Statistics Toolbox

daugment

D-optimal augmentation of an experimental design

Syntax

• settings = daugment(startdes,nruns)
  [settings,X] = daugment(startdes,nruns)
  [settings,X] = daugment(startdes,nruns,'model')
  [settings, X] = daugment(...,'param1',value1,'param2',value2,...)
  

Description

settings = daugment(startdes,nruns) adds nruns runs to an experimental design using the coordinate exchange D-optimal algorithm. startdes is a matrix of factor settings in the original design. The output matrix settings is the matrix of factor settings for the design.

[settings,X] = daugment(startdes,nruns) also generates the associated design matrix, X.

[settings,X] = daugment(startdes,nruns,'model') also controls the order of the regression model. The input, 'model', can be one of these:

'linear'	Includes constant and linear terms (the default)
'interaction'	Includes constant, linear, and cross-product terms.
'quadratic'	Includes interactions and squared terms.
'purequadratic'	Includes constant, linear and squared terms.

Alternatively model can be a matrix of term definitions as accepted by the x2fx function.

[settings, X] = daugment(...,'param1',value1,'param2',value2,...) provides more control over the design generation through a set of parameter/value pairs. Valid parameters are the following::

Parameter	Value
'display'	Either 'on' or 'off' to control display of iteration counter. The default is 'on'.
'init'	Initial design as an nruns-by-nfactors matrix. The default is a randomly selected set of points.
'maxiter'	Maximum number of iterations. The default is 10.

Example

We add 5 runs to a 2² factorial design to allow us to fit a quadratic model.

• startdes = [-1 -1; 1 -1; -1 1; 1 1];
  settings = daugment(startdes,5,'quadratic')
  settings =
      -1    -1
       1    -1
      -1     1
       1     1
       1     0
      -1     0
       0     1
       0     0
       0    -1
  

The result is a 3² factorial design.

See Also
cordexch, x2fx

crosstab

dcovary