The HPPRINCOMP Procedure


The main features of the HPPRINCOMP procedure are as follows:

  • 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 sum-of-squares-and-crossproducts (SSCP) matrix and the principal component scores

  • supports a PARTIAL statement for analyzing a partial correlation or covariance matrix

  • supports a FREQ statement for grouped analysis

  • supports a WEIGHT statement for weighted analysis

  • produces an output data set that contains principal component scores

  • produces an output data set that contains means, standard deviations, number of observations, correlations or covariances, eigenvalues, and eigenvectors