The PRINQUAL Procedure
- Overview
- Getting Started
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Syntax
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DetailsThe Three Methods of Variable TransformationUnderstanding How PROC PRINQUAL WorksSplinesMissing ValuesControlling the Number of IterationsPerforming a Principal Component Analysis of Transformed DataUsing the MAC MethodOutput Data SetAvoiding Constant TransformationsConstant VariablesCharacter OPSCORE VariablesREITERATE Option UsagePassive ObservationsComputational ResourcesDisplayed OutputODS Table NamesODS 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 117: The TRANSREG Procedure, for more information about missing data estimation.