ABC--XYZ, or PQR:.
All costs are assumed to be 0.
Specifies the variable in the DECDATA= data set that contains the prior probabilities to use for
Specifies the frequency variable. If specified, the FREQ variable overrides whatever is in the
DMDB metadata. If the FREQ statement contains no name, then a FREQ variable is not used.
If there is a frequency variable in the DMDB, it is not advisable to use another
variable as a frequency variable because the training data does not contain
observations with invalid values in the FREQ variable specified in the DMDB. For
example, if the frequency variable specified in the DMDB contains a 0 or negative
value, then that observation is discarded even if the FREQ variable that you specified
in the FREQ statement of the DMREG procedure contains valid frequency values.
If the FREQ statement is not specified, the frequency variable in the DMDB
is used. If the FREQ statement is specified without a variable, a frequency of
1 is used for all observations.
The frequency variable can contain integer or non-integer values.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
where the arguments are defined as follows:
Specifies the response variable (target).
Specifies the explanatory variables or effects (inputs). The syntax of effects is described in .
Specifies options that affect the fit, confidence intervals, variable selection, and specification of
the model as follows:
MODEL Options - Fitting Options
Specifies the critical misclassification rate at which to stop iterations.
0 - 1
Specifies the number of iterations to be processed before checking misclassification rate.
Depends on the optimization technique:
TECHNIQUE = NEWRAP, NRRIDG, TRUREG
TECHNIQUE = QUANEW, DBLDOG
TECHNIQUE = CONGRA
Specifies the significance level of confidence intervals for regression parameters.
Specifies the computation of confidence intervals for parameters.
Specifies that the correlation matrix is to be printed.
Specifies that the covariance matrix is to be printed.
Specifies the criterion for the selection of the model.
Represents the Akaike Information Criterion. The model with the smallest criterion value is
Represents the total profit/loss for the training data. The model with the largest profit or the
smallest loss is chosen.
Represents the total profit/loss for the VALIDATA= data set. The model with the largest
profit or the smallest loss is chosen.
errors for least-square regression and negative log-likelihood for logistic regression. The
model with the smallest error rate is chosen.
smallest misclassification rate is chosen.
Represents the total profit/loss for cross-validation of the training data. The model with the
largest profit or the smallest loss is chosen.
least-square regression and negative log-likelihood for logistic regression. The model with
the smallest error rate is chosen.
misclassification rate is chosen.
If decision processing is specified, the default is CHOOSE=TDECDATA; if
the VALIDATA= data set is also specified, the default is
Prints details at each model selection step.
Specifies how containment is to be applied.
Specifies that all independent variables that meet hierarchical requirements are included in
Specifies that the first n effects in the model are to be included in each model.