The MI Procedure

Syntax: MI Procedure

The following statements are available in the MI procedure:

PROC MI <options> ;
BY variables ;
CLASS variables ;
EM <options> ;
FCS <options> ;
FREQ variable ;
MCMC <options> ;
MNAR options ;
MONOTONE <options> ;
TRANSFORM transform (variables</ options>) <…transform (variables</ options>)> ;
VAR variables ;

The BY statement specifies groups in which separate multiple imputation analyses are performed.

The CLASS statement lists the classification variables in the VAR statement. If the MNAR statement is specified, the CLASS statement also includes the identification variables in the MNAR statement. Classification variables can be either character or numeric.

The EM statement uses the EM algorithm to compute the maximum likelihood estimate (MLE) of the data with missing values, assuming a multivariate normal distribution for the data.

The FREQ statement specifies the variable that represents the frequency of occurrence for other values in the observation.

For a data set with a monotone missing pattern, you can use the MONOTONE statement to specify applicable monotone imputation methods; otherwise, you can use either the MCMC statement assuming multivariate normality or the FCS method assuming a joint distribution for variables exists. Note that you can specify no more than one of these statements. When none of these three statements is specified, the MCMC method with its default options is used.

The FCS statement uses a multivariate imputation by chained equations method to impute values for a data set with an arbitrary missing pattern, assuming a joint distribution exists for the data.

The MCMC statement uses a Markov chain Monte Carlo method to impute values for a data set with an arbitrary missing pattern, assuming a multivariate normal distribution for the data.

The MNAR statement imputes missing values, assuming that the missing data are missing not at random (MNAR). The MNAR statement is applicable only if you also specify either an FCS or MONOTONE statement.

The MONOTONE statement specifies monotone methods to impute continuous and classification variables for a data set with a monotone missing pattern.

The TRANSFORM statement specifies the variables to be transformed before the imputation process; the imputed values of these transformed variables are reverse-transformed to the original forms before the imputation.

The VAR statement lists the numeric variables to be analyzed. If you omit the VAR statement, all numeric variables not listed in other statements are used.

The PROC MI statement is the only required statement for the MI procedure. The rest of this section provides detailed syntax information for each of these statements, beginning with the PROC MI statement. The remaining statements are presented in alphabetical order.