PROC OPTEX: MODEL Statement

The OPTEX Procedure

MODEL Statement

MODEL effects </ options> ;

You use the MODEL statement to specify the independent effects used to model data that are to be collected with the design that is being constructed. The effects can be

simple continuous regressor effects
polynomial continuous effects
main effects of classification variables
interactions of classification variables
continuous-by-class effects

The variables used to form effects in the MODEL statement must be present in all input data sets. For details on input data sets, see the section Input Data Sets. For details on the specification of different types of effects and on how the design matrix is defined with respect to the effects, see the section Specifying Effects in MODEL Statements.

If you specify a data set containing fixed covariate effects with a DESIGN= data set in the BLOCKS statement, then a CLASS or MODEL statement that follows the BLOCKS statement refers to the model for the fixed covariates. A CLASS or MODEL statement that defines the model for the candidate points (treatment model) should occur before the BLOCKS statement.

The following options can be used in the MODEL statement:

NOINT

excludes the intercept parameter from the model. By default, the OPTEX procedure includes the intercept parameter in the model.

PRIOR=num-list

specifies prior precision values corresponding to groups of effects in the model. Groups of effects in the MODEL statement with the same prior precision must be separated by commas. Then use the PRIOR= option, listing as many prior precision values as there are groups of effects. See Example 10.6 for an example.

When you specify prior precision values, the information matrix for estimating the linear parameters is $\text{[math]}$ , where $\text{[math]}$ is the design matrix and $\text{[math]}$ is a diagonal matrix with the prior precision values that you specify on the diagonal. Thus, in terms of a prior distribution, the inverses of the prior precision values can be interpreted as prior variances for the linear parameters corresponding to each effect. As an alternative interpretation, note that with orthogonal coding the value of the prior for an effect says roughly how many prior "observations’ worth" of information you have for that effect. See the section Design Coding for details on orthogonal coding.

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