The main features of the HPCANDISC procedure are as follows:
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