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The DISCRIM Procedure

Example 31.1 Univariate Density Estimates and Posterior Probabilities

In this example, several discriminant analyses are run with a single quantitative variable, petal width, so that density estimates and posterior probabilities can be plotted easily. The example produces Output 31.1.1 through Output 31.1.5. ODS Graphics is used to display the sample distribution of petal width in the three species. For general information about ODS Graphics, see Chapter 21, Statistical Graphics Using ODS. Note the overlap between the species I. versicolor and I. virginica that the bar chart shows. The following statements produce Output 31.1.1: 

title 'Discriminant Analysis of Fisher (1936) Iris Data';

proc format;
   value specname
      1='Setosa    '
      2='Versicolor'
      3='Virginica ';
run;

data iris;
   input SepalLength SepalWidth PetalLength PetalWidth
         Species @@;
   format Species specname.;
   label SepalLength='Sepal Length in mm.'
         SepalWidth ='Sepal Width in mm.'
         PetalLength='Petal Length in mm.'
         PetalWidth ='Petal Width in mm.';
   datalines;
50 33 14 02 1 64 28 56 22 3 65 28 46 15 2 67 31 56 24 3
63 28 51 15 3 46 34 14 03 1 69 31 51 23 3 62 22 45 15 2
59 32 48 18 2 46 36 10 02 1 61 30 46 14 2 60 27 51 16 2

   ... more lines ...   

63 33 60 25 3 53 37 15 02 1
;

proc freq data=iris noprint;
   tables petalwidth * species / out=freqout;
run;

proc sgplot data=freqout;
   vbar petalwidth / response=count group=species;
   keylegend / location=inside position=ne noborder across=1;
run;

Output 31.1.1 Sample Distribution of Petal Width in Three Species
Sample Distribution of Petal Width in Three Species

In order to plot the density estimates and posterior probabilities, a data set called plotdata is created containing equally spaced values from –5 to 30, covering the range of petal width with a little to spare on each end. The plotdata data set is used with the TESTDATA= option in PROC DISCRIM. The following statements make the data set: 

data plotdata;
   do PetalWidth=-5 to 30 by 0.5;
      output;
   end;
run;

The same plots are produced after each discriminant analysis, so macros are used to reduce the amount of typing required. The macros use two data sets. The data set plotd, containing density estimates, is created by the TESTOUTD= option in PROC DISCRIM. The data set plotp, containing posterior probabilities, is created by the TESTOUT= option. For each data set, the macros remove uninteresting values (near zero) and create an overlay plot showing all three species in a single plot.

The following statements create the macros: 

%macro plotden;
   title3 'Plot of Estimated Densities';

   data plotd2;
      set plotd;
      if setosa     < .002 then setosa     = .;
      if versicolor < .002 then versicolor = .;
      if virginica  < .002 then virginica  = .;
      g = 'Setosa    '; Density = setosa;     output;
      g = 'Versicolor'; Density = versicolor; output;
      g = 'Virginica '; Density = virginica;  output;
      label PetalWidth='Petal Width in mm.';
   run;

   proc sgplot data=plotd2;
      series y=Density x=PetalWidth / group=g;
      discretelegend;
   run;
   %mend;

%macro plotprob;
   title3 'Plot of Posterior Probabilities';

   data plotp2;
      set plotp;
      if setosa     < .01 then setosa     = .;
      if versicolor < .01 then versicolor = .;
      if virginica  < .01 then virginica  = .;
      g = 'Setosa    '; Probability = setosa;     output;
      g = 'Versicolor'; Probability = versicolor; output;
      g = 'Virginica '; Probability = virginica;  output;
      label PetalWidth='Petal Width in mm.';
   run;

   proc sgplot data=plotp2;
      series y=Probability x=PetalWidth / group=g;
      discretelegend;
   run;
%mend;

The first analysis uses normal-theory methods (METHOD=NORMAL) assuming equal variances (POOL=YES) in the three classes. The NOCLASSIFY option suppresses the resubstitution classification results of the input data set observations. The CROSSLISTERR option lists the observations that are misclassified under cross validation and displays cross validation error-rate estimates. The following statements produce Output 31.1.2: 


title2 'Using Normal Density Estimates with Equal Variance';

proc discrim data=iris method=normal pool=yes
             testdata=plotdata testout=plotp testoutd=plotd
             short noclassify crosslisterr;
   class Species;
   var PetalWidth;
run;

%plotden;
%plotprob;

Output 31.1.2 Normal Density Estimates with Equal Variance
Discriminant Analysis of Fisher (1936) Iris Data
Using Normal Density Estimates with Equal Variance

