| Statistics Toolbox | |
D-optimal design with specified fixed covariates.
Syntax
settings = dcovary(factors,covariates) [settings,X] = dcovary(factors,covariates,'model')
Description
settings = dcovary(factors,covariates,'model')
creates a D-optimal design subject to the constraint of fixed covariates for each run. factors is the number of experimental variables you want to control.
[settings,X] = dcovary(factors,covariates,'model')
also creates the associated design matrix, X. The input, 'model', controls the order of the regression model. By default, dcovary assumes a linear additive model. Alternatively, 'model' can be any of these:
'interaction' - includes constant, linear, and cross product terms.'quadratic' - includes interactions plus squared terms.'purequadratic' - includes constant, linear, and squared terms.Example
Suppose we want to block an eight run experiment into 4 blocks of size 2 to fit a linear model on two factors.
covariates = dummyvar([1 1 2 2 3 3 4 4]);
settings = dcovary(2,covariates(:,1:3),'linear')
settings =
1 1 1 0 0
-1 -1 1 0 0
-1 1 0 1 0
1 -1 0 1 0
1 1 0 0 1
-1 -1 0 0 1
-1 1 0 0 0
1 -1 0 0 0
The first two columns of the output matrix contain the settings for the two factors. The last three columns are dummy variable codings for the four blocks.
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