The MIANALYZE Procedure
- Overview
- Getting Started
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Syntax
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Details
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Examples
Reading Means and Standard Errors from Variables in a DATA= Data Set Reading Means and Covariance Matrices from a DATA= COV Data Set Reading Regression Results from a DATA= EST Data Set Reading Mixed Model Results from PARMS= and COVB= Data Sets Reading Generalized Linear Model Results Reading GLM Results from PARMS= and XPXI= Data Sets Reading Logistic Model Results from PARMS= and COVB= Data Sets Reading Mixed Model Results with Classification Variables Using a TEST statement Combining Correlation Coefficients
- References
| MODELEFFECTS Statement |
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.