Output Data Sets
You can specify the output data set of imputed values
with the OUT= option in the PROC MI statement.
When an EM statement is used, you can specify
the data set containing MLE computed
with the EM algorithm with the OUTEM= option in the EM statement.
When a MCMC method is used, you can specify
the data set containing parameter estimates used in each imputation
with the OUTEST= option and
the data set containing parameters used in the imputation step
for each iteration with the OUTITER option
in the MCMC statement.
-
OUT=SAS-data-set
- The OUT= data set contains all the variables in the original data set
and a new variable named _Imputation_ that identifies the imputation.
For each imputation, the data set contains all variables in the input
DATA= data set with missing values replaced by imputed values.
-
OUTEM=SAS-data-set
- The OUTEM= data set is a TYPE=COV data set and
contains the MLE computed with the EM algorithm.
The observations with _TYPE_=`MEAN'
contain the estimated mean and the observations
with _TYPE_=`COV' contain the estimated covariances.
-
OUTEST=SAS-data-set
- The OUTEST= data set is a TYPE=EST data set and
contains parameter estimates used in each imputation in the MCMC method.
It also includes an index variable named
_Imputation_, which identifies the imputation.
The observations with _TYPE_=`SEED' contain the seed
information for the random number generator.
The observations with _TYPE_=`PARM' or
_TYPE_=`PARMS'
contain the point estimate and the observations
with _TYPE_=`COV' or _TYPE_=`COVB' contain
the associated covariances.
These estimates are used as the parameters of the reference distribution
to impute values for observations in the DATA= dataset.
Note that these estimates are the values used in the I-step
before each imputation. These are not the parameter values
simulated from the P-step in the same iteration.
See Example 9.8 for a usage of this option.
-
OUTITER < ( options ) > =SAS-data-set in an EM statement
- The OUTITER= data set in an EM statement is a TYPE=COV data set and
contains parameters for each iteration.
It also includes a variable _Iteration_
that provides the iteration number.
The parameters in the output data set depend on the options specified.
You can specify the MEAN and COV options for OUTITER.
With the MEAN option, the output data set contains the mean parameters
in observations with the variable _TYPE_=`MEAN'.
Similarly, with the MEAN option, the output data set contains the covariance parameters
in observations with the variable _TYPE_=`COV'.
When no options are specified, the output data set contains
the mean parameters for each iteration.
-
OUTITER < ( options ) > =SAS-data-set in a MCMC statement
- The OUTITER= data set in a MCMC statement is a TYPE=COV data set and
contains parameters used in the imputation step for each iteration.
It also includes variables named _Imputation_ and _Iteration_,
which provide the imputation number and iteration number.
The parameters in the output data set depend on the options specified.
The following table summarizes the options available for OUTITER
and the corresponding values for the output variable _TYPE_.
Table 9.3: Summary of Options for OUTITER in a MCMC statement
|
Options
|
Output Parameters
|
_TYPE_
|
| MEAN | mean parameters | MEAN |
| STD | standard deviations | STD |
| COV | covariances | COV |
| LR | -2 log LR statistic | LOG_LR |
| LR_POST | -2 log LR statistic of the posterior mode | LOG_POST |
| WLF | worst linear function | WLF |
When no options are specified, the output data set contains
the mean parameters used in the imputation step for each iteration.
For a detailed description of the worst linear function and LR statistics,
see the "Convergence in MCMC" section.
Copyright © 2001 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
