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Example 9.4: MCMC Method

This example uses the MCMC method to impute missing values for a data set with an arbitrary missing pattern. The following statements invoke the MI procedure and specify the MCMC method with three imputations.

   proc mi data=FitMiss seed=55417 nimpute=3 mu0=50 10 180;
      mcmc chain=multiple displayinit initial=em(itprint);   
      var Oxygen RunTime RunPulse; 
   run;

With CHAIN=MULTIPLE, the procedure uses multiple chains and completes the default 200 burn-in iterations before each imputation. The 200 burn-in iterations are used to make the iterations converge to the stationary distribution before the imputation.

By default, the procedure uses a noninformative Jeffreys prior to derive the posterior mode from the EM algorithm as the starting values for the MCMC process.

The following "Missing Data Patterns" table lists distinct missing data patterns with corresponding statistics.

With the ITPRINT option in INITIAL=EM, the procedure also displays the "EM (Posterior Mode) Iteration History" table.

With the DISPLAYINIT option in the MCMC statement, the following "Initial Parameter Estimates for MCMC" table displays the starting mean and covariance estimates used in MCMC. The same starting estimates are used for the MCMC process for multiple chains because the EM algorithm is applied to the same data set in each chain. You can explicitly specify different initial estimates for different imputations, or you can use the bootstrap to generate different parameter estimates from the EM algorithm for the MCMC process.

The following two tables display variance information and parameter estimates from the multiple imputation.

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