The arboretum procedure

DMREG Response Profile

The Response Profile table lists the target categories, their ordered values, and their total frequencies for the given data.

DMREG Optimization Table

The Optimization table provides a summary of the Newton-Raphson Ridge optimization results.

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DMREG Model Fitting Information and Testing Global Null Hypothesis Beta=0

The Model Fitting Information and Testing Global Null Hypothesis Beta=0 table contains the negative of twice the log likelihood (-2 LOG

L) for the fitted model. Results of the likelihood ratio test and the efficient score test for testing the joint significance of the explanatory

inputs are also printed in the table.

DMREG Analysis of Maximum Likelihood Estimates

The Analysis of Maximum Likelihood Estimates table lists the parameter estimates, their standard errors, and the results of the Wald test for

the individual parameters. A standardized estimate for each slope parameter and the odds ratio for each estimate is also printed. An odds ratio

is obtained by exponentiating the corresponding parameter estimate.

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DMREG Odds Ratio Estimates

The Odd Ratio Estimates table lists the odd ratios for the explanatory inputs. The odd ratio estimates provide the change in odds for a unit

increase in each input.

PROC PRINT Report of Selected Fit Statistics for the Training Data Set

The misclassification rate for the training data set is only 37.22%.

PROC FREQ Misclassification Table for the Training Data

All observations in the training data are classified into the C=3 level. The linear model is not adequate.

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PROC GPLOT Plot of the Classification Results

The target classes are not linearly separable.

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PROC GCONTOUR Plots of the Posterior Probabilities

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Quadratic-Logistic Program

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

   class c;

   model c=x|x|y|y @2;

   score out=qout outfit=qfit;

   score data=sampsio.dmsring nodmdb out=qgridout;

   title1 'Quadratic-Logistic Regression with Ordinal Target';

run;

 

proc print data=qfit noobs label;

   var _aic_ _max_ _rfpe_ _misc_;

  title2 'Fit Statistics for the Training Data Set';

 run;

 

proc freq data=qout;

   tables f_c*i_c;

   title2 'Misclassification Table: Training Data';

run;

 

proc gplot data=qout;

   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=qgridout;

   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;

Quadratic-Logistic Output

DMREG Output

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PROC PRINT Report of Selected Fit Statistics for the Training Data

Note that the training misclassification rate is 0. All cases are correctly classified by the quadratic-logistic model.

PROC FREQ Misclassification Table for the Training Data

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PROC GPLOT Plot of the Classification Results

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PROC GCONTOUR Plots of the Posterior Probabilities

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

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PROC GPLOT creates a scatter plot of the Rings training data.

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;

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The PROC DMREG statement invokes the procedure. The DATA= option identifies

the DMDB encoded training data set that is used to fit the model. The DMDBCAT=option

identifies the DMDB training data catalog. You can create DMDB encoded data

sets and catalogs with the DMDB procedure.

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

            

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The CLASS statement identifies the target C as a categorical variable.

class c;

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The MODEL statement specifies the linear-logistic model.

model c = x y;

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