The arboretum procedure



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Use PROC DECIDE to assign a treatment to each patient.

The cost associated with each treatment is: NOTHING = 0 cost, ANTIBIOT

= 5 for possible bad side effects, and SURGERY = 20 for possible severe side

effects.

proc decide data=outdis out=outdec outstat=sumdec;

   target dx;

   posteriors benign infectio cancer;

   decision decdata=rx

            oldpriorvar=eqprior priorvar=prior

            decvars=nothing antibiot surgery

            cost=     0       5       20;

run;

title2 'Treatment: Cost of surgery=20';



proc print data=sumdec label; run;

proc plot data=outdec; plot y*x=d_rx; run;




The DECIDE Procedure

References

Berger, J. O. (1980), Statistical Decision Theory and Bayesian Analysis, Second Edition, New

York: Springer-Verlag.

Clemen, R. T. (1991), Making Hard Decisions: An Introduction to Decision Analysis, Boston:

PWS-Kent.

DeGroot, M. H. (1970), Optimal Statistical Decisions, New York: McGraw-Hill.

Robert, C. P. (1994), The Bayesian Choice, a Decision Theoretic Motivation, New York:

Springer-Verlag.

Savage, L. J. (1972), The Foundations of Statistics, Second Revised Edition, New York: Dover.

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




The DMDB Procedure

The DMDB Procedure

Overview

Procedure Syntax

PROC DMDB Statement

CLASS Statement

FREQ Statement

ID Statement

TARGET Statement

VARIABLE Statement

WEIGHT Statement



Details

Examples

Example 1: Getting Started with the DMDB Procedure

Example 2: Specifying a FREQ Variable

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




The DMDB Procedure

Overview

SAS/Enterprise Miner architecture is based on the creation of a data mining database (DMDB) that is a

snapshot of the original data. PROC DMDB creates this DMDB from the input data source. It also

compiles and computes metadata information about the input data based on variable roles and stores it in

a metadata catalog. The DMDB and the associated metadata catalog facilitate subsequent data mining

activities. They are both designed for processing and storage efficiencies.



Note:   The DMDBCAT= argument is required for the following procedures: ASSOC, DMDB, DMINE,

DMSPLIT, SPLIT, and SEQ. It is optional in the NEURAL and DMREG procedures. It is not a valid

argument in the RULEGEN and STDIZE procedures.  

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




The DMDB Procedure

Procedure Syntax

PROC DMDB <option(s)>;

CLASS variable(s) <ORDER=order-option(s)>;

ID variable(s);

FREQ variable;

TARGET variable(s);

VARIABLE variable(s) WEIGHT=weight-variable>;

WEIGHT variable;

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




The DMDB Procedure

PROC DMDB Statement

Invokes the DMDB procedure.

PROC DMDB <option(s)>;

Required Arguments

DATA= SAS-data-set

Names the SAS data set containing the information that you want added to the data mining

database.

DMDBCAT=SAS-catalog

Names the metadata catalog that is created or updated by PROC DMDB.

Refer to 

Details


 for more information.

Options

BATCH | NOMETAIN

Specifies the creation of a new metadata catalog as specified in the DMDBCAT= option, instead

of updating an existing metadata catalog. Any existing catalog with this name will be overridden

and replaced by system-generated information.



MAXLEVEL=integer

Specifies the maximum number of class levels to be processed.



Default:

MACINT. (If an integer greater than MACINT is specified, the integer

specified for MAXLEVEL is ignored and MACINT is used.)

Range:

integer   3

OUT= SAS-data-set

Names the data mining database (DMDB) that you want created. The new DMDB contains each

of the ID, VAR, and FREQ variables - copied 'as is' from the DATA= data set. The DMDB also

contains each of the CLASS variables written out as their corresponding integer class level value

in 5-bytes (sizeof (float)+1).

UPDATE= SAS-data-set

Names the previously created data mining database that you want updated with the observations

given in the DATA= data set. At the same time, these observations update the information

maintained in the DMDBCAT= option, reflecting the revised statistics from the additional input




records.

VARDEF=divisor

Specifies the divisor to use in the calculation of the variance and standard deviation. The

following table shows the possible values for VARDEF:

Values for VARDEF=

Value

Divisor

Formula

DF

degrees of



freedom

n- 1

N

number of



observations

n

WDF


sum of

weights


minus one

(

w



i

) -


1

WEIGHT|WGT

sum of

weights


w

i

Default:

DF (degrees of freedom)

Tip:

When you use the WEIGHT statement, you can specify VARDEF=WDF to

get an estimate of the variance of an observation using the average

observation weight.



CAUTION:

Using a Divisor with the WEIGHT statement. If you use a WEIGHT statement and you

calculate the variance or standard deviation, you may want to specify VARDEF=WEIGHT.

Use WEIGHT as the divisor to compute an approximate estimate of the variance of an

observation using the average observation weight. Use VARDEF=DF to compute the

standard error of the weighted mean.  

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






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