block. This creates a blocking factor that places the original runs and the augmented runs in separate blocks. You will not use this option in this example.
6. Click Augment.
The original 16 runs are shown in the Factor Design panel. You will add 6 new runs with the goal of estimating all 5 quadratic terms.
7. In the Model outline, click the Powers button and select 2nd.
This adds the five quadratic terms to the model, shown in Figure 6.24. The augmented design will enable you to estimate these quadratic terms.
8. In the Design Generation outline, enter 22 in the box to the right of Enter Number of Runs (counting 16 included runs).
Figure 6.24 Model, Factor Design, and Design Generation Outlines
9. Click Make Design.
The six new runs appear in the Design panel. Note that you can explore various properties of the augmented design in the Design Evaluation outline.
Figure 6.25 24 Total Runs
10. Click Make Table.
This creates the augmented design table (Figure 6.26) with the additional runs.
Figure 6.26 The Augmented Design Table with New Runs
11. Click on the green triangle next to the Model script.
Note that all five quadratic effects are listed as model effects in the Model Specification window.
12. Enter fictional Percent Reacted data values for the six new runs, rows 17 to 22, and click Run.
Note that you can test for all effects, including the five new quadratic effects.
Augment Design Options
This section describes the options available under the Augment Design red triangle menu.
Save Responses
Saves the information in the Responses panel to a new data table. You can then quickly load the responses and their associated information into most DOE windows. This option is helpful if you anticipate re-using the responses.
Load Responses
Loads responses that you saved 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. You can then quickly load the factors and their associated information into most DOE windows.
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.
Load Factors
Loads factors that you saved using the Save Factors option.
Save Constraints
(Unavailable for some platforms) 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” in the “Column Properties” appendix.
Load Constraints
(Unavailable for some platforms) Loads factor constraints that you saved using the Save Constraints option.
Set Random Seed
Sets the random seed that JMP uses to control certain actions that have a random component. These actions include the following:
‒ simulating responses using the Simulate Responses option
‒ randomizing Run Order for design construction
‒ selecting a starting design for designs based on random starts
To reproduce a design or simulated responses, enter the random seed that generated them. For designs using random starts, set the seed before clicking Make Design. To control simulated responses or run order, set the seed before clicking Make Table.
Note: 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 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 a column that contains a simulation model formula is added to the design table. The formula and values are based on the model that is specified in the design window.
‒ A Model window appears where you can set the values of coefficients for model effects and specify one of three distributions: Normal, Binomial, or Poisson.
‒ A script called DOE Simulate is saved to the design table. This script re-opens the Model window, enabling you to re-simulate values or to make changes to the simulated response distribution.
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.
For additional details, see “Simulate Responses” in the “Custom Designs” chapter.
Note: 
You can use Simulate Responses to conduct simulation analyses using the JMP Pro Simulate feature. For information about Simulate and some DOE examples, see the Simulate chapter in the Basic Analysis book.
You can use Simulate Responses to conduct simulation analyses using the JMP Pro Simulate feature. For information about Simulate and some DOE examples, see the Simulate chapter in the Basic Analysis book.Save X Matrix
Saves scripts called Moments Matrix and Model Matrix to the design data table. These scripts contain the moments and design matrices. See “Save X Matrix” in the “Custom Designs” chapter.
Caution: For a design with nominal factors, the matrix in the Model Matrix script 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
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” in the “Custom Designs” appendix.
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” in the “Custom Designs” chapter.
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” in the “Custom Designs” chapter and “Number of Starts” in the “Custom Designs” chapter.
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 outline 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. For more details, see “The Alias Matrix” in the “Technical Details” appendix and “Optimality Criteria” in the “Custom Designs” chapter.
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” in the “Custom Designs” chapter. For details about alias optimality, see “Alias Optimality” in the “Custom Designs” chapter.
Advanced Options > Set Delta for Power
Specify the difference in the mean response that you want to detect for model effects. See “Set Delta for Power” in the “Custom Designs” chapter.
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.
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