- develops a discriminant criterion to classify
each observation into groups
- the discriminant function is determined by a parametric method (a measure of generalized squared distance) when the distribution within each group is assumed to be multivariate normal
- nonparametric methods can be used to estimate the group-specific
densities when no assumptions can be made about the distribution within each group, or when the distribution
is assumed not to be multivariate normal
- nonparametric methods include the kernel and k-nearest-neighbor methods
- uses uniform, normal, Epanechnikov, biweight, or triweight kernels for density estimation
- either Mahalanobis or Euclidean distance can be used to determine proximity
- Mahalanobis distance can be based on either the full covariance matrix or the diagonal matrix of variances
- the pooled covariance matrix is used to calculate the Mahalanobis distances with a k-nearest-neighbor method
- individual within-group covariance matrices or the
pooled covariance matrix can be used to calculate the Mahalanobis distances with a kernel method
- posterior probability estimates of group membership for each class can be evaluated
- evaluates the performance of a discriminant criterion by estimating error rates
(probabilities of misclassification) in the classification of future observations
- obtain separate analyses on observations in groups
- perform weighted analysis
- uses ODS to create a SAS data set corresponding to any table
For further details see the SAS/STAT User's Guide:
The DISCRIM Procedure
( PDF | HTML )
Examples
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