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Example 21.1: Analysis of Iris Data Using PROC CANDISC

The iris data published by Fisher (1936) have been widely used for examples in discriminant analysis and cluster analysis. The sepal length, sepal width, petal length, and petal width are measured in millimeters on fifty iris specimens from each of three species: Iris setosa, I. versicolor, and I. virginica.

This example is a canonical discriminant analysis that creates an output data set containing scores on the canonical variables and plots the canonical variables. The following statements produce Output 21.1.1 through Output 21.1.7:

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

   data iris;
      title 'Fisher (1936) Iris Data';
      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.';
      symbol = put(Species, specname10.);
      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
   65 30 52 20 3 56 25 39 11 2 65 30 55 18 3 58 27 51 19 3
   68 32 59 23 3 51 33 17 05 1 57 28 45 13 2 62 34 54 23 3
   77 38 67 22 3 63 33 47 16 2 67 33 57 25 3 76 30 66 21 3
   49 25 45 17 3 55 35 13 02 1 67 30 52 23 3 70 32 47 14 2
   64 32 45 15 2 61 28 40 13 2 48 31 16 02 1 59 30 51 18 3
   55 24 38 11 2 63 25 50 19 3 64 32 53 23 3 52 34 14 02 1
   49 36 14 01 1 54 30 45 15 2 79 38 64 20 3 44 32 13 02 1
   67 33 57 21 3 50 35 16 06 1 58 26 40 12 2 44 30 13 02 1
   77 28 67 20 3 63 27 49 18 3 47 32 16 02 1 55 26 44 12 2
   50 23 33 10 2 72 32 60 18 3 48 30 14 03 1 51 38 16 02 1
   61 30 49 18 3 48 34 19 02 1 50 30 16 02 1 50 32 12 02 1
   61 26 56 14 3 64 28 56 21 3 43 30 11 01 1 58 40 12 02 1
   51 38 19 04 1 67 31 44 14 2 62 28 48 18 3 49 30 14 02 1
   51 35 14 02 1 56 30 45 15 2 58 27 41 10 2 50 34 16 04 1
   46 32 14 02 1 60 29 45 15 2 57 26 35 10 2 57 44 15 04 1
   50 36 14 02 1 77 30 61 23 3 63 34 56 24 3 58 27 51 19 3
   57 29 42 13 2 72 30 58 16 3 54 34 15 04 1 52 41 15 01 1
   71 30 59 21 3 64 31 55 18 3 60 30 48 18 3 63 29 56 18 3
   49 24 33 10 2 56 27 42 13 2 57 30 42 12 2 55 42 14 02 1
   49 31 15 02 1 77 26 69 23 3 60 22 50 15 3 54 39 17 04 1
   66 29 46 13 2 52 27 39 14 2 60 34 45 16 2 50 34 15 02 1
   44 29 14 02 1 50 20 35 10 2 55 24 37 10 2 58 27 39 12 2
   47 32 13 02 1 46 31 15 02 1 69 32 57 23 3 62 29 43 13 2
   74 28 61 19 3 59 30 42 15 2 51 34 15 02 1 50 35 13 03 1
   56 28 49 20 3 60 22 40 10 2 73 29 63 18 3 67 25 58 18 3
   49 31 15 01 1 67 31 47 15 2 63 23 44 13 2 54 37 15 02 1
   56 30 41 13 2 63 25 49 15 2 61 28 47 12 2 64 29 43 13 2
   51 25 30 11 2 57 28 41 13 2 65 30 58 22 3 69 31 54 21 3
   54 39 13 04 1 51 35 14 03 1 72 36 61 25 3 65 32 51 20 3
   61 29 47 14 2 56 29 36 13 2 69 31 49 15 2 64 27 53 19 3
   68 30 55 21 3 55 25 40 13 2 48 34 16 02 1 48 30 14 01 1
   45 23 13 03 1 57 25 50 20 3 57 38 17 03 1 51 38 15 03 1
   55 23 40 13 2 66 30 44 14 2 68 28 48 14 2 54 34 17 02 1
   51 37 15 04 1 52 35 15 02 1 58 28 51 24 3 67 30 50 17 2
   63 33 60 25 3 53 37 15 02 1
   ;
   proc candisc data=iris out=outcan distance anova;
      class Species;
      var SepalLength SepalWidth PetalLength PetalWidth;
   run;

PROC CANDISC first displays information about the observations and the classes in the data set in Output 21.1.1.

   %plotit(data=outcan, plotvars=Can2 Can1,
           labelvar=_blank_, symvar=symbol, typevar=symbol,
           symsize=1, symlen=4, exttypes=symbol, ls=100,
           tsize=1.5, extend=close);

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