The HPCANDISC Procedure

performs a canonical discriminant analysis, computes squared Mahalanobis distances between class means, and performs both univariate and multivariate one-way analyses of variance

can perform analysis on a massively parallel SAS high-performance appliance

reads input data in parallel and writes output data in parallel when the data source is the appliance database

is highly multithreaded during calculations of the within-class sum-of-squares-and-crossproducts (SSCP) matrix and the canonical variable scores

supports a FREQ statement for grouped analysis

supports a WEIGHT statement for weighted analysis

displays both standardized and unstandardized canonical coefficients

displays correlations between the canonical variables and the original variables

displays class means for the canonical variables

produces two output data sets: one that contains the canonical coefficients and another that contains scored canonical variables