Discriminant Node Output Options

Select the optional output you want to display in the advanced output of the logistic regression model nugget. To view the advanced output, browse the model nugget and click the Advanced tab. See the topic Discriminant Model Nugget Advanced Output for more information.

Descriptives. Available options are means (including standard deviations), univariate ANOVAs, and Box's M test.

• Means. Displays total and group means, as well as standard deviations for the independent variables.
• Univariate ANOVAs. Performs a one-way analysis-of-variance test for equality of group means for each independent variable.
• Box's M. A test for the equality of the group covariance matrices. For sufficiently large samples, a nonsignificant p value means there is insufficient evidence that the matrices differ. The test is sensitive to departures from multivariate normality.

Function Coefficients. Available options are Fisher's classification coefficients and unstandardized coefficients.

• Fisher's. Displays Fisher's classification function coefficients that can be used directly for classification. A separate set of classification function coefficients is obtained for each group, and a case is assigned to the group for which it has the largest discriminant score (classification function value).
• Unstandardized. Displays the unstandardized discriminant function coefficients.

Matrices. Available matrices of coefficients for independent variables are within-groups correlation matrix, within-groups covariance matrix, separate-groups covariance matrix, and total covariance matrix.

• Within-groups correlation. Displays a pooled within-groups correlation matrix that is obtained by averaging the separate covariance matrices for all groups before computing the correlations.
• Within-groups covariance. Displays a pooled within-groups covariance matrix, which may differ from the total covariance matrix. The matrix is obtained by averaging the separate covariance matrices for all groups.
• Separate-groups covariance. Displays separate covariance matrices for each group.
• Total covariance. Displays a covariance matrix from all cases as if they were from a single sample.

Classification. The following output pertains to the classification results.

• Casewise results. Codes for actual group, predicted group, posterior probabilities, and discriminant scores are displayed for each case.
• Summary table. The number of cases correctly and incorrectly assigned to each of the groups based on the discriminant analysis. Sometimes called the "Confusion Matrix."
• Leave-one-out classification. Each case in the analysis is classified by the functions derived from all cases other than that case. It is also known as the "U-method."
• Territorial map. A plot of the boundaries used to classify cases into groups based on function values. The numbers correspond to groups into which cases are classified. The mean for each group is indicated by an asterisk within its boundaries. The map is not displayed if there is only one discriminant function.
• Combined-groups. Creates an all-groups scatterplot of the first two discriminant function values. If there is only one function, a histogram is displayed instead.
• Separate-groups. Creates separate-group scatterplots of the first two discriminant function values. If there is only one function, histograms are displayed instead.

Stepwise. Summary of Steps displays statistics for all variables after each step; F for pairwise distances displays a matrix of pairwise F ratios for each pair of groups. The F ratios can be used for significance tests of the Mahalanobis distances between groups.