The MI Procedure
- Overview
- Getting Started
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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
| TRANSFORM Statement |
The TRANSFORM statement lists the transformations and their associated variables to be transformed. The options are transformation options that provide additional information for the transformation.
The MI procedure assumes that the data are from a multivariate normal distribution when either the regression method or the MCMC method is used. When some variables in a data set are clearly non-normal, it is useful to transform these variables to conform to the multivariate normality assumption. With a TRANSFORM statement, variables are transformed before the imputation process, and these transformed variable values are displayed in all of the results. When you specify an OUT= option, the variable values are back-transformed to create the imputed data set.
The following transformations can be used in the TRANSFORM statement:
- BOXCOX
specifies the Box-Cox transformation of variables. The variable Y is transformed to
, where 
is a constant such that each value of 
must be positive. If the specified constant 
, the logarithmic transformation is used. - EXP
specifies the exponential transformation of variables. The variable Y is transformed to
, where is a constant. - LOG
specifies the logarithmic transformation of variables. The variable Y is transformed to
, where is a constant such that each value of must be positive. - LOGIT
specifies the logit transformation of variables. The variable Y is transformed to
, where the constant 
and the values of 
must be between 0 and 1. - POWER
-
specifies the power transformation of variables. The variable Y is transformed to
, where is a constant such that each value of must be positive and the constant 
. The following options provide the constant
and 
values in the transformations. - C=number
specifies the
value in the transformation. The default is 
for logit transformation and 
for other transformations. - LAMBDA=number
-
specifies the
value in the power and Box-Cox transformations. You must specify the value for these two transformations. For example, the following statement requests that variables
, a logarithmic transformation for the variable y1, and 
, a power transformation for the variable y2, be used in the imputation: transform log(y1) power(y2/c=1 lambda=.5);
If the MU0= option is used to specify a parameter value
for a transformed variable, the same transformation for the variable is also applied to its corresponding MU0= value in the 
test. Otherwise, 
is used for the transformed variable. See Example 56.10 for a usage of the TRANSFORM statement.