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The PRINQUAL Procedure

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 71.1. 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.

Figure 71.1 Automobile Ratings Iteration History
Ratings for Automobiles Manufactured in 1980

The PRINQUAL Procedure

PRINQUAL MTV Algorithm Iteration History
Iteration
Number
Average
Change
Maximum
Change
Proportion
of Variance
Criterion
Change
Note
1 0.18087 1.24219 0.53742    
2 0.06916 0.77503 0.57244 0.03502  
3 0.04653 0.38237 0.57978 0.00734  
4 0.03387 0.18682 0.58300 0.00321  
5 0.02661 0.13506 0.58484 0.00185  
6 0.01730 0.09213 0.58600 0.00115  
7 0.00969 0.07107 0.58660 0.00061  
8 0.00705 0.04798 0.58685 0.00025  
9 0.00544 0.03482 0.58699 0.00014  
10 0.00442 0.02641 0.58708 0.00009  
11 0.00363 0.02062 0.58714 0.00006  
12 0.00298 0.01643 0.58717 0.00004  
13 0.00245 0.01325 0.58720 0.00002  
14 0.00201 0.01077 0.58721 0.00002  
15 0.00165 0.00880 0.58723 0.00001  
16 0.00136 0.00721 0.58723 0.00001  
17 0.00112 0.00591 0.58724 0.00001  
18 0.00092 0.00485 0.58724 0.00000  
19 0.00075 0.00399 0.58724 0.00000  
20 0.00062 0.00328 0.58725 0.00000  
21 0.00051 0.00269 0.58725 0.00000  
22 0.00042 0.00221 0.58725 0.00000  
23 0.00035 0.00182 0.58725 0.00000  
24 0.00028 0.00149 0.58725 0.00000  
25 0.00023 0.00123 0.58725 0.00000  
26 0.00019 0.00101 0.58725 0.00000  
27 0.00016 0.00083 0.58725 0.00000  
28 0.00013 0.00068 0.58725 0.00000  
29 0.00011 0.00056 0.58725 0.00000  
30 0.00009 0.00046 0.58725 0.00000  
31 0.00007 0.00038 0.58725 0.00000  
32 0.00006 0.00031 0.58725 0.00000  
33 0.00005 0.00025 0.58725 0.00000  
34 0.00004 0.00021 0.58725 0.00000  
35 0.00003 0.00017 0.58725 0.00000  
36 0.00003 0.00014 0.58725 0.00000  
37 0.00002 0.00012 0.58725 0.00000  
38 0.00002 0.00010 0.58725 0.00000  
39 0.00001 0.00008 0.58725 0.00000  
40 0.00001 0.00006 0.58725 0.00000  
41 0.00001 0.00005 0.58725 0.00000  
42 0.00001 0.00004 0.58725 0.00000 Converged

Algorithm converged.

The PCA biplot in Figure 71.2 shows the transformed automobile ratings projected into the two-dimensional plane of the analysis. The automobiles on the left tend to be smaller than the autos on the right, and the autos at the top tend to be cheaper than the autos at the bottom. The vectors can help you interpret the plot of the scores. Longer vectors show the variables that better fit the two-dimensional model. A larger component of them is in the plane of the plot. In contrast, shorter vectors show the variables that do not fit the two-dimensional model as well. They tend to be located less in the plot and more away from the plot; hence their projection into the plot is shorter. To envision this, lay a pencil on your desk directly under a light, and slowly rotate it up to form a 90-degree angle with your desk. As you do so, the shadow or projection of the pencil onto your desk will get progressively shorter. The results show, for example, that the Chevette would be expected to do well on gas mileage but not well on quiet and acceleration. In contrast, the Corvette and the Firebird have the opposite pattern.

Figure 71.2 Automobile Ratings PCA Biplot
Automobile Ratings PCA Biplot


There are many patterns shown in the transformations in Figure 71.3. The transformation of Braking, for example, is not very different from the original scoring. The optimal scoring for other variables, such as Acceleration and Handling, is binary. Automobiles are differentiated by high versus everything else or low versus everything else.

Figure 71.3 Automobile Ratings Transformations
Automobile Ratings TransformationsAutomobile Ratings Transformations, continued

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