The MI Procedure
- Overview
- Getting Started
-
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
PROC MI assigns a name to each table it creates. You must use these names to reference tables when using the Output Delivery System (ODS). These names are listed in Table 63.8. For more information about ODS, see Chapter 20: Using the Output Delivery System.
Table 63.8: ODS Tables Produced by PROC MI
|
ODS Table Name |
Description |
Statement |
Option |
|---|---|---|---|
|
Corr |
Pairwise correlations |
SIMPLE |
|
|
EMEstimates |
EM (MLE) estimates |
EM |
|
|
EMInitEstimates |
EM initial estimates |
EM |
|
|
EMIterHistory |
EM (MLE) iteration |
EM |
ITPRINT |
|
history |
|||
|
EMPostEstimates |
EM (posterior mode) |
MCMC |
INITIAL=EM |
|
estimates |
|||
|
EMPostIterHistory |
EM (posterior mode) |
MCMC |
INITIAL=EM (ITPRINT) |
|
iteration history |
|||
|
EMWLF |
Worst linear function |
MCMC |
WLF |
|
FCSDiscrim |
Discriminant model |
FCS |
DISCRIM (/DETAILS) |
|
group means |
|||
|
FCSLogistic |
Logistic model |
FCS |
LOGISTIC (/DETAILS) |
|
FCSModel |
FCS models |
FCS |
|
|
FCSReg |
Regression model |
FCS |
REG (/DETAILS) |
|
FCSRegPMM |
Predicted mean matching |
FCS |
REGPMM (/DETAILS) |
|
model |
|||
|
MCMCInitEstimates |
MCMC initial estimates |
MCMC |
DISPLAYINIT |
|
MissPattern |
Missing data patterns |
||
|
MNARModel |
Observations that are used |
MNAR |
MODEL |
|
for imputation model |
|||
|
under MNAR |
|||
|
MNARAdjust |
Adjustment parameters and |
MNAR |
ADJUST |
|
imputed values to be adjusted |
|||
|
under MNAR |
|||
|
ModelInfo |
Model information |
||
|
MonoDiscrim |
Discriminant model |
MONOTONE |
DISCRIM (/DETAILS) |
|
group means |
|||
|
MonoLogistic |
Logistic model |
MONOTONE |
LOGISTIC (/DETAILS) |
|
MonoModel |
Monotone models |
MONOTONE |
|
|
MonoPropensity |
Propensity score model |
MONOTONE |
PROPENSITY (/DETAILS) |
|
logistic function |
|||
|
MonoReg |
Regression model |
MONOTONE |
REG (/DETAILS) |
|
MonoRegPMM |
Predicted mean matching |
MONOTONE |
REGPMM (/DETAILS) |
|
model |
|||
|
ParameterEstimates |
Parameter estimates |
||
|
Transform |
Variable transformations |
TRANSFORM |
|
|
Univariate |
Univariate statistics |
SIMPLE |
|
|
VarianceInfo |
Between, within, and |
||
|
total variances |