The MI Procedure
- Overview
- Getting Started
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Syntax
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DetailsDescriptive StatisticsEM Algorithm for Data with Missing ValuesStatistical Assumptions for Multiple ImputationMissing Data PatternsImputation MethodsMonotone Methods for Data Sets with Monotone Missing PatternsMonotone and FCS Regression MethodsMonotone and FCS Predictive Mean Matching MethodsMonotone and FCS Discriminant Function MethodsMonotone and FCS Logistic Regression MethodsMonotone Propensity Score MethodFCS Methods for Data Sets with Arbitrary Missing PatternsChecking Convergence in FCS MethodsMCMC Method for Arbitrary Missing Multivariate Normal DataProducing Monotone Missingness with the MCMC MethodMCMC Method SpecificationsChecking Convergence in MCMCInput Data SetsOutput Data SetsCombining Inferences from Multiply Imputed Data SetsMultiple Imputation EfficiencyImputer’s Model Versus Analyst’s ModelParameter Simulation versus Multiple ImputationSensitivity Analysis for the MAR AssumptionMultiple Imputation with Pattern-Mixture ModelsSpecifying Sets of Observations for Imputation in Pattern-Mixture ModelsAdjusting Imputed Values in Pattern-Mixture ModelsSummary of Issues in Multiple ImputationODS Table NamesODS Graphics
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ExamplesEM Algorithm for MLEMonotone Propensity Score MethodMonotone Regression MethodMonotone Logistic Regression Method for CLASS VariablesMonotone Discriminant Function Method for CLASS VariablesFCS Method for Continuous VariablesFCS Method for CLASS VariablesFCS Method with Trace PlotMCMC MethodProducing Monotone Missingness with MCMCChecking Convergence in MCMCSaving and Using Parameters for MCMCTransforming to NormalityMultistage ImputationCreating Control-Based Pattern Imputation in Sensitivity AnalysisAdjusting Imputed Continuous Values in Sensitivity AnalysisAdjusting Imputed Classification Levels in Sensitivity AnalysisAdjusting Imputed Values with Parameters in a Data Set
- References
This example uses the MCMC method with multiple chains as specified in Example 63.9. It saves the parameter values used for each imputation in an output data set of type EST called Miest. This output data set can then be used to impute missing values in other similar input data sets. The following statements
invoke the MI procedure and specify the MCMC method with multiple chains to create three imputations:
proc mi data=Fitness1 seed=21355417 nimpute=6 mu0=50 10 180; mcmc chain=multiple initial=em outest=miest; var Oxygen RunTime RunPulse; run;
The following statements list the parameters used for the imputations in Output 63.12.1. Note that the data set includes observations with _TYPE_=‘SEED’ which contains the seed to start the next random number generator.
proc print data=miest(obs=15); title 'Parameters for the Imputations'; run;
Output 63.12.1: OUTEST Data Set
| Parameters for the Imputations |
| Obs | _Imputation_ | _TYPE_ | _NAME_ | Oxygen | RunTime | RunPulse |
|---|---|---|---|---|---|---|
| 1 | 1 | SEED | 825240167.00 | 825240167.00 | 825240167.00 | |
| 2 | 1 | PARM | 46.77 | 10.47 | 169.41 | |
| 3 | 1 | COV | Oxygen | 30.59 | -8.32 | -50.99 |
| 4 | 1 | COV | RunTime | -8.32 | 2.90 | 17.03 |
| 5 | 1 | COV | RunPulse | -50.99 | 17.03 | 200.09 |
| 6 | 2 | SEED | 1895925872.00 | 1895925872.00 | 1895925872.00 | |
| 7 | 2 | PARM | 47.41 | 10.37 | 173.34 | |
| 8 | 2 | COV | Oxygen | 22.35 | -4.44 | -21.18 |
| 9 | 2 | COV | RunTime | -4.44 | 1.76 | 1.25 |
| 10 | 2 | COV | RunPulse | -21.18 | 1.25 | 125.67 |
| 11 | 3 | SEED | 137653011.00 | 137653011.00 | 137653011.00 | |
| 12 | 3 | PARM | 48.21 | 10.36 | 170.52 | |
| 13 | 3 | COV | Oxygen | 23.59 | -5.25 | -19.76 |
| 14 | 3 | COV | RunTime | -5.25 | 1.66 | 5.00 |
| 15 | 3 | COV | RunPulse | -19.76 | 5.00 | 110.99 |
The following statements invoke the MI procedure and use the INEST= option in the MCMC statement:
proc mi data=Fitness1 mu0=50 10 180; mcmc inest=miest; var Oxygen RunTime RunPulse; run;
The "Model Information" table in Output 63.12.2 describes the method used in the multiple imputation process. The remaining tables for the example are identical to the tables in Output 63.9.2, Output 63.9.4, Output 63.9.5, and Output 63.9.6 in Example 63.9.