The MIANALYZE Procedure
- Overview
- Getting Started
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Syntax
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Details
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ExamplesReading Means and Standard Errors from a DATA= Data SetReading Means and Covariance Matrices from a DATA= COV Data SetReading Regression Results from a DATA= EST Data SetReading Mixed Model Results from PARMS= and COVB= Data SetsReading Generalized Linear Model ResultsReading GLM Results from PARMS= and XPXI= Data SetsReading Logistic Model Results from a PARMS= Data SetReading Mixed Model Results with Classification CovariatesReading Nominal Logistic Model ResultsUsing a TEST statementCombining Correlation CoefficientsSensitivity Analysis with Control-Based Pattern ImputationSensitivity Analysis with Tipping-Point Approach
- References
The CLASS statement specifies the classification variables in the MODELEFFECTS statement. Classification variables can be either character or numeric. Classification levels are determined from the formatted values of the classification variables. See "The FORMAT Procedure" in the Base SAS Procedures Guide for details.