The PRINQUAL Procedure
- Overview
- Getting Started
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Syntax
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Details
The Three Methods of Variable Transformation Understanding How PROC PRINQUAL Works Splines Missing Values Controlling the Number of Iterations Performing a Principal Component Analysis of Transformed Data Using the MAC Method Output Data Set Avoiding Constant Transformations Constant Variables Character OPSCORE Variables REITERATE Option Usage Passive Observations Computational Resources Displayed Output ODS Table Names ODS Graphics
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Examples
- References
| Missing Values |
PROC PRINQUAL can estimate missing values, subject to optional constraints, so that the covariance matrix is optimized. The procedure provides several approaches for handling missing data. When you specify the NOMISS option in the PROC PRINQUAL statement, observations with missing values are excluded from the analysis. Otherwise, missing data are estimated, using variable means as initial estimates. Missing values for OPSCORE character variables are treated the same as any other category during the initialization. See the section Missing Values in Chapter 93, The TRANSREG Procedure, for more information about missing data estimation.