The HPCANDISC Procedure


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