17.7.4.1 The Discriminant Analysis Dialog BoxDiscAnalysisDialog
In the Discriminant Analysis dialog, you can perform Discriminant Analysis or Canonical Discriminant Analysis. To perform Canonical Discriminant Analysis, click the button to the right of Dialog Theme and select the Canonical Discriminant Analysis theme from the menu.
Recalculate
Specify the way to recalculate and update the result if there is any change in the input data or settings.
None

The output will not be connected to the source data, and any change will not result in an update of the result. You can't change settings to recalculate the result.

Auto

The result automatically updates when source data changes. You can also change settings to recalculate the result.

Manual

The result will not automatically update when source data changes. You must manually activate the update by clicking the Recalculate button in the Standard toolbar. You can also change settings to recalculate the result.

Input
Select data for Discriminant Analysis.
Group for Training Data

Select data from a column to specify group for training data. Note that the grouping column will be set as categorical if Text column.

Training Data

Select data to specify training data.
Note that the number of rows of Group for Training Data and Training Data should be the same, otherwise only the rows in Group for Training Data corresponding to Training Data are included in analysis.

Predict Membership for Test Data

Determine whether to predict membership for test data. If checked, Test Data control is shown.

Test Data

Select data to specify test data.
Note that test data should contain the same number of variables as training data.

Settings
Specify the settings in Discriminant Analysis.
Prior Probabilities

Select the type of prior probabilities for each group. Origin supports two types:
 Prior probabilities are equal for all groups.
 Proportional to group size
 Prior probability for a group is proportional to the number of observations in the group.

Discriminant Function

Select the method to classify. Origin provides two methods:
 Using Linear Discriminant Function. The pooled withingroup covariance matrix is used to calculate Mahalanobis distance.
 Using Quadratic Discriminant Function. Withingroup covariance matrices are used to calculate Mahalanobis distance.
For more details, see the algorithm of discriminant functions.

Canonical Discriminant Analysis

Specify whether to perform Canonical Discriminant Analysis. If not selected, Canonical Discriminant Analysis branch in Quantities group and Canonical Score Plot check box in Plots group will be disabled.

Cross Validation

Specify whether to classify training data using Cross Validation method.

Statistics
Specify whether to perform statistics analysis on training data (e.g., Descriptive Statistics, Univariate ANOVA).
Descriptive Statistics

Specify whether to perform Descriptive Statistics on training data, including means, standard deviations for each variable in each group, and total.

Descriptive Matrices

Specify whether to calculate covariance matrix, correlation matrix and group distance(squared Mahalanobis) matrices of training data.

Univariate ANOVA

Specify whether to perform Univariate ANOVA on training data to test the difference in group means for each variable.

Equality Test of Covariance Matrices

Specify whether to perform Equality Test of Covariance Matrices on training data to test the equality of withingroup covariance matrices.

Pooled Withingroup Covariance/Correlation Matrix

Specify whether to output pooled withingroup covariance matrix and correlation matrix for training data.

Withingroup Covariance Matrices

Specify whether to output withingroup covariance matrices for training data.

Quantities
Specify quantities to calculate in Discriminant Analysis.
Discriminant Function Coefficients

Specify whether to calculate discriminant function coefficients including constant and linear coefficients. Enabled only when Linear is chosen in the Discriminant Function radio box.

Canonical Discriminant Analysis

This branch determines which quantities to calculate in Canonical Discriminant Analysis. It includes the following check boxes.
 Canonical Structure Matrix
 Specify whether to calculate canonical structure matrix in Canonical Discriminant Analysis.
 Specify whether to calculate canonical coefficients in Canonical Discriminant Analysis, including unstandardized canonical coefficients and standardized canonical coefficients.
 Specify whether to calculate canonical scores and canonical group means in Canonical Discriminant Analysis. Note that when Canonical Score Plot is seelected in Plots group, it will be checked and disabled automatically.
Note that Eigenvalues, Wilks' Lamba Test will be included in the result of Canonical Discriminant Analysis by default.
For details, see the introduction of Canonical Discriminant Analysis.

Classification Results

This branch determines which quantities are included in the classification result of training data, test data, and cross validation result of training data. It includes the following check boxes.
 Specify whether posterior probabilities are included in the classification result for observations of training data and test data in different groups. Note that when Classification Fit Plot is chosen in Plots group, it will be checked and disabled automatically.
 Squared Mahalanobis Distance
 Specify whether squared Mahalanobis distance is included in the classification result for observations of training data and test data in different groups.
 Specify whether atypicality index is included in the classification result for observations of training data and test data in different groups.
Note that predicted membership for each observation is listed in the classification result by default.
For details, see the introduction of classification result.

Classification Summary

Specify whether to summarize the classification result, including observation count in each predicted group, error rate for training data and cross validation of training data. Note that if Classification Summary Plot is selected in Plots group, it will be chosen and disabled automatically.

Plots
Specify whether to show plots in Discriminant Analysis report.
Classification Summary Plot

Specify whether to show Classification Summary Plot in the report, which shows the source of predicted group members.

Classification Fit Plot

Specify whether to show Classification Fit Plot in the report, which shows the posterior probabilities of observations for the predicted group.

Canonical Score Plot

Specify whether to show Canonical Score Plot in the report, which shows scores of observations in the first two canonical variables.

Output
Specify the destination of output results.
Discriminant Analysis Report

Specify the sheet for the discriminant analysis report. The default value is a new sheet in the workbook of input data.

Classification Result for Training Data

Specify the sheet for the classification result of training data. The default value is a new sheet in the workbook of input data. Note that the sheet will not be created for Canonical Discriminant Analysis if it is set to <optional>.

Classification Result for Test Data

Specify the sheet for the classification result of test data. The default value is a new sheet in the workbook of input data. Note that it will be disabled if Predict Membership for Test Data is not selected in Input Data group.

Canonical Scores

Specify the sheet for canonical scores. The default value is a new sheet in the workbook of input data. Note that it will be disabled if neither Canonical Scores in the Canonical Discriminant branch of Quantities group nor Canonical Score Plot in Plots group is selected.

