Definition:
The DMINE procedure organizes numeric variables into 16 equally-spaced
groups or bins called AOV16 variables. The AOV16 variables are created to
help identify non-linear relationships with the target. Bins that have zero
observations are eliminated; therefore, an AOV16 variable can have fewer
than 16 bins.
Default:
Create the AOV16 variables. Note that there is not an AOV16 option, only a
NOAOV16 option to prevent these variables from being used in the final
forward stepwise selection process.
NOINTER
Specifies not to consider interactions between categories (that is, a two-way interaction) of
CLASS variables in the process of variable selection.
Definition:
A two-way interaction measures the effect of a classification input variable
across the levels of another classification variable. For example, credit
worthiness may not be consistent across job classifications. The lack of
uniformity in the response may signify a credit worthiness by job interaction.
Default:
Two-way interactions between categories of the class variables are
considered in the variable selection process. Note that the two-way
interactions can dramatically increase the processing time of the DMINE
procedure.
MAXROWS=value
Specifies the upper bound for the number of independent variables selected for the model. This is
an upper bound for the number of rows and columns of the X'X matrix of the regression problem.
Default:
3000. This means that for most models, the MINR2 and STOPR2 settings
will determine the number of selected independent variables. The X'X matrix
used for the stepwise regression requires
double precision
values storage in RAM, where n is the number of rows in the matrix. (This
corresponds to 3000 * 1500 * 8 bytes (which is about 36 megabytes) of RAM
needed for storage.)
MINR2=value
Specifies a lower bound for the individual R-square value of a variable to be eligible for the model
selection process. Variables with R-square values greater than or equal to value are included in the
selection process.
Definition:
R-square is the ratio of the model sum of squares (SS) to the total sum of
squares. It measures the sequential improvement in the model as input
variables are selected.
Default:
NOMONITOR
Suppresses the output of the status monitor that indicates the progress made in the computations.
Default:
The output of the status monitor is displayed.
NOPRINT
Suppresses all output printed in the output window.
Default:
The output is printed to the output window.
STOPR2=value
Specifies a lower value for the incremental model R-square value at which the variable selection
process is stopped.
Default:
USEGROUPS
PROC DMINE automatically tries to reduce the levels of each class variable to a group variable
based on the relationship with the target. By doing so, observations of class variables with many
categories (for example, ZIP codes) can be mapped into groups of fewer categories. If you specify
the USEGROUPS option, and a class variable can be reduced to a group variable, then only the
group version of the variable is considered in the model. If you omit the USEGROUPS option,
then both the group variable and the original class variable are allowed in the model.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The DMINE Procedure
FREQ Statement
Alias: FREQUENCY
Tip: Specify the FREQ variable in PROC DMDB so that the information is saved in the catalog and
so that the variable is automatically used as a FREQ variable in PROC DMINE. This also
ensures that the FREQ variable is automatically used by all other Enterprise Miner procedures
in the project.
FREQ variable;
Required Argument
variable
Specifies one numeric (interval-scaled) FREQUENCY variable.
Range:
Any integer. A noninteger value is truncated.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The DMINE Procedure
TARGET Statement
TARGET variable;
Required Argument
variable
Specifies the output variable. One variable name can be specified identifying the target (response)
variable for the two regressions.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The DMINE Procedure
VARIABLES Statement
Alias: VAR
VARIABLES variable-list;
Required Argument
variable-list
Specifies all the variables (numeric and categorical, that is, INTERVAL and CLASS) that can be
used for independent variables in the prediction or modeling of the target variable.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.