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_TASEL_

Test: Lower

95% Confidence

Limit for TASE

_TASEU_

Test: Upper 95%



Confidence

Limit for TASE

_TAVERR_ Test: Average

Error Function

_TDIV_

Test: Divisor for



TASE

_TERR_


Test: Error

Function


_TMAX_

Test: Maximum

Absolute Error

_TMSE_


Test: Mean

Square Error

_TNOBS_

Test: Sum of



Frequencies

_TRASE_


Test: Root

Average


Squared Error

TRMSE_


Test: Root Mean

Square Error

_TSSE_

Test: Sum of



Square Errors

_TSUMW_


Test: Sum of

Case Weights

Times

Frequency



_TMISC_

Test:


Misclassification

Rate



_TMISL_

Test: Lower

95% Confidence

Limit for

TMISC

_TMISU_


Test: Upper 95%

Confidence

Limit for

TMISC


Fit Statistics for the Validation

Data

Fit Statistic

Validation Data

_VASE_


Valid: Average

Squared Error

_VAVERR_ Valid: Average

Error Function

_VDIV_

Valid: Divisor



for VASE

_VERR_


Valid: Error

Function


_VMAX_

Valid:


Maximum

Absolute Error

_VMSE_

Valid: Mean



Square Error

_VNOBS_


Valid: Sum of

Frequencies

_VRASE_

Valid: Root



Average

Squared Error

_VRMSE_

Valid: Root



Mean Square

Error


_VSSE_

Valid: Sum of

Square Errors



_VSUMW_

Valid: Sum of

Case Weights

Times


Frequency

_VMISC_


Valid:

Misclassification

Rate

Chapter Contents



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




The DMREG Procedure

Examples

The following examples were executed using the HP-UX version 10.20 operating system and the SAS

software release 6.12TS045.

Example 1: Linear and Quadratic Logistic Regression with an Ordinal Target (Rings

Data)

Example 2: Performing a Stepwise OLS Regression (DMREG Baseball Data)

Example 3: Comparison of the DMREG and LOGISTIC Procedures when Using a

Categorical Input Variable

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




The DMREG Procedure

Example 1: Linear and Quadratic Logistic Regression with an Ordinal

Target (Rings Data)

Features

Scoring a Test Data Set

q   

Outputting Fit Statistics



q   

Creating a Classification Table

q   

Plotting the Posterior Probabilities



q   

This example demonstrates how to perform both a linear and a quadratic logistic regression with an ordinal target. The example DMDB

training data set SAMPSIO.DMDRING contains an ordinal target with 3 levels (C= 0, 1, or 2) and two continuous inputs (X and Y). There

are 180 observations in the data set. The SAMPSIO.DMSRING data set is scored using the scoring formula from the trained models. Both

data sets are stored in the sample library.

Linear-Logistic Program

 

proc gplot data=sampsio.dmdring;



   plot y*x=c /haxis=axis1 vaxis=axis2;

   symbol c=black i=none v=dot;

   symbol2 c=red i=none v=square;

   symbol3 c=green i=none v=triangle;

   axis1 c=black width=2.5  order=(0 to 30 by 5);

   axis2 c=black width=2.5 minor=none order=(0 to 20 by 2);

   title 'Plot of the Rings Training Data';

run;


 

proc dmreg data=sampsio.dmdring dmdbcat=sampsio.dmdring;

            

 

class c;



 

model c = x y;

 

 score out=out outfit=fit;



 

score data=sampsio.dmsring nodmdb out=gridout;

     title 'Linear-Logistic Regression with Ordinal Target';

run;


 

proc print data=fit noobs label;

   var _aic_ _max_ _rfpe_ _misc_ ;

   title2 'Fit Statistics for the Training Data Set';

 run;

 

proc freq data=out;



   tables f_c*i_c;

   title2 'Misclassification Table: Training Data';

run;



 

proc gplot data=out;

   plot y*x=i_c / haxis=axis1 vaxis=axis2;

   symbol  c=black i=none v=dot;

   symbol2 c=red i=none v=square;

   symbol3 c=green i=none v=triangle;

   axis1 c=black width=2.5 order=(0 to 30 by 5);

   axis2 c=black width=2.5 minor=none order=(0 to 20 by 2);

   title2 'Classification Results';

run;


 

proc gcontour data=gridout;

   plot y*x=p_c1 / pattern ctext=black coutline=gray;

   plot y*x=p_c2 / pattern ctext=black coutline=gray;

   plot y*x=p_c3 / pattern ctext=black coutline=gray;

   title2 'Posterior Probabilities';

   pattern v=msolid;

   legend frame;

run;

Linear-Logistic Output

PROC GPLOT Plot of the Rings Training Data

DMREG Summary Profile Information

PROC DMREG first lists background information about the fitting of the linear-logistic model. Included are the name of the input data set,

the response variable, the number of response levels, the number of observations used, the error distribution, and the link function.




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