The Custom Design window updates as you work through the design steps. The outlines that appear, separated by buttons that update the window, follow the flow in Custom Design Flow.
Tip: When you have completed your Responses panel, consider selecting Save Responses from the red triangle menu. This saves the response names, goals, limits, and importance values in a data table that you can later reload.
The name of the response. When added, a response is given a default name of Y, Y2, and so on. To change this name, doubleclick it and enter the desired name.
The Goal tells JMP whether you want to maximize your response, minimize your response, match a target, or that you have no response goal. JMP assigns a Response Limits column property, based on these specifications, to each response column in the design table. It uses this information to define a desirability function for each response. The Profiler and Contour Profiler use these desirability functions to find optimal factor settings. For further details, see the Profilers book and Response Limits in Column Properties.
Note: If your target response is not midway between the Lower Limit and the Upper Limit, you can change the target after you generate your design table. In the data table, open the Column Info window for the response column (Cols > Column Info) and enter the desired target value.
The Goal, Lower Limit, Upper Limit, and Importance that you specify when you enter a response are used in finding optimal factor settings. For each response, the information is saved in the generated design data table as a Response Limits column property. JMP uses this information to define the desirability function. The desirability function is used in the Prediction Profiler to find optimal factor settings. For further details about the Response Limits column property and examples of its use, see Response Limits in Column Properties.
Adds multiple factors. Enter the number of factors to add, click Add Factor, and then select the factor type. Repeat Add N Factors to add multiple factors of different types.
Tip: When you have completed the Factors outline, consider selecting Save Factors from the red triangle menu. This saves the response names, goals, limits, and importance values in a data table that you can later reload.
The name of the factor. When added, a factor is given a default name of X1, X2, and so on. To change this name, doubleclick it and enter the desired name.
Indicates whether the factor levels are Easy, Hard, or Very Hard to change. Click on the default value of Easy to change it. When you specify factors as Hard or Very Hard to change, your design reflects these restrictions on randomization. A factor cannot be designated as Very Hard unless the Factors list contains a factor designated as Hard. The Factor Changes column property is saved to the data table. For more details, see Changes and Random Blocks.
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To remove a factor level, click the value, click Delete, and click outside the text box.

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To modify the entry under Changes, click the value in the Changes column and select the appropriate entry.

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To edit a value, click the value in the Values column.

To choose a factor type, click Add Factor in Custom Design.
The default values for a discrete numeric factor with k levels, where , are the integers . The default values for a discrete numeric factor with levels are 1 and 1. Replace the default values with the settings that you plan to use in your experiment.
In the assumed model, the effects for a discrete numeric factor with k levels include polynomial terms in that effect through order k1. For k greater than 6, powers up to the 5th level are included. The Estimability for polynomial effects (powers of two or higher) is set to If Possible. This allows the algorithm to use the multiple levels as permitted by the run size. If the polynomial terms are not included, then a main effects only design is created. For more details about how discrete numeric factors are treated in the assumed model, see Model.
Either numeric or character data types. The values of a covariate factor are measurements on experimental units that are known in advance of an experiment. Covariate values are selected to ensure the optimality of the resulting design relative to the optimality criterion. See Changes and Random Blocks and Covariates with HardtoChange Levels.
Continuous factors that represent ingredients in a mixture. The values for a mixture factor must sum to a constant. By default, the values for all mixture factors sum to one. To set the sum of the mixture components to some other positive value, select Advanced Options > Mixture Sum from the red triangle menu. The Mixture column property is saved to the data table.
For designs with Hard or Very Hard to change factors, Custom Design strives to find a design that is optimal, given your specified optimality criterion. See Optimality Criteria. For details about the methodology used to generate splitplot designs, see Jones and Goos (2007). For details relating to designs with hardtochange covariates, see Jones and Goos (2014, to appear).
Factors and Design Generation Outline for a SplitSplitPlot Design shows a splitsplitplot scenario, using the factors from the Cheese Factors.jmp sample data table (located in the Design Experiment folder).
If you assign Changes as Hard for one or more factors, but no factors have Changes assigned as Very Hard, a categorical factor called Whole Plots is added to the design. This situation results in a splitplot design:
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Each level of Whole Plots corresponds to a block of constant settings of the hardtochange factors.

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The factor Whole Plots is assigned the Design Role column property with a value of Random Block.

