Modeling and Multivariate Methods - SAS

174 Fitting Multiple Response Models Chapter 5
Discriminant Analysis
For a classification variable with k levels, JMP adds k distance columns, k classification probability columns,
the predicted classification column, and two columns of other computational information to the current
data table.
Example of the Save Discrim Option
Examine Fisher’s Iris data as found in Mardia, Kent, and Bibby (1979). There are k = 3 levels of species and
four measures on each sample.
1. Open the Iris.jmp sample data table.
2. Select Analyze > Fit Model.
3. Select Sepal length, Sepal width, Petal length, and Petal width and click Y.
4. Select Species and click Add.
5. Next to Personality, select Manova.
6. Click Run.
7. From the red triangle menu next to Manova Fit, select Save Discrim.
The following columns are added to the Iris.jmp sample data table:
SqDist[0]
SqDist[setosa]
SqDist[versicolor]
SqDist[virginica]
Prob[0]
Prob[setosa]
Prob[versicolor]
Prob[virginica]
Pred Species
Quadratic form needed in the Mahalanobis distance calculations.
Mahalanobis distance of the observation from the Setosa centroid.
Mahalanobis distance of the observation from the Versicolor centroid.
Mahalanobis distance of the observation from the Virginica centroid.
Sum of the negative exponentials of the Mahalanobis distances, used below.
Probability of being in the Setosa category.
Probability of being in the Versicolor category.
Probability of being in the Virginica category.
Species that is most likely from the probabilities.
Now you can use the new columns in the data table with other JMP platforms to summarize the
discriminant analysis with reports and graphs. For example:
1. From the updated Iris.jmp data table (that contains the new columns) select Analyze > Fit Y by X.
2. Select Species and click Y, Response.
3. Select Pred Species and click X, Factor.
4. Click OK.