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PRINQUAL Procedure


The PRINQUAL procedure performs principal component analysis (PCA) of qualitative, quantitative, or mixed data. PROC PRINQUAL enables you to do the following:

  • find linear and nonlinear transformations of variables, using the method of alternating least squares, that optimize properties of the transformed variables' correlation or covariance matrix. Nonoptimal transformations such as logarithm and rank are also available.
  • fit metric and nonmetric principal component analyses
  • perform metric and nonmetric multidimensional preference (MDPREF) analyses
  • reduce the number of variables for subsequent use in regression analyses, cluster analyses, and other analyses
  • choose between three methods, each of which seeks to optimize a different property of the transformed variables' covariance or correlation matrix. These methods are as follows:
    • maximum total variance, or MTV
    • minimum generalized variance, or MGV
    • maximum average correlation, or MAC
  • transform ordinal variables monotonically by scoring the ordered categories so that order is weakly preserved (adjacent categories can be merged) and the covariance matrix is optimized. You can undo ties optimally or leave them tied. You can also transform ordinal variables to ranks.
  • transform nominal variables by optimally scoring the categories
  • transform interval and ratio scale of measurement variables linearly, or transform them nonlinearly with spline transformations or monotone spline transformations. In addition, nonoptimal transformations for logarithm, rank, exponential, power, logit, and inverse trigonometric sine are available.
  • estimate missing data without constraint, with category constraints (missing values within the same group get the same value), and with order constraints (missing value estimates in adjacent groups can be tied to preserve a specified ordering).
  • detect nonlinear relationships
  • perform weighted estimation
  • perform BY group processing, which enables you to obtain separate analyses on grouped observations
  • create a SAS data set that contains the original variables, transformed variables, components, or data approximations
  • create a SAS data set that corresponds to any output table
  • automatically create graphs by using ODS Graphics

For further details see the SAS/STAT User's Guide: The PRINQUAL Procedure
( PDF | HTML )

Examples