The DISCRIM Procedure

Total Sample Size 150 DF Total 149
Variables 1 DF Within Classes 147
Classes 3 DF Between Classes 2

Number of Observations Read 150
Number of Observations Used 150

Class Level Information
Species Variable
Name		 Frequency Weight Proportion Prior
Probability
Setosa Setosa 50 50.0000 0.333333 0.333333
Versicolor Versicolor 50 50.0000 0.333333 0.333333
Virginica Virginica 50 50.0000 0.333333 0.333333

Discriminant Analysis of Fisher (1936) Iris Data
Using Normal Density Estimates with Equal Variance

The DISCRIM Procedure
Classification Results for Calibration Data: WORK.IRIS
Cross-validation Results using Linear Discriminant Function

Posterior Probability of Membership in Species
Obs From Species Classified into
Species		 Setosa Versicolor Virginica
5 Virginica Versicolor * 0.0000 0.9610 0.0390
9 Versicolor Virginica * 0.0000 0.0952 0.9048
57 Virginica Versicolor * 0.0000 0.9940 0.0060
78 Virginica Versicolor * 0.0000 0.8009 0.1991
91 Virginica Versicolor * 0.0000 0.9610 0.0390
148 Versicolor Virginica * 0.0000 0.3828 0.6172

* Misclassified observation


Discriminant Analysis of Fisher (1936) Iris Data
Using Normal Density Estimates with Equal Variance

The DISCRIM Procedure
Classification Summary for Calibration Data: WORK.IRIS
Cross-validation Summary using Linear Discriminant Function

Number of Observations and Percent Classified
into Species
From Species Setosa Versicolor Virginica Total
Setosa
50
100.00
0
0.00
0
0.00
50
100.00
Versicolor
0
0.00
48
96.00
2
4.00
50
100.00
Virginica
0
0.00
4
8.00
46
92.00
50
100.00
Total
50
33.33
52
34.67
48
32.00
150
100.00
Priors
0.33333
 
0.33333
 
0.33333
 
 
 

Error Count Estimates for Species
  Setosa Versicolor Virginica Total
Rate 0.0000 0.0400 0.0800 0.0400
Priors 0.3333 0.3333 0.3333  

Discriminant Analysis of Fisher (1936) Iris Data
Using Normal Density Estimates with Equal Variance

The DISCRIM Procedure
Classification Summary for Test Data: WORK.PLOTDATA
Classification Summary using Linear Discriminant Function

Observation Profile for Test Data
Number of Observations Read 71
Number of Observations Used 71

Number of Observations and Percent Classified
into Species
  Setosa Versicolor Virginica Total
Total
26
36.62
18
25.35
27
38.03
71
100.00
Priors
0.33333
 
0.33333
 
0.33333
 
 
 

disx1fdisx1f, continued

The next analysis uses normal-theory methods assuming unequal variances (POOL=NO) in the three classes. The following statements produce Output 31.1.3: 

title2 'Using Normal Density Estimates with Unequal Variance';

proc discrim data=iris method=normal pool=no
             testdata=plotdata testout=plotp testoutd=plotd
             short noclassify crosslisterr;
   class Species;
   var PetalWidth;
run;

%plotden;
%plotprob;

Output 31.1.3 Normal Density Estimates with Unequal Variance
Discriminant Analysis of Fisher (1936) Iris Data
Using Normal Density Estimates with Unequal Variance

The DISCRIM Procedure

Total Sample Size 150 DF Total 149
Variables 1 DF Within Classes 147
Classes 3 DF Between Classes 2

Number of Observations Read 150
Number of Observations Used 150

Class Level Information
Species Variable
Name		 Frequency Weight Proportion Prior
Probability
Setosa Setosa 50 50.0000 0.333333 0.333333
Versicolor Versicolor 50 50.0000 0.333333 0.333333
Virginica Virginica 50 50.0000 0.333333 0.333333

Discriminant Analysis of Fisher (1936) Iris Data
Using Normal Density Estimates with Unequal Variance

The DISCRIM Procedure
Classification Results for Calibration Data: WORK.IRIS
Cross-validation Results using Quadratic Discriminant Function

Posterior Probability of Membership in Species
Obs From Species Classified into
Species		 Setosa Versicolor Virginica
5 Virginica Versicolor * 0.0000 0.8740 0.1260
9 Versicolor Virginica * 0.0000 0.0686 0.9314
42 Setosa Versicolor * 0.4923 0.5073 0.0004
57 Virginica Versicolor * 0.0000 0.9602 0.0398
78 Virginica Versicolor * 0.0000 0.6558 0.3442
91 Virginica Versicolor * 0.0000 0.8740 0.1260
148 Versicolor Virginica * 0.0000 0.2871 0.7129

