10.4 - Example: Insect Data

Data were collected on two species of insects in the genus Chaetocnema, (species a) Ch. concinna and (species b) Ch. heikertlingeri. Three variables were measured on each insect:

• \(X_1\) = Width of the 1^st joint of the tarsus (legs)
• \(X_2\) = Width of the 2^nd joint of the tarsus
• \(X_3\) = Width of the aedeagus (reproductive organ)

We have ten individuals of each species to make up training data. Data on these ten individuals of each species are used to estimate the model parameters which we will use in the linear score function.

Our objective is to obtain a classification rule for identifying the insect species from these three variables.

Let's begin...

Step 1

Collect the training data. (described above)

Step 2

Specify the prior probabilities. In this case, we do not have any information regarding the relative abundances of the two species. Without any information in order to help specify prior probabilities, equal priors are selected:

\[\hat{p}_1 = \hat{p}_2 = \dfrac{1}{2}\]

Step 3

options ls=78;
title "Discriminant Analysis - Insect Data";

data insect;
  infile "D:\Statistics\STAT 505\data\insect.csv" firstobs=2 delimiter=',';
  input species $ joint1 joint2 aedeagus;
  run;

 /* A new data set called 'test' is created to store any new
  * values to be classified with our discriminant rule.
  * The variables must match the quantitative ones in the training set.
  */

data test;
  input joint1 joint2 aedeagus;
  cards;
  194 124 49
  ; run;

 /* The pool option conducts a test of equal covariance matrices.
  * If the results of the test are insignificant (at the 0.10 level), the
  * sample covariance matrices are pooled, resulting in a linear discriminant
  * function; otherwise, the sample covariance matrices are not pooled, 
  * resulting in a quadratic discriminant function.
  * The crossvalidate option calculates the confusion matrix based on 
  * the holdout method, where each obs is classified from the other obs only. 
  * The testdata= option specifies the data set with obs to be classified.
  * The testout= option specifies the name of the data set where classification
  * results are stored.
  * The class statement specifies the variable with groups for classification.
  * The var statement specifies the quantitative variables used to estimate
  * the mean and covariance matrices of the groups.
  */

proc discrim data=insect pool=test crossvalidate testdata=test testout=a;
  class species;
  var joint1 joint2 aedeagus;
  run;

 /* This will print the results of the classifications of the obs
  * from the 'test' data set.
  */

proc print data=a;
  run;

Step 4

Estimate the parameters of the conditional probability density functions, i.e., the population mean vectors and the population variance-covariance matrices involved. It turns out that all of this is done automatically in the discriminant analysis procedure.

Step 5

The linear discriminant functions for the two species can be obtained directly from the SAS or Minitab output.

Step 6

We will discuss this step in Lesson 10.5.

Step 7

Conclusion

According to the classification rule the insect is classified into the species with the highest linear discriminant function. Because \(\hat{s}^L_b(\mathbf{x}) > \hat{s}^L_a(\mathbf{x})\), we conclude that the insect belongs to species (b) Ch. heikertlingeri.

Of course, the addition of the log of .5 does not make any difference. Whether we classify on the basis of \(\hat{d}^L_b(\mathbf{x})\) or on the basis of the score function, the decision will remain the same. In case the priors are not equal, this would not hold.

You can think of the priors as a 'penalty' in some sense. If you have a higher prior probability of a given species you will give it very little 'penalty' because you will be taking the log of a number close to one which is not going to subtract much.  On the other hand, if there is a low prior probability, then the log of a very small number results in a larger reduction.