The MI Procedure

This example uses discriminant monotone methods to impute values of a CLASS variable from the observed observation values in a data set with a monotone missing pattern.

The following statements impute the continuous variables Height and Width with the regression method and the classification variable Species with the discriminant function method:

proc mi data=Fish2 seed=7545417 nimpute=3 out=outex5;
   class Species;
   monotone reg( Height Width)
            discrim( Species= Length Height Width/ details);
   var Length Height Width Species;
run;

The “Model Information”  table in Output 57.5.1 describes the method and options used in the multiple imputation process.

The “Monotone Model Specification”  table in Output 57.5.2 describes methods and imputed variables in the imputation model. The procedure uses the regression method to impute the variables Height and Width, and uses the logistic regression method to impute the variable Species in the model.

The “Missing Data Patterns” table in Output 57.5.3 lists distinct missing data patterns with corresponding frequencies and percentages. The table confirms a monotone missing pattern for these variables.

When you use the DETAILS option, the parameters estimated from the observed data and the parameters used in each imputation are displayed in Output 57.5.4.

The following statements list the first 10 observations of the data set outex5 in Output 57.5.5. Note that all missing values of the variables Width and Species are imputed.

proc print data=outex5(obs=10);
   title 'First 10 Observations of the Imputed Data Set';
run;