The MI Procedure

Example 56.6 FCS Method for Continuous Variables

This example uses FCS regression methods to impute values for all continuous variables in a data set with an arbitrary missing pattern.

The following statements invoke the MI procedure and impute missing values for the Fitness1 data set:

proc mi data=Fitness1 seed=1213 nimpute=4 mu0=50 10 180 out=outex6;
   fcs nbiter=10 reg(/details);
   var Oxygen RunTime RunPulse;
run;

The NIMPUTE= option specifies the total number of imputations. The FCS statement requests multivariate imputations by FCS methods, and the NBITER=10 option (which is the default) specifies the number of burn-in iterations before each imputation.

The "Model Information"  table in Output 56.6.1 describes the method and options used in the multiple imputation process.

The "FCS Model Specification"  table in Output 56.6.2 describes methods and imputed variables in the imputation model. With the REG option in the FCS statement, the procedure uses the regression method to impute variables RunTime, RunPulse, and Oxygen in the model.

The "Missing Data Patterns" table in Output 56.6.3 lists distinct missing data patterns with corresponding frequencies and percentages. With the default ORDER=FREQ option, variables are ordered by the descending frequency counts for the missing values in the filled-in and imputation phases.

When you use the DETAILS option, the parameters used in each imputation are displayed in Output 56.6.4, Output 56.6.5, and Output 56.6.6.

The following statements list the first 10 observations of the data set outex6 in Output 56.6.7. Note that all missing values of all variables are imputed.

proc print data=outex6(obs=10);
   title 'First 10 Observations of the Imputed Data Set';
run;

After the completion of the specified four imputations, the "Variance Information" table in Output 56.6.8 displays the between-imputation variance, within-imputation variance, and total variance for combining complete-data inferences. The relative increase in variance due to missingness, the fraction of missing information, and the relative efficiency for each variable are also displayed. These statistics are described in the section Combining Inferences from Multiply Imputed Data Sets.

The "Parameter Estimates" table in Output 56.6.9 displays a 95% mean confidence interval and a t statistic with its associated p-value for each of the hypotheses requested with the MU0= option.