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 MODELEFFECTS statement lists the effects in the data set to be analyzed. Each effect is a variable or a combination of variables, and is specified with a special notation that uses variable names and operators.
Each variable is either a classification (or CLASS) variable or a continuous variable. If a variable is not declared in the CLASS statement, it is assumed to be continuous. Crossing and nesting operators can be used in an effect to create crossed and nested effects.
One general form of an effect involving several variables is
X1 * X2 * A * B * C ( D E )
where A, B, C, D, and E are classification variables and X1 and X2 are continuous variables.
When the input DATA= data set is not a specially structured SAS data set, you must also specify standard errors of the parameter estimates in an STDERR statement.