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The MI Procedure

Example 54.4 Logistic Regression Method for CLASS Variables

This example uses logistic regression method to impute values for a binary variable in a data set with a monotone missing pattern.

In the following statements, the logistic regression method is used for the binary CLASS variable Species:

   proc mi data=Fish2 seed=1305417 out=outex4;
      class Species;
      monotone logistic( Species= Height Width Height*Width/ details);
      var Height Width Species;
   run;

The "Model Information"  table in Output 54.4.1 describes the method and options used in the multiple imputation process.

Output 54.4.1 Model Information
The MI Procedure

Model Information
Data Set WORK.FISH2
Method Monotone
Number of Imputations 5
Seed for random number generator 1305417

The "Monotone Model Specification"  table in Output 54.4.2 describes methods and imputed variables in the imputation model. The procedure uses the logistic regression method to impute the variable Species in the model. Missing values in other variables are not imputed.

Output 54.4.2 Monotone Model Specification
Monotone Model Specification
Method Imputed Variables
Logistic Regression Species

The "Missing Data Patterns" table in Output 54.4.3 lists distinct missing data patterns with corresponding frequencies and percents. The table confirms a monotone missing pattern for these variables.

Output 54.4.3 Missing Data Patterns
Missing Data Patterns
Group Height Width Species Freq Percent Group Means
Height Width
1 X X X 47 85.45 12.097645 4.808204
2 X X . 6 10.91 11.411050 4.567050
3 X . . 2 3.64 14.126350 .


When you use the DETAILS option, parameters estimated from the observed data and the parameters used in each imputation are displayed in the "Logistic Models for Monotone Method" table in Output 54.4.4.

Output 54.4.4 Logistic Regression Model
Logistic Models for Monotone Method
Imputed
Variable
Effect Obs-Data Imputation
1 2 3 4 5
Species Intercept 2.14183 1.240681 5.018482 5.509416 -1.325099 6.069734
Species Height 9.08604 3.774512 11.322763 11.230355 5.711366 12.766614
Species Width -5.02065 0.674528 -6.245428 -5.785890 2.394018 -9.689260
Species Height*Width -1.91634 -3.299450 -3.326538 -5.045058 -2.570333 -2.214031

The following statements list the first 10 observations of the data set outex4 in Output 54.4.5:

   proc print data=outex4(obs=10);
      title 'First 10 Observations of the Imputed Data Set';
   run;

Output 54.4.5 Imputed Data Set
First 10 Observations of the Imputed Data Set

Obs _Imputation_ Species Length3 Height Width
1 1 Gp1 30.0 11.5200 4.0200
2 1 Gp1 31.2 12.4800 4.3056
3 1 Gp1 31.1 12.3778 4.6961
4 1   33.5 12.7300 .
5 1 Gp1 34.0 12.4440 5.1340
6 1 Gp1 34.7 13.6024 4.9274
7 1 Gp1 34.5 14.1795 5.2785
8 1 Gp1 35.0 12.6700 4.6900
9 1 Gp1 35.1 14.0049 4.8438
10 1 Gp1 36.2 14.2266 4.9594

Note that a missing value of the variable Species is not imputed if the corresponding covariates are missing and not imputed, as shown by observation 4 in the table.

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