The input to the DMREG procedure can be assigned one of these roles:
The DATA= data set is used to fit the initial model.
The VALIDATA= data set is used to compute assessment statistics and to fine-tune the model
during stepwise selection.
The DATA= data set in the SCORE statement is used for predicting target values for a new data
Different types of effects can be used in the DMREG procedure. In the following list, assume that A, B,
and C are class variables and that X1, X2, and Y are continuous variables:
Regressor effects are specified by writing continuous variables individually:
Main effects are specified by writing class variables individually:
Continuous-by-class effects are written by joining continuous variables and class variables with
The following table provides a list of the general nonlinear optimization methods and the default
maximum number of iterations and function calls for each method.
Optimization Methods for the Regression
with Line Search
Small-to-medium problems - The Trust-Region, Newton-Raphson with Ridging, and
Newton-Raphson with Line Search methods are appropriate for small and medium sized
optimization problems (number of model parameters up to 40) where the Hessian matrix is easy
and cheap to compute. Sometimes, Newton-Raphson with Ridging can be faster than
Trust-Region, but Trust-Region is numerically more stable. If the Hessian matrix is not singular at
the optimum, then the Newton-Raphson with Line Search can be a very competitive method.
optimization problems (number of model parameters up to 400) where the objective function and
the gradient are must faster to compute than the Hessian. Quasi-Newton and Double Dogleg
require more iterations than does the Trust-Region or the Newton-Raphson methods, but each
iteration is much faster.
(number of model parameters greater than 400) where the objective function and the gradient are
much faster to compute than the Hessian matrix, and where they need too much memory to store
the approximate Hessian matrix.
Note: To learn about these optimization methods, see the SAS/OR Technical Report: The NLP
The underlying "Default" optimization entry method depends on the number of parameters in the model.
with Ridging. If the number of parameters is greater than 40 and less than 400, then the default method is
set to quasi-Newton. If the number of parameters is greater than 400, then Conjugate Gradient is the
The OUTEST= data set in the PROC DMREG statement contains fit statistics for the training, test,
and/or validation data. Depending on the ROLE= option in the SCORE statement, the OUTFIT= data set
contains fit statistics for either the training, test, or validation data.
_AVERR_ Train: Average
Average Sum of
Train: Sum of