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 EfficiencyNumber of ImputationsImputer’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ModelsAdjusting Imputed Values in Pattern-Mixture ModelsSummary of Issues in Multiple ImputationPlot Options Superseded by ODS GraphicsODS Table NamesODS Graphics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 Methods 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
FREQ Statement
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FREQ variable;
To run a procedure on an input data set that contains observations that occur multiple times, you can use a variable in the data set to represent how frequently observations occur and specify a FREQ statement with the name of that variable as its argument (variable) when you run the procedure.
When you specify a FREQ statement in other SAS procedures, they treat the data set as if each observation appeared n times, where n is the value of variable in the observation. However, PROC MI treats the data set differently because as PROC MI imputes each missing value in each observation, it generates only one imputed value for that missing value. That is, when you specify a FREQ variable, each imputed observation (with its imputed value in place of the missing value) is treated as if it appeared n times. In contrast, if an observation actually occurs n times in the data set, the missing value at each occurrence is imputed separately, and the resulting n observations are not identical.
PROC MI uses only the integer portion of each value of variable; if any value is less than 1, PROC MI does not use the corresponding observation in the analysis. When PROC MI calculates significance probabilities, it considers the total number of observations to be equal to the sum of the values of variable.