• A Covariance Matrices report that gives the pooled-within covariance and correlation matrices.
 • For the Quadratic and Regularized methods, a Correlations for Each Group report that shows:
 ‒ the within-group correlation matrices
 ‒ for each group, the log of the determinant of the within-group covariance matrix
 • For the Quadratic discriminant method, adds a Group Covariances outline to the Covariance Matrices report that shows the within-group covariance matrices.
Saves a script called Discrim Results to the data table. The script is a list of the following objects for use in JSL:
 ‒ a list of the covariates (Ys)
 ‒ the categorical variable X
 ‒ a list of the levels of X
 ‒ a matrix of the means of the covariates by the levels of X
 ‒ the pooled-within covariance matrix
 ‒ The distance formulas are SqDist and SqDist[], where represents a level of X. The distance formulas produce intermediate values connected with the Mahalanobis distance calculations.
 ‒ The probability formulas are Prob[], where represents a level of X. Each probability column gives the posterior probability of an observation’s membership in that level of X. The Response Probability column property is saved to each probability column. For details about the Response Probability column property, see the Using JMP book.
 ‒ The predicted membership formula is Pred and contains the “most likely level” classification rule.
 ‒ The Wide Linear method also saves a Discrim Data Matrix column containing the vector of covariates and a Discrim Prin Comp formula. See Wide Linear Discriminant Method.
 ‒ By default, the rays emanate from the point (0,0), which represents the grand mean of the data in terms of the canonical variables. In the Canonical Plot, you can drag the rays or use this option to specify coordinates.
 ‒ The default Radius Scaling in the canonical plots is 1.5, unless an adjustment is needed to make the rays visible. Radius Scaling is done relative to the Standardized Scoring Coefficients.
Colors the points in the Canonical Plot and the Canonical 3D Plot based on the levels of the X variable. Color markers are added to the rows in the data table. This option is equivalent to selecting Rows > Color or Mark by Column and selecting the X variable. It is also equivalent to right-clicking the graph and selecting Row Legend, and then coloring by the classification column.
Shows or hides Canonical Structures report. See Show Canonical Structure. Not available for the Wide Linear discriminant method.
Tip: In a script, sending the scripting command Save to New Data Table to the Discriminant object saves the following to a new data table: group means on the canonical variables; the biplot rays with 1.5 Radius Scaling of the Standardized Scoring Coefficients; and the canonical scores. Not available for the Wide Linear discriminant method.
Canonical Details for Iris.jmp
Canonical correlations between the covariates and the groups defined by the categorical X. Suppose that you define numeric indicator variables to represent the groups defined by X. Then perform a canonical correlation analysis using the covariates as one set of variables and the indicator variables representing the groups in X as the other. The Canonical Corr values are the canonical correlation values that result from this analysis.
F value associated with the corresponding test. For certain tests, the F value is approximate or an upper bound. See Approximate F-Tests in Statistical Details.
p-value for the corresponding test.
Coefficients used to compute canonical scores in terms of the raw data. These are the coefficients used for the option Canonical Options > Save Canonical Scores. For details about how these are computed, see “The CANDISC Procedure” in SAS Institute Inc. (2011).
Coefficients used to compute canonical scores in terms of the standardized data. Often called loadings. For details about how these are computed, see “The CANDISC Procedure” in SAS Institute Inc. (2011).
Canonical Structure for Iris.jmp Showing between Canonical Structure
 1 Select Help > Sample Data Library and open Owl Diet.jmp.
 2 Select rows 180 through 294.
These are the rows for which species is missing. You will hide and exclude these rows.
 3 Select Rows > Hide and Exclude.
 4 Select Rows > Color or Mark by Column.
 5 Select species.
 6 From the Colors menu, select JMP Dark.
 7 Check Make Window with Legend.
 8 Click OK.
 9
 10 Specify skull length, teeth row, palatine foramen, and jaw length as Y, Covariates.
 11 Specify species as X, Categories.
 12 Click OK.
 13 Select Canonical 3D Plot from the Discriminant Analysis red triangle menu.
Canonical 3D Plot with Legend Window
Observations that would be better fit using a new group are assigned to the new level, called “Other”. Probability of membership in the Other group assumes that these observations have the distribution of the entire set of observations where no group structure is assumed. This leads to correspondingly wide normal contours associated with the covariance structure. Distance calculations are adjusted by the specified prior probability.
Save Discrim Matrices creates a global list (DiscrimResults) for use in the JMP scripting language. The list contains the following, calculated for the training set:
 • YNames, a list of the covariates (Ys)
 • XName, the categorical variable
 • XValues, a list of the levels of X
 • YMeans, a matrix of the means of the covariates by the levels of X
 • YPartialCov, the within covariance matrix
Consider the analysis obtained using the Discriminant script in the Iris.jmp sample data table. If you select Save Discrim Matrices from the red triangle menu, the script Discrim Results is saved to the data table. The script is shown in Discrim Results Table Script for Iris.jmp.
Discrim Results Table Script for Iris.jmp
Scatterplot Matrix for Iris.jmp