The arboretum procedure

The DMREG Procedure

Overview

DMREG enables you to fit both linear and logistic regression models. Linear regression attempts to

predict the value of a continuous target as a linear function of one or more independent inputs. Logistic

regression attempts to predict the probability that a categorical (binary, ordinal, or nominal) target will

acquire the event of interest as a function of one or more independent inputs. The procedure supports

forward, backward, and stepwise selection methods. It also allows you to score data sets or generate SAS

DATA step code to score a data set.

Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.

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The DMREG Procedure

Procedure Syntax

PROC DMREG < option(s)>;

MODEL dependent=independent(s) </ model-option(s)>;

CLASS variable(s);

CODE code-option(s);

DECISION DECDATA=<libref.>SAS-data-set<DECVARS=decision-variable(s)>

<option(s)>;

FREQ variable;

NLOPTIONS nonlinear-option(s);

REMOTE remote-option(s);

SCORE scoring-option(s);

Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.

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The DMREG Procedure

PROC DMREG Statement

Invokes the DMREG procedure.

PROC DMREG <option(s)>;

Required Arguments

DATA= SAS-data-set

Identifies the training data set.

DMDBCAT= SAS-catalog

Identifies the training data catalog.

Options

COVOUT

Specifies that the OUTEST= data set is to include the variance-covariance matrix of the parameter

estimates.

DESCENDING

Specifies that the order of categorical target is to be reversed.

ESTITER=n

Specifies that the OUTEST= data set contains parameter estimates and fit statistics (for training,

test, and validation data) for every nth iteration.

Default:

0. Only the parameter estimates of the final iteration are output.

INEST= SAS-data-set

Identifies the data set that contains initial estimates.

MINIMAL

Specifies the use of minimal resources to fit a logistic regression model. Memory for the Hessian

matrix is not needed. The optimization defaults to the conjugate gradient technique and standard

errors of the regression parameters are not computed. Model selection is disabled when this option

is specified. This option does not apply to the normal error regression models.

NAMELEN=n

Specifies the length of effect names in the printed output to be n characters, where n is a value

between 20 and 200. The default length is 20 characters.

OUTEST= SAS-data-set

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Identifies the output data set containing estimates and fit statistics. See for more information.

NOPRINT

Suppresses all printed output.

SIMPLE

Prints simple descriptive statistics of the input variables.

TESTDATA= SAS-data-set

Identifies the data set containing test data.

VALIDATA= SAS-data-set

Identifies the data set containing validation data.

Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.

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The DMREG Procedure

CLASS Statement

Specifies one or more categorical variables to be used in the analysis.

CLASS variable(s);

Required Argument

variable(s)

Specifies a list of categorical variables to be used in the analysis. You must specify the target

variable if it has a categorical (binary, ordinal, or nominal) measurement level.

Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.

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The DMREG Procedure

CODE Statement

Controls the creation of SAS code that can be used to score data sets.

Tip: If neither FILE= nor METABASE= is specified, then the SAS code is written to the SAS log.

CODE <code-option(s)>;

CODE Options

ERROR

Specifies that the error function is to be computed.

FILE=

Specifies the path for writing the code to an external file. For example,

FILE="c:\mydir\scorecode.sas".

FORMAT=

Specifies numeric formats for printing the estimated parameters.

GROUP=

Specifies the group identifier (up to four characters) for group processing.

METABASE=mylib.mycat.myentry

Specifies the code catalog entry to which the results are written.

RESIDUAL

Specifies that residuals are to be computed.

Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.

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The DMREG Procedure

DECISION Statement

Specifies information used for decision processing in the DECIDE, DMREG, NEURAL, and

SPLIT procedures. This documentation applies to all four procedures.

Tip: The DECISION statement is required for the DMREG and NEURAL procedures. It is optional

for PROC SPLIT.

DECISION DECDATA= SAS-data-set <DECVARS=decision-variable(s)><option(s)>;

DECDATA=  SAS-data-set

Specifies the input data set that contains the decision matrix. The DECDATA= data set must

contain the target variable.

Note:   The DECDATA= data set may also contain decision variables specified by means of the

DECVARS= option, and prior probability variable(s) specified by means of the PRIORVAR=

option.

The target variable is specified by means of the TARGET statement in the DECIDE, NEURAL,

and SPLIT procedures or by using the MODEL statement in the DMREG procedure. If the target

variable in the DATA= data set is categorical then the target variable of the DECDATA= data set

should contain the category values, and the decision variables will contain the common

consequences of making those decisions for the corresponding target level. If the target variable is

interval, then each decision variable will contain the value of the consequence for that decision at

a point specified in the target variable. The unspecified regions of the decision function are

interpolated by a piecewise linear spline.  

Tip:

The DECDATA= data set may be of TYPE=LOSS, PROFIT, or REVENUE.

If unspecified, TYPE=PROFIT is assumed by default. TYPE= is a data set

option that should be specified when the data set is created.

DECVARS=decision-variable(s)

Specifies the decision variables in the DECDATA= data set that contain the target-specific

consequences for each decision.

Default:

None

COST=cost-option(s)

Specifies numeric constants that gives the cost of a decision, or variables in the DATA= data set

that contain the case-specific costs, or any combination of constants and variables. There must be

the same number of cost constants and variables as there are decision variables in the DECVARS=

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