| Multivariate Analysis: Discriminant Analysis |
Multivariate Analysis: Discriminant Analysis
For a set of observations containing one or more interval variables and a classification variable defining groups of observations, discriminant analysis derives a discriminant criterion function to classify each observation into one of the groups.
When the distribution within each group is assumed to be multivariate normal, a parametric method can be used to develop a discriminant function. The discriminant function, also known as a classification criterion, is determined by a generalized squared distance. The classification criterion can be based on either the individual within-group covariance matrices (yielding a quadratic function) or the pooled covariance matrix (yielding a linear function). It also takes into account the prior probabilities of the groups.
When no assumptions can be made about the distribution within each group, or when the distribution is not assumed to be multivariate normal, nonparametric methods can be used to estimate the group-specific densities. These methods include the kernel and 
-nearest-neighbor methods.
You can run the Discriminant analysis by selecting Analysis 
Multivariate Analysis Discriminant Analysis from the main menu. The analysis is implemented by calling the DISCRIM procedure in SAS/STAT. See the documentation for the DISCRIM procedure in the SAS/STAT User's Guide for additional details.
Example
Specifying the Discriminant Analysis
Analysis of Selected Variables
