Use the Custom Design platform to construct an optimal design custom built for your specific experimental needs.
You can include a wide range of factor types, including the following:
• Continuous
• Discrete numeric (with any number of levels)
• Categorical (with any number of levels)
• Blocking (with an specified number of runs per block)
• Covariate
• Mixture
• Constant
• Uncontrolled
You can restrict your experimental region to reflect your operating conditions using linear factor constraints or disallowed combinations. In particular, restrictions can be specified for categorical, continuous, and discrete numeric factors. See Define Factor Constraints.
For continuous, discrete numeric, categorical, and mixture factors, you can indicate two levels of difficult-to-change factors. These difficulty levels are represented by whole plots or whole plots and split plots. You can also specify hard-to-change covariates.
You can explicitly specify your assumed model. Your assumed model is an initial model that ideally contains all the effects that you want to estimate. Your model can contain any combination of main effects, interactions, response surface effects, and polynomial effects (up to the fifth power). You can specify the effects for which estimability is necessary and those for which estimability is desired. Custom Design uses a Bayesian optimality approach to estimate effects whose estimability is desired, subject to the number of runs. See Model.
The Custom Design platform enables you to specify the number of runs that matches the budget for your experimental situation. The platform indicates the minimum number of runs that can be used to estimate the required effects and provides a default number of runs. These values can serve as a guide for determining a feasible number of runs. See Design Generation.
Custom Design can construct a wide variety of design types. These include classical designs and random block designs. For examples of different design types, see Examples of Custom Designs.
Given your specific requirements, the Custom Design platform constructs a design that is optimal. The algorithm supports several optimality criteria:
• D optimality
• I optimality
• Bayesian D and I optimality (using If Possible effects)
• A optimality
• Alias optimality
See Optimality Criteria.
Designs are constructed using the coordinate-exchange algorithm (Meyer and Nachtsheim, 1995). See Coordinate-Exchange Algorithm.