The MI Procedure
- Overview
- Getting Started
-
Syntax
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Details
Descriptive Statistics EM Algorithm for Data with Missing Values Statistical Assumptions for Multiple Imputation Missing Data Patterns Imputation Methods Monotone Methods for Data Sets with Monotone Missing Patterns Monotone and FCS Regression Methods Monotone and FCS Predictive Mean Matching Methods Monotone Propensity Score Method Monotone and FCS Discriminant Function Methods Monotone and FCS Logistic Regression Methods FCS Methods for Data Sets with Arbitrary Missing Patterns Checking Convergence in FCS Methods MCMC Method for Arbitrary Missing Multivariate Normal Data Producing Monotone Missingness with the MCMC Method MCMC Method Specifications Checking Convergence in MCMC Input Data Sets Output Data Sets Combining Inferences from Multiply Imputed Data Sets Multiple Imputation Efficiency Imputer’s Model Versus Analyst’s Model Parameter Simulation versus Multiple Imputation Summary of Issues in Multiple Imputation ODS Table Names ODS Graphics
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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
- 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 56.8. For more information about ODS, see Chapter 20, Using the Output Delivery System.
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 |