* Misclassified observation


Discriminant Analysis of Fisher (1936) Iris Data
Using Normal Density Estimates with Unequal Variance

The DISCRIM Procedure
Classification Summary for Calibration Data: WORK.IRIS
Cross-validation Summary using Quadratic Discriminant Function

Number of Observations and Percent Classified
into Species
From Species Setosa Versicolor Virginica Total
Setosa
49
98.00
1
2.00
0
0.00
50
100.00
Versicolor
0
0.00
48
96.00
2
4.00
50
100.00
Virginica
0
0.00
4
8.00
46
92.00
50
100.00
Total
49
32.67
53
35.33
48
32.00
150
100.00
Priors
0.33333
 
0.33333
 
0.33333
 
 
 

Error Count Estimates for Species
  Setosa Versicolor Virginica Total
Rate 0.0200 0.0400 0.0800 0.0467
Priors 0.3333 0.3333 0.3333  

Discriminant Analysis of Fisher (1936) Iris Data
Using Normal Density Estimates with Unequal Variance

The DISCRIM Procedure
Classification Summary for Test Data: WORK.PLOTDATA
Classification Summary using Quadratic Discriminant Function

Observation Profile for Test Data
Number of Observations Read 71
Number of Observations Used 71

Number of Observations and Percent Classified
into Species
  Setosa Versicolor Virginica Total
Total
23
32.39
20
28.17
28
39.44
71
100.00
Priors
0.33333
 
0.33333
 
0.33333
 
 
 

disx1mdisx1m, continued

Two more analyses are run with nonparametric methods (METHOD=NPAR), specifically kernel density estimates with normal kernels (KERNEL=NORMAL). The first of these uses equal bandwidths (smoothing parameters) (POOL=YES) in each class. The use of equal bandwidths does not constrain the density estimates to be of equal variance. The value of the radius parameter that, assuming normality, minimizes an approximate mean integrated square error is (see the section Nonparametric Methods). Choosing gives a more detailed look at the irregularities in the data. The following statements produce Output 31.1.4: 

title2 'Using Kernel Density Estimates with Equal Bandwidth';

proc discrim data=iris method=npar kernel=normal
                r=.4 pool=yes
             testdata=plotdata testout=plotp
                testoutd=plotd
             short noclassify crosslisterr;
   class Species;
   var PetalWidth;
run;

%plotden;
%plotprob;

Output 31.1.4 Kernel Density Estimates with Equal Bandwidth
Discriminant Analysis of Fisher (1936) Iris Data
Using Kernel Density Estimates with Equal Bandwidth

The DISCRIM Procedure

Total Sample Size 150 DF Total 149
Variables 1 DF Within Classes 147
Classes 3 DF Between Classes 2

Number of Observations Read 150
Number of Observations Used 150

Class Level Information
Species Variable
Name		 Frequency Weight Proportion Prior
Probability
Setosa Setosa 50 50.0000 0.333333 0.333333
Versicolor Versicolor 50 50.0000 0.333333 0.333333
Virginica Virginica 50 50.0000 0.333333 0.333333

Discriminant Analysis of Fisher (1936) Iris Data
Using Kernel Density Estimates with Equal Bandwidth

The DISCRIM Procedure
Classification Results for Calibration Data: WORK.IRIS
Cross-validation Results using Normal Kernel Density

Posterior Probability of Membership in Species
Obs From Species Classified into
Species		 Setosa Versicolor Virginica
5 Virginica Versicolor * 0.0000 0.8827 0.1173
9 Versicolor Virginica * 0.0000 0.0438 0.9562
57 Virginica Versicolor * 0.0000 0.9472 0.0528
78 Virginica Versicolor * 0.0000 0.8061 0.1939
91 Virginica Versicolor * 0.0000 0.8827 0.1173
148 Versicolor Virginica * 0.0000 0.2586 0.7414

* Misclassified observation


Discriminant Analysis of Fisher (1936) Iris Data
Using Kernel Density Estimates with Equal Bandwidth

The DISCRIM Procedure
Classification Summary for Calibration Data: WORK.IRIS
Cross-validation Summary using Normal Kernel Density

