Input Data Sets

The MI Procedure

Input Data Sets

You can specify the input data set with missing values with the DATA= option in the PROC MI statement. When a MCMC method is used, you can specify the data set containing the reference distribution information for imputation with the INEST= option, the data set containing initial parameter estimates for the MCMC process with the INITIAL=INPUT= option, and the data set containing information for the prior distribution with the PRIOR=INPUT= option in the MCMC statement.

DATA=SAS-data-set

The input DATA= data set is an ordinary SAS data set containing multivariate data with missing values.

INEST=SAS-data-set

The input INEST= data set is a TYPE=EST data set and contains a variable _Imputation_ to identify the imputation number. For each imputation, PROC MI reads the point estimate from the observations with _TYPE_=`PARM' or _TYPE_=`PARMS' and the associated covariances from the observations with _TYPE_=`COV' or _TYPE_=`COVB'. These estimates are used as the reference distribution to impute values for observations in the DATA= data set. When the input INEST= data set also contains observations with _TYPE_=`SEED', PROC MI reads the seed information for the random number generator from these observations. Otherwise, the SEED= option provides the seed information. See Example 9.8 for a usage of this option.

INITIAL=INPUT=SAS-data-set

The input INITIAL=INPUT= data set is a TYPE=COV or CORR data set and provides initial parameter estimates for the MCMC process. The covariances derived from the TYPE=COV/CORR data set are divided by the number of observations to get the correct covariance matrix for the point estimate (sample mean).

If TYPE=COV, PROC MI reads the number of observations from the observations with _TYPE_=`N', the point estimate from the observations with _TYPE_=`MEAN', and the covariances from the observations with _TYPE_=`COV'.

If TYPE=CORR, PROC MI reads the number of observations from the observations with _TYPE_=`N', the point estimate from the observations with _TYPE_=`MEAN', the correlations from the observations with _TYPE_=`CORR', and the standard deviations from the observations with _TYPE_=`STD'.

PRIOR=INPUT=SAS-data-set

The input PRIOR=INPUT= data set is a TYPE=COV data set that provides information for the prior distribution. You can use the data set to specify a prior distribution for ${\Sigma}$ of the form

${\Sigma} \sim W^{-1} ( \, d^{*}, \, d^{*}S^{*} )$

where d^*=n^*-1 is the degrees of freedom. PROC MI reads the matrix S^* from observations with _TYPE_=`COV' and n^* from observations with _TYPE_=`N'.

You can also use this data set to specify a prior distribution for

of the form

${mu} \sim N ( \, {mu}_{0} ,\, \frac 1{n_{0}} {\Sigma} )$

PROC MI reads the mean vector $mu_{0}$ from observations with _TYPE_=`MEAN' and n₀ from observations with _TYPE_=`N_MEAN'. When there are no observations with _TYPE_=`N_MEAN', PROC MI reads n₀ from observations with _TYPE_=`N'.

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