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 The MIANALYZE Procedure

## Example 55.8 Reading Mixed Model Results with Classification Variables

This example creates data sets containing parameter estimates and corresponding covariance matrices with classification variables computed by a mixed regression model 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 MIXED to generate the parameter estimates and covariance matrix for each imputed data set:

```proc mixed data=outfish;
class Species;
model Length3= Species Height Width/ solution covb;
by _Imputation_;
ods output SolutionF=mxparms CovB=mxcovb;
run;
```

The following statements display (in Output 55.8.1) the output mixed model coefficients from PROC MIXED for the first two imputed data sets:

```proc print data=mxparms (obs=10);
var _Imputation_ Effect Species Estimate StdErr;
title 'MIXED Model Coefficients (First Two Imputations)';
run;
```

Output 55.8.1 PROC MIXED Model Coefficients
 MIXED Model Coefficients (First Two Imputations)

Obs _Imputation_ Effect Species Estimate StdErr
1 1 Intercept   6.8207 1.0248
2 1 Species Gp1 -0.00467 0.8550
3 1 Species Gp2 0 .
4 1 Height   0.9085 0.1964
5 1 Width   3.2744 0.5643
6 2 Intercept   6.9259 0.9823
7 2 Species Gp1 -0.3542 0.8605
8 2 Species Gp2 0 .
9 2 Height   0.9630 0.1910
10 2 Width   3.1548 0.5370

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

```proc mianalyze parms(classvar=full)=mxparms;
class Species;
modeleffects Intercept Species Height Width;
run;
```

The "Variance Information" table in Output 55.8.2 displays the between-imputation, within-imputation, and total variances for combining complete-data inferences.

Output 55.8.2 Variance Information
The MIANALYZE Procedure

Variance Information
Parameter Species Variance DF Relative
Increase
in Variance
Fraction
Missing
Information
Relative
Efficiency
Between Within Total
Intercept   0.013439 1.008137 1.024263 16136 0.015996 0.015866 0.996837
Species Gp1 0.131130 0.719737 0.877093 124.28 0.218629 0.192300 0.962964
Species Gp2 0 . . . . . .
Height   0.005162 0.037108 0.043302 195.48 0.166925 0.151682 0.970557
Width   0.025787 0.302173 0.333117 463.55 0.102406 0.096781 0.981011

The "Parameter Estimates" table in Output 55.8.3 displays the combined parameter estimates with associated standard errors.

Output 55.8.3 Parameter Estimates
Parameter Estimates
Parameter Species Estimate Std Error 95% Confidence Limits DF Minimum Maximum Theta0 t for H0:
Parameter=Theta0
Pr > |t|
Intercept   6.825324 1.012059 4.84158 8.809072 16136 6.692058 6.956116 0 6.74 <.0001
Species Gp1 -0.174302 0.936532 -2.02792 1.679317 124.28 -0.726082 0.129023 0 -0.19 0.8527
Species Gp2 0 . . . . 0 0 0 . .
Height   0.927390 0.208091 0.51700 1.337782 195.48 0.860775 1.034011 0 4.46 <.0001
Width   3.242346 0.577163 2.10817 4.376527 463.55 3.011865 3.400082 0 5.62 <.0001

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