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 Propensity Score MethodMonotone and FCS Discriminant Function MethodsMonotone and FCS Logistic Regression MethodsFCS 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 ImputationSummary 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 Imputation
- References
ODS Table Names
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 57.8. For more information about ODS, see Chapter 20: Using the Output Delivery System.
Table 57.8: 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 |
||
|
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 |