When you designate Changes as both Hard and Very Hard, categorical factors called Subplots and Whole Plots are added to the design. This situation results in a splitsplitplot design:
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Each level of Subplots corresponds to a block of constant settings of the hardtochange factors.

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Each level of Whole Plots corresponds to a block of constant settings of the veryhardtochange factors.

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The Model script in the design table applies the Random Effect attribute to the Whole Plots and Subplots effects.

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In the design table, both of the factors Whole Plots and Subplots are assigned the Design Role column property with a value of Random Block.

To construct a twoway splitplot design, select the Hard to change factors can vary independently of Very Hard to change factors option under Design Generation. The option crosses the levels of the hardtochange factor with the levels of the veryhardtochange factor. See TwoWay SplitPlot Designs.
Use the Number of Whole Plots and Number of Subplots text boxes to specify values for the numbers of whole plots or subplots. These boxes are initialized to suggested numbers of whole plots and subplots. For information about how these values are obtained, see Numbers of Whole Plots and Subplots.
For more details and scenarios that illustrate random block splitplot, splitsplitplot, and twoway splitplot designs, see Designs with Randomization Restrictions. For details about designs with hardtochange covariates, see Covariates with HardtoChange Levels.
For each factor, various column properties are saved to the data table. You can find details about these column properties and related examples in Appendix A, “Column Properties”.
Each factor is given the Design Role column property. The Role that you specify in defining the factor determines the value of its Design Role column property. When you add a random block under Design Generation, that factor is assigned the Random Block value. The Design Role property reflects how the factor is intended to be used in modeling the experimental data. Design Role values are used in the Augment Design platform. For details, see Design Role in Column Properties.
Each factor is assigned the Factor Changes column property. The value that you specify under Changes determines the value of its Factor Changes column property. The Factor Changes property reflects how the factor is used in modeling the experimental data. Factor Changes values are used in the Augment Design and Evaluate Design platforms. For details, see Factor Changes in Column Properties.
If the Role is Continuous, Discrete Numeric, a continuous Covariate, or Uncontrolled, the Coding column property for the factor is saved. This property transforms the factor values so that the low and high values correspond to –1 and +1, respectively. For details, see Coding in Column Properties.
If the Role is Categorical or Blocking, the Value Ordering column property for the factor is saved. This property determines the order in which levels of the factor appear. For details, see Value Ordering in Column Properties.
If the Role is Mixture, the Mixture column property for the factor is saved. This property indicates the limits for the factor and the mixture sum. It also enables you to choose the coding for the mixture factors. For details, see Mixture in Column Properties.
For a blocking factor, indicates the maximum allowable number of runs in each block. When a Blocking factor is specified in the Factors outline, the RunsPerBlock column property is saved for that factor. For details, see RunsPerBlock in Column Properties.
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:
Specifies inequality constraints on linear combinations of factors. Only available for factors with a Role of Continuous or Mixture. See Specify Linear Constraints.
Defines sets of constraints based on restricting values of individual factors. You can define both AND and OR constraints. See Use Disallowed Combinations Filter.
Defines disallowed combinations and other constraints as Boolean JSL expressions in a script editor box. See Use Disallowed Combinations Script.
Click Add to enter one or more linear inequality constraints.
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 Using JMP book for additional details about the column selection menu.
To remove a single factor, select Delete from its red triangle menu.
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.
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Blocks Display shows each level as a block.

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List Display shows each level as a member of a list.

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Single Category Display shows each level.

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Check Box Display adds a check box next to each value.

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:
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Clear Find clears the results of the Find operation and returns the panel to its original state.

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Match Case uses the case of the search string to return the correct results.

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Contains searches for values that include the search string.

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Does not contain searches for values that do not include the search string.

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Starts with searches for values that start with the search string.

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Ends with searches for values that end with the search string.

