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Multivariate Methods > Discriminant Analysis
Publication date: 04/21/2023

Discriminant Analysis

Predict Classifications Based on Continuous Variables

Discriminant analysis predicts membership in a group or category based on observed values of several continuous variables. Specifically, discriminant analysis predicts a classification (X) variable (categorical) based on known continuous responses (Y). The data for a discriminant analysis consist of a sample of observations with known group membership together with their values on the continuous variables.

For example, you might attempt to classify loan applicants into three loan categories (X) based on expected profitability: low interest rate loan, long term loan, or no loan. You might use continuous variables such as current salary, years in current job, age, and debt burden, (Ys) to predict an individual’s most profitable loan category. You could build a predictive model to classify an individual into a loan category using discriminant analysis.

Features of the Discriminant platform include the following:

A stepwise selection option to help choose variables that discriminate well.

A choice of fitting methods: Linear, Quadratic, Regularized, and Wide Linear.

A canonical plot and a misclassification summary.

Discriminant scores and squared distances to each group.

Options to save prediction distances and probabilities to the data table.

Figure 5.1 Canonical Plot 

Canonical Plot

Contents

Overview of the Discriminant Platform

Example of Discriminant Analysis

Launch the Discriminant Platform

Stepwise Variable Selection
Discriminant Method
Shrink Covariances

Discriminant Analysis Report

Principal Components
Canonical Plot and Canonical Structure
Discriminant Scores
Score Summaries

Discriminant Analysis Options

Show Canonical Details
Show Canonical Structure
Consider New Levels
Save Discrim Matrices

Validation in JMP and JMP Pro

Additional Examples of Discriminant Analysis

Example of a Canonical 3D Plot
Example of Stepwise Variable Selection

Statistical Details for the Discriminant Platform

Statistical Details for the Wide Linear Algorithm
Statistical Details for Saved Formulas
Statistical Details for Multivariate Tests
Statistical Details for Approximate F-Tests
Statistical Details for the Between Groups Covariance Matrix
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