Capabilities

The DISCRIM Procedure

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 )

E-mail:
Password