In JMP you specify the measurement variables as Y effects and the classification variable as a single X effect. The multivariate fitting platform gives estimates of the means and the covariance matrix for the data, assuming that the covariances are the same for each group. You obtain discriminant information with the Save Discrim option in the popup menu next to the MANOVA platform name. This command saves distances and probabilities as columns in the current data table using the initial E and H matrices.
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.
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.
6.
Click Run.
The following columns are added to the Iris.jmp sample data table:
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.
Contingency Table of Predicted and Actual Species