Number of Observations and Percent Classified
into Species
From Species Setosa Versicolor Virginica Total
Setosa
50
100.00
0
0.00
0
0.00
50
100.00
Versicolor
0
0.00
48
96.00
2
4.00
50
100.00
Virginica
0
0.00
4
8.00
46
92.00
50
100.00
Total
50
33.33
52
34.67
48
32.00
150
100.00
Priors
0.33333
 
0.33333
 
0.33333
 
 
 

Error Count Estimates for Species
  Setosa Versicolor Virginica Total
Rate 0.0000 0.0400 0.0800 0.0400
Priors 0.3333 0.3333 0.3333  

Discriminant Analysis of Fisher (1936) Iris Data
Using Kernel Density Estimates with Equal Bandwidth

The DISCRIM Procedure
Classification Summary for Test Data: WORK.PLOTDATA
Classification Summary using Normal Kernel Density

Observation Profile for Test Data
Number of Observations Read 71
Number of Observations Used 71

Number of Observations and Percent Classified
into Species
  Setosa Versicolor Virginica Total
Total
26
36.62
18
25.35
27
38.03
71
100.00
Priors
0.33333
 
0.33333
 
0.33333
 
 
 

disx1sdisx1s, continued

Another nonparametric analysis is run with unequal bandwidths (POOL=NO). The following statements produce Output 31.1.5: 

title2 'Using Kernel Density Estimates with Unequal Bandwidth';

proc discrim data=iris method=npar kernel=normal
                r=.4 pool=no
             testdata=plotdata testout=plotp
                testoutd=plotd
             short noclassify crosslisterr;
   class Species;
   var PetalWidth;
run;

%plotden;
%plotprob;

Output 31.1.5 Kernel Density Estimates with Unequal Bandwidth
Discriminant Analysis of Fisher (1936) Iris Data
Using Kernel Density Estimates with Unequal Bandwidth

The DISCRIM Procedure

Total Sample Size 150 DF Total 149
Variables 1 DF Within Classes 147
Classes 3 DF Between Classes 2

Number of Observations Read 150
Number of Observations Used 150

Class Level Information
Species Variable
Name		 Frequency Weight Proportion Prior
Probability
Setosa Setosa 50 50.0000 0.333333 0.333333
Versicolor Versicolor 50 50.0000 0.333333 0.333333
Virginica Virginica 50 50.0000 0.333333 0.333333

Discriminant Analysis of Fisher (1936) Iris Data
Using Kernel Density Estimates with Unequal Bandwidth

The DISCRIM Procedure
Classification Results for Calibration Data: WORK.IRIS
Cross-validation Results using Normal Kernel Density

Posterior Probability of Membership in Species
Obs From Species Classified into
Species		 Setosa Versicolor Virginica
5 Virginica Versicolor * 0.0000 0.8805 0.1195
9 Versicolor Virginica * 0.0000 0.0466 0.9534
57 Virginica Versicolor * 0.0000 0.9394 0.0606
78 Virginica Versicolor * 0.0000 0.7193 0.2807
91 Virginica Versicolor * 0.0000 0.8805 0.1195
148 Versicolor Virginica * 0.0000 0.2275 0.7725

* Misclassified observation


Discriminant Analysis of Fisher (1936) Iris Data
Using Kernel Density Estimates with Unequal Bandwidth

The DISCRIM Procedure
Classification Summary for Calibration Data: WORK.IRIS
Cross-validation Summary using Normal Kernel Density

Number of Observations and Percent Classified
into Species
From Species Setosa Versicolor Virginica Total
Setosa
50
100.00
0
0.00
0
0.00
50
100.00
Versicolor
0
0.00
48
96.00
2
4.00
50
100.00
Virginica
0
0.00
4
8.00
46
92.00
50
100.00
Total
50
33.33
52
34.67
48
32.00
150
100.00
Priors
0.33333
 
0.33333
 
0.33333
 
 
 

Error Count Estimates for Species
  Setosa Versicolor Virginica Total
Rate 0.0000 0.0400 0.0800 0.0400
Priors 0.3333 0.3333 0.3333  

Discriminant Analysis of Fisher (1936) Iris Data
Using Kernel Density Estimates with Unequal Bandwidth

The DISCRIM Procedure
Classification Summary for Test Data: WORK.PLOTDATA
Classification Summary using Normal Kernel Density

Observation Profile for Test Data
Number of Observations Read 71
Number of Observations Used 71

Number of Observations and Percent Classified
into Species
  Setosa Versicolor Virginica Total
Total
25
35.21
18
25.35
28
39.44
71
100.00
Priors
0.33333
 
0.33333
 
0.33333
 
 
 

disx1ydisx1y, continued

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