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D-Optimal Designs

Computer-generated designs for efficient data collection for a model you specify


candexch D-optimal design from candidate set using row exchanges
candgen Candidate set generation
cordexch Coordinate exchange
daugment D-optimal augmentation
dcovary D-optimal design with fixed covariates
rowexch Row exchange
rsmdemo Interactive response surface demonstration

Examples and How To

Generate D-Optimal Designs

Two Statistics and Machine Learning Toolbox™ algorithms generate D-optimal designs:

Augment D-Optimal Designs

In practice, you may want to add runs to a completed experiment to learn more about a process and estimate additional model coefficients.

Specify Fixed Covariate Factors

In many experimental settings, certain factors and their covariates are constrained to a fixed set of levels or combinations of levels.

Specify Categorical Factors

Categorical factors take values in a discrete set of levels.

Specify Candidate Sets

The row-exchange algorithm exchanges rows of an initial design matrix X with rows from a design matrix C evaluated at a candidate set of feasible treatments.


Introduction to D-Optimal Designs

Traditional experimental designs (Full Factorial Designs (Statistics and Machine Learning Toolbox), Fractional Factorial Designs (Statistics and Machine Learning Toolbox), and Response Surface Designs (Statistics and Machine Learning Toolbox)) are appropriate for calibrating linear models in experimental settings where factors are relatively unconstrained in the region of interest.

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