The MI Procedure
- Overview
- Getting Started
-
Syntax
-
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
-
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
The following statements are available in the MI procedure:
PROC MI
<options> ;
BY
variables ;
CLASS
variables ;
EM
<options> ;
FCS
<options> ;
FREQ
variable ;
MCMC
<options> ;
MNAR
options ;
MONOTONE
<options> ;
TRANSFORM
transform (variables</ options>) <…transform (variables</ options>)> ;
VAR
variables ;
The BY statement specifies groups in which separate multiple imputation analyses are performed.
The CLASS statement lists the classification variables in the VAR statement. If the MNAR statement is specified, the CLASS statement also includes the identification variables in the MNAR statement. Classification variables can be either character or numeric.
The EM statement uses the EM algorithm to compute the maximum likelihood estimate (MLE) of the data with missing values, assuming a multivariate normal distribution for the data.
The FREQ statement specifies the variable that represents the frequency of occurrence for other values in the observation.
For a data set with a monotone missing pattern, you can use the MONOTONE statement to specify applicable monotone imputation methods; otherwise, you can use either the MCMC statement assuming multivariate normality or the FCS method assuming a joint distribution for variables exists. Note that you can specify no more than one of these statements. When none of these three statements is specified, the MCMC method with its default options is used.
The FCS statement uses a multivariate imputation by chained equations method to impute values for a data set with an arbitrary missing pattern, assuming a joint distribution exists for the data.
The MCMC statement uses a Markov chain Monte Carlo method to impute values for a data set with an arbitrary missing pattern, assuming a multivariate normal distribution for the data.
The MNAR statement imputes missing values, assuming that the missing data are missing not at random (MNAR). The MNAR statement is applicable only if you also specify either an FCS or MONOTONE statement.
The MONOTONE statement specifies monotone methods to impute continuous and classification variables for a data set with a monotone missing pattern.
The TRANSFORM statement specifies the variables to be transformed before the imputation process; the imputed values of these transformed variables are reverse-transformed to the original forms before the imputation.
The VAR statement lists the numeric variables to be analyzed. If you omit the VAR statement, all numeric variables not listed in other statements are used.
The PROC MI statement is the only required statement for the MI procedure. The rest of this section provides detailed syntax information for each of these statements, beginning with the PROC MI statement. The remaining statements are presented in alphabetical order.