The INEST= data set names a SAS data set that specifies initial estimates for all the parameters in the model.
The INEST= data set must contain the intercept variables (named Intercept
for binary response model and Intercept
, Intercept2
, Intercept3
, and so forth, for multinomial response models) and all independent variables in the MODEL statement.
If BY processing is used, the INEST= data set should also include the BY variables, and there must be at least one observation for each BY group. If there is more than one observation in a BY group, the first one read is used for that BY group.
If the INEST= data set also contains the _TYPE_
variable, only observations with the _TYPE_
value “PARMS” are used as starting values. Combining the INEST= data set and the option MAXIT= in the MODEL statement, partial scoring
can be done, such as predicting on a validation data set by using the model built from a training data set.
You can specify starting values for the iterative algorithm in the INEST= data set. This data set overwrites the INITIAL= option in the MODEL statement, which is a little difficult to use for models with multilevel interaction effects. The INEST= data set has the same structure as the OUTEST= SAS-data-set, but it is not required to have all the variables or observations that appear in the OUTEST= data set. One simple use of the INEST= option is passing the previous OUTEST= data set directly to the next model as an INEST= data set, assuming that the two models have the same parameterization.