Custom Design Flow
Responses Outline
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, double-click it and enter the desired name.
Factors Outline
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, double-click it and enter the desired name.
To remove a factor level, click the value, click Delete, and click outside the text box.
To modify the entry under Changes, click the value in the Changes column and select the appropriate entry.
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 k-1. 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.
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 split-plot designs, see Jones and Goos (2007). For details relating to designs with hard-to-change covariates, see Jones and Goos (2014, to appear).
Factors and Design Generation Outline for a Split-Split-Plot Design shows a split-split-plot scenario, using the factors from the Cheese Factors.jmp sample data table (located in the Design Experiment folder).
Factors and Design Generation Outline for a Split-Split-Plot Design
Each level of Whole Plots corresponds to a block of constant settings of the hard-to-change factors.
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 split-split-plot design:
Each level of Subplots corresponds to a block of constant settings of the hard-to-change factors.
Each level of Whole Plots corresponds to a block of constant settings of the very-hard-to-change factors.
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 two-way split-plot 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 hard-to-change factor with the levels of the very-hard-to-change factor. See Two-Way Split-Plot Designs.
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).
To remove a single factor, select Delete from its red triangle menu.
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.
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.
Enter the expression (Exp(X1) + 2*X2 < 0) & (X3 == 2) into the script window.
Expression in Script Editor
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.
Model Outline
The Bayesian D-Optimal 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 D-Optimality.
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.
Alias Terms Outline
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.
Alias Matrix
Design Generation Outline
(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 very-hard-to-change factor. The factor Whole Plots corresponds to the very-hard-to-change factors (split-split-plot design), if there are any, otherwise to the hard-to-change factors (split-plot 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 very-hard-to-change factor. The factor Subplots corresponds to the hard-to-change factors in the split-split-plot 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.
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.
Output Options Panel
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.
Custom Design Table Showing Scripts
Runs the Analyze > Modeling > Screening platform.
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 Use Disallowed Combinations Filter or the Use Disallowed Combinations Script options.