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 variables’ covariance or correlation matrix. One property is the sum of the first eigenvalues, which is a measure of the fit of a principal component model with 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 PRINQUAL’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.