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Comparison of the PRINCOMP and PRINQUAL Procedures

The PRINCOMP procedure performs principal component analysis. The PRINQUAL procedure finds linear and nonlinear transformations
of variables to optimize properties of the transformed variablesâ€™ covariance or correlation matrix. One property is the sum
of the first n eigenvalues, which is a measure of the fit of a principal component model with n components. Use PROC PRINQUAL to find nonlinear transformations of your variables or to perform a multidimensional preference
analysis. Use PROC PRINCOMP to fit a principal component model to your data or to PROC PRINQUALâ€™s output data set. PROC PRINCOMP
produces a report of the principal component analysis, a number of graphical displays, and output data sets. PROC PRINQUAL
produces only a few graphs and an output data set.