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The MI Procedure

Example 54.4 Logistic Regression Method for CLASS Variables

This example uses logistic regression method to impute values for a binary variable in a data set with a monotone missing pattern.

In the following statements, the logistic regression method is used for the binary CLASS variable Species:

   proc mi data=Fish2 seed=1305417 out=outex4;
      class Species;
      monotone logistic( Species= Height Width Height*Width/ details);
      var Height Width Species;
   run;

The "Model Information"  table in Output 54.4.1 describes the method and options used in the multiple imputation process.

Output 54.4.1 Model Information

The MI Procedure

Model Information
Data Set	WORK.FISH2
Method	Monotone
Number of Imputations	5
Seed for random number generator	1305417

The "Monotone Model Specification"  table in Output 54.4.2 describes methods and imputed variables in the imputation model. The procedure uses the logistic regression method to impute the variable Species in the model. Missing values in other variables are not imputed.

The "Missing Data Patterns" table in Output 54.4.3 lists distinct missing data patterns with corresponding frequencies and percents. The table confirms a monotone missing pattern for these variables.

When you use the DETAILS option, parameters estimated from the observed data and the parameters used in each imputation are displayed in the "Logistic Models for Monotone Method" table in Output 54.4.4.

The following statements list the first 10 observations of the data set outex4 in Output 54.4.5:

   proc print data=outex4(obs=10);
      title 'First 10 Observations of the Imputed Data Set';
   run;

Note that a missing value of the variable Species is not imputed if the corresponding covariates are missing and not imputed, as shown by observation 4 in the table.

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