# The MIANALYZE Procedure

### Example 76.7 Reading Logistic Model Results from a PARMS= Data Set

This example creates data sets that contains parameter estimates computed by a logistic regression analysis for a set of imputed data sets. These estimates are then combined to generate valid statistical inferences about the model parameters.

The following statements use PROC LOGISTIC to generate the parameter estimates for each imputed data set:

```ods select none;
proc logistic data=outfish2;
class Species;
model Species= Length Width / covb;
by _Imputation_;
ods output ParameterEstimates=lgsparms;
run;
ods select all;
```

Because of the ODS SELECT statements, no output is displayed. The following statements display (in Output 76.7.1) the output logistic regression coefficients from PROC LOGISTIC for the first two imputed data sets:

```proc print data=lgsparms (obs=6);
title 'LOGISTIC Model Coefficients (First Two Imputations)';
run;
```

Output 76.7.1: PROC LOGISTIC Model Coefficients

 LOGISTIC Model Coefficients (First Two Imputations)

Obs _Imputation_ Variable DF Estimate StdErr WaldChiSq ProbChiSq _ESTTYPE_
1 1 Intercept 1 0.1637 1.8405 0.0079 0.9291 MLE
2 1 Length 1 1.4543 0.5167 7.9231 0.0049 MLE
3 1 Width 1 -10.2950 3.4860 8.7216 0.0031 MLE
4 2 Intercept 1 0.6473 1.9003 0.1160 0.7334 MLE
5 2 Length 1 1.2831 0.4778 7.2123 0.0072 MLE
6 2 Width 1 -9.2991 3.2187 8.3469 0.0039 MLE

The following statements displays the covariance matrices associated with parameter estimates derived from the first two imputations in Output 76.7.2:

The following statements use the MIANALYZE procedure with input PARMS= data set:

```proc mianalyze parms=lgsparms;
modeleffects Intercept Length Width;
run;
```

The "Variance Information" table in Output 76.7.2 displays the between-imputation, within-imputation, and total variances for combining complete-data inferences.

Output 76.7.2: Variance Information

The MIANALYZE Procedure

Variance Information (25 Imputations)
Parameter Variance DF Relative
Increase
in Variance
Fraction
Missing
Information
Relative
Efficiency
Between Within Total
Intercept 0.602599 3.034752 3.661455 819.21 0.206509 0.173178 0.993121
Length 0.077877 0.188227 0.269219 265.18 0.430288 0.306054 0.987906
Width 3.757896 8.382542 12.290754 237.36 0.466232 0.323655 0.987219

The "Parameter Estimates" table in Output 76.7.3 displays the combined parameter estimates with associated standard errors.

Output 76.7.3: Parameter Estimates

Parameter Estimates (25 Imputations)
Parameter Estimate Std Error 95% Confidence Limits DF Minimum Maximum Theta0 t for H0:
Parameter=Theta0
Pr > |t|
Intercept -0.130560 1.913493 -3.8865 3.62537 819.21 -2.321742 0.827356 0 -0.07 0.9456
Length 1.169782 0.518863 0.1482 2.19140 265.18 0.385118 1.554454 0 2.25 0.0250
Width -8.284998 3.505817 -15.1915 -1.37851 237.36 -11.149787 -2.878900 0 -2.36 0.0189