Performing a Principal Component Analysis of Transformed Data |
PROC PRINQUAL produces an iteration history table that displays (for each iteration) the iteration number, the maximum and average absolute change in standardized variable scores computed over the iteratively transformed variables, the criterion being optimized, and the criterion change. In order to examine the results of the analysis in more detail, you can analyze the information in the output data set by using other SAS procedures.
Specifically, use the PRINCOMP procedure to perform a principal components analysis on the transformed data. PROC PRINCOMP accepts the raw data from PROC PRINQUAL but issues a warning, because the PROC PRINQUAL output data set has _NAME_ and _TYPE_ variables but is not a TYPE=CORR data set. You can ignore this warning.
If the output data set contains both scores and correlations, you must subset it for analysis with PROC PRINCOMP. Otherwise, the correlation observations are treated as ordinary observations and the PROC PRINCOMP results are incorrect. For example, consider the following statements:
proc prinqual data=a out=b correlations replace; transform spline(var1-var50 / nknots=3); run; proc princomp data=b; where _TYPE_='SCORE'; run;
Also note that the proportion of variance accounted for, as reported by PROC PRINCOMP, can exceed the proportion of variance accounted for in the last PROC PRINQUAL iteration. This is because PROC PRINQUAL reports the variance accounted for by the components analysis that generated the current scaling of the data, not a components analysis of the current scaling of the data.