EM Statement
- EM < options > ;
The expectation-maximization (EM) algorithm is a technique
for maximum likelihood estimation in parametric models
for incomplete data.
The EM statement uses the EM algorithm to compute the MLE
for 
, the means and covariance matrix,
of a multivariate normal distribution from the input data set
with missing values.
PROC MI uses the means and standard deviations from available cases
as the initial estimates for the EM algorithm.
The correlations are set to zero.
You can also use the EM statement with the NIMPUTE=0 option
in the PROC statement to compute the EM estimates without
multiple imputation,
as shown in Example 9.1 in the "Examples" section.
The following five options are available with the EM statement.
- CONVERGE=p
-
sets the convergence criterion.
The value must be between 0 and 1.
The iterations are considered to have converged
when the maximum change in the parameter estimates
between iteration steps is less than the value specified.
The change is a relative change if the parameter
is greater than 0.01 in absolute value;
otherwise, it is an absolute change.
By default, CONVERGE=1E-4.
- ITPRINT
-
prints the iteration history in the EM algorithm.
- MAXITER=number
-
specifies the maximum number of iterations used
in the EM algorithm.
The default is MAXITER=200.
- OUTEM=SAS-data-set
-
creates an output SAS data set of TYPE=COV containing
the MLE of the parameter vector .These estimates are computed with the EM algorithm.
See the "Output Data Sets" section for a description of
this output data set.
- OUTITER < ( options ) > =SAS-data-set
-
creates an output SAS data set of TYPE=COV
containing parameters for each iteration.
The data set includes a variable named _Iteration_
to identify the iteration number.
The parameters in the output data set depend on the options specified.
You can specify the MEAN and COV options to output
the mean and covariance parameters.
When no options are specified, the output data set contains
the mean parameters for each iteration.
See the "Output Data Sets" section for a description of
this data set.
Copyright © 2001 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
