Getting Started: PRINQUAL Procedure

PROC PRINQUAL can be used to fit a principal component model with nonlinear transformations of the variables and graphically display the results. This example finds monotonic transformations of ratings of automobiles.

   title 'Ratings for Automobiles Manufactured in 1980';
   
   data cars;
      input Origin $ 1-8 Make $ 10-19 Model $ 21-36
            (MPG Reliability Acceleration Braking Handling Ride
             Visibility Comfort Quiet Cargo) (1.);
      datalines;
   GMC      Buick      Century         3334444544
   GMC      Buick      Electra         2434453555
   GMC      Buick      Lesabre         2354353545
   
   ... more lines ...   

   GMC      Pontiac    Sunbird         3134533234
   ;

   ods graphics on;
   
   proc prinqual data=cars plots=all maxiter=100;
      transform monotone(mpg -- cargo);
      id model;
   run;
   
   ods graphics off;

The PROC PRINQUAL statement names the input data set Cars. The ODS GRAPHICS statement, along with the PLOTS=ALL option, requests all graphical displays. The MDPREF option requests the PCA plot with the scores (automobiles) represented as points and the structure (variables) represented as vectors. By default, the vector lengths are increased by a factor of 2.5 to produce a better graphical display. If instead you were to specify MDPREF=1, you would get the actual vectors, and they would all be short and would end near the origin where there are a lot of points. It is often the case that increasing the vector lengths by a factor of 2 or 3 makes a better graphical display, so by default the vector lengths are increased by a factor of 2.5. Up to 100 iterations are requested with the MAXITER= option. All of the numeric variable are specified with a MONOTONE transformation, so their original values, 1 to 5, are optimally rescored to maximize fit to a two-component model while preserving the original order. The Model variable provides the labels for the row points in the plot.

The iteration history table is shown in Figure 70.1. The monotonic transformations allow the PCA to account for 5% more variance in two principal components than the ordinary PCA model applied to the untransformed data.