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• SAS/STAT Procedures A-Z

The MI Procedure

• performs multiple imputation of missing data
• creates multiple imputed data sets for incomplete multivariate data
• applies parametric and nonparametric methods for data sets with monotone missing values:
  • continuous variables:
    • regression method
    • predictive mean matching method
    • propensity score method
  • classification variables:
    • logistic regression method
    • discriminant function method
• enables you to specify a multivariate imputation that uses fully conditional specification (FCS) methods
• applies a Markov chain Monte Carlo (MCMC) method for data sets with arbitrary missing patterns
• uses the EM algorithm to compute the MLE for (μ,Σ), the means and covariance matrix, of a multivariate normal distribution from the input data set with missing values
• provides the following transformation methods for data that do not satisfy the assumption of multivariate normality:
  • Box-Cox
  • exponential
  • logarithmic
  • logit
  • power
• obtain separate analyses on observations in groups
• uses ODS to create a SAS data set corresponding to any table
• supports ODS Graphics

Once the m complete data sets are analyzed using standard SAS procedures, the MIANALYZE procedure can be used to generate valid statistical inferences about these parameters by combining results from the m analyses.

For further details see the SAS/STAT User's Guide: The MI Procedure
( PDF | HTML )

Examples

• Generating the Data Sets used in the MI Procedure Examples
• EM Algorithm for MLE
• Monotone Propensity Score Method
• Monotone Regression Method
• Monotone Logistic Regression Method for CLASS Variables
• Monotone Discriminant Function Method for CLASS Variables
• FCS Method for Continuous Variables
• FCS Method for CLASS Variables
• FCS Method with Trace Plot
• MCMC Method
• Producing Monotone Missingness with MCMC
• Checking Convergence in MCMC
• Saving and Using Parameters for MCMC
• Transforming to Normality
• Multistage Imputation

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