MI Procedure


The INITIAL= option in the EM statement sets the initial estimates for the EM algorithm. Either the means and covariances from complete cases or the means and standard deviations from available cases can be used as the initial estimates for the EM algorithm. You can also specify the correlations for the initial estimates from available cases.

9.1  

For data sets with monotone missingness, the REGPMM option in the MONOTONE statement uses the predictive mean matching method to impute a value randomly from a set of observed values whose predicted values are closest to the predicted value for the missing value from the simulated regression model.

You can specify more than one method in the MONOTONE statement, and for each imputed variable, the covariates can be specified separately.

The DETAILS option in the MONOTONE statement requests the display of the model parameters used for each imputation.

The experimental CLASS statement is now available to specify categorical variables. These classification variables are used either as covariates for imputed variables or as imputed variables for data sets with monotone missing patterns.

The experimental options LOGISTIC and DISCRIM in the MONOTONE statement impute missing categorical variables by logistic and discriminant methods , respectively.




SAS.STAT 9.1 Users Guide (Vol. 6)
SAS.STAT 9.1 Users Guide (Vol. 6)
ISBN: N/A
EAN: N/A
Year: 2004
Pages: 127

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