Enter the expression (Exp(X1) + 2*X2 < 0) & (X3 == 2) into the script window.
When the Model outline opens, for most factors only the main effects appear. If you have entered a discrete numeric factor, polynomial terms also appear. The Estimability of secondand higherorder terms is set to If Possible. If you want to ensure that these terms are estimable, change their Estimability to Necessary.
Note: You can ensure that the estimability of discrete numeric polynomial terms is always set to Necessary. Select File > Preferences > Platforms > DOE. Check Discrete Numeric Powers Set to Necessary.
The Bayesian DOptimal design approach obtains precise estimation of all Necessary terms while providing omnibus detectability (and some estimability) for If Possible terms. For more detail, see Response Surface Experiments in Examples of Custom Designs and Bayesian DOptimality.
It is possible that effects not included in your assumed model are active. In the Alias Terms outline, add potentially active effects that are not in your assumed model but might bias the estimates of model terms. Once you generate your design, the Alias Matrix outline appears under Design Evaluation. The Alias Matrix entries represent the degree of bias imparted to model parameters by the effects that you specified in the Alias Terms outline. For details, see the The Alias Matrix in Technical Details.
By default, the Alias Terms outline includes all twoway interaction effects that are not in your Model outline (with the exception of terms involving blocking factors). Add terms using the buttons. For a description of how to use these buttons to add effects to the Alias Terms table, see Model.
The Alias Terms table includes all twoway interactions by default. You can add the threeway interaction by selecting Interactions > 3rd.
Once you specify six runs in the Design Generation outline and click Make Design, the Design Evaluation outline appears. Open the Design Evaluation outline and the Alias Matrix outline. See Alias Matrix.
The Alias Matrix indicates that each main effect is partially aliased with two of the interactions. See Alias Matrix in Evaluate Designs and The Alias Matrix in Technical Details.
(Not available if a blocking factor is specified) To construct a random block design, enter the number of runs that you want in each block. When you specify the sample size, a factor called Random Block is created. Its levels define blocks of a size that is consistent with the block size that you entered, given the specified number of runs. If the number of runs is an integer multiple of the block size, the block sizes equal your specified value.
Appears when you specify a hard or veryhardtochange factor. The factor Whole Plots corresponds to the veryhardtochange factors (splitsplitplot design), if there are any, otherwise to the hardtochange factors (splitplot design). JMP suggests a value for the number of whole plots that maximizes the information about the coefficients in the model. Or, you can enter a value for the number of whole plots. For details, see Numbers of Whole Plots and Subplots.
Appears when you specify a veryhardtochange factor. The factor Subplots corresponds to the hardtochange factors in the splitsplitplot design. JMP suggests values for the number of whole plots and subplots that maximize the information about the coefficients in the model. Or, you can enter a value for the number of subplots. For details, see Numbers of Whole Plots and Subplots.
Select this option to create a stripplot (also known as twoway splitplot or split block) design. This option creates a design where the hardtochange factors are randomized within the levels of the veryhardtochange factors. They are not nested within the veryhardtochange factors.
Appears only if the design contains factors with a Continuous or Mixture factor type. 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 Starting Out with DOE.
Specify the number of replicate trials that you want to add to the design. This does not replicate the entire design, but chooses the optimal design points to replicate. See Center Points, Replicate Runs, and Testing in Starting Out with DOE.
A lower bound on the number of runs necessary to avoid failures in design generation. When you select Minimum, the resulting design is saturated. There are no degrees of freedom for error.
Specify the number of runs that you want. Enter that value into the Number of Runs text box. This option enables you to balance the cost of additional runs against the potential gain in information.
Once you have completed the Design Generation outline, click Make Design. Custom Design generates the design, presents it in the Design outline, and provides evaluation information in the Design Evaluation outline. The Output Options panel also appears, allowing you to create the design table.
Note: Sometimes several designs can optimize the optimality criterion. When this is the case, the design algorithm might generate different designs when you click the Back and Make Design buttons repeatedly.
Gives coefficients that indicate the degree by which the model parameters are biased by effects that are potentially active, but not in the model. You specify the terms representing potentially active effects in the Alias Terms table. See The Alias Matrix in Technical Details.
Indicates the optimality criterion used to construct the design. Also gives efficiency measures for your design. See Optimality Criterion in Custom Design Options and Optimality Criteria.
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The Run Order options determine the order of the runs in the design table. Choices include the following:
Click Make Table to construct the custom design data table. In the Custom Design table, the Table panel (in the upper left) can contain scripts, as appropriate given your design. The Model and DOE Dialog scripts are always provided. To run a script, select Run Script from the red triangle menu.
Runs the Analyze > Fit Model platform. The model described by the script is determined by your choices in the Model outline and by the type of design.
Shows model constraints that you entered in the Define Factor Constraints outline using the Specify Linear Constraints option.
Shows model constraints that you entered in the Define Factor Constraints outline using the Use Disallowed Combinations Filter or the Use Disallowed Combinations Script options.