# The MIANALYZE Procedure

### Example 76.8 Reading Mixed Model Results with Classification Covariates

This example creates data sets that contains 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:

```ods select none;
proc mixed data=outfish2;
class Species;
model Length= Species Width/ solution;
by _Imputation_;
ods output SolutionF=mxparms;
run;
ods select all;
```

Because of the ODS SELECT statements, no output is displayed. The following statements display (in Output 76.8.1) the output mixed model coefficients from PROC MIXED for the first two imputed data sets:

```proc print data=mxparms (obs=8);
var _Imputation_ Effect Species Estimate StdErr;
title 'MIXED Model Coefficients (First Two Imputations)';
run;
```

Output 76.8.1: PROC MIXED Model Coefficients

 MIXED Model Coefficients (First Two Imputations)

Obs _Imputation_ Effect Species Estimate StdErr
1 1 Intercept   4.5106 0.8244
2 1 Species Parkki 1.5774 0.7020
3 1 Species Perch 0 .
4 1 Width   5.2585 0.1599
5 2 Intercept   4.5250 0.8771
6 2 Species Parkki 1.4885 0.7693
7 2 Species Perch 0 .
8 2 Width   5.2389 0.1701

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

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

The "Variance Information" table in Output 76.8.2 displays the between-imputation, within-imputation, and total variances for combining complete-data inferences.

Output 76.8.2: Variance Information

The MIANALYZE Procedure

Variance Information (25 Imputations)
Parameter Species Variance DF Relative
Increase
in Variance
Fraction
Missing
Information
Relative
Efficiency
Between Within Total
Intercept   0.065665 0.668667 0.736959 2794.9 0.102131 0.093316 0.996281
Species Parkki 0.077291 0.525640 0.606023 1364.1 0.152924 0.133909 0.994672
Species Perch 0 . . . . . .
Width   0.002276 0.025876 0.028243 3416 0.091488 0.084356 0.996637

The "Parameter Estimates" table in Output 76.8.3 displays the combined parameter estimates with associated standard errors.

Output 76.8.3: Parameter Estimates

Parameter Estimates (25 Imputations)
Parameter Species Estimate Std Error 95% Confidence Limits DF Minimum Maximum Theta0 t for H0:
Parameter=Theta0
Pr > |t|
Intercept   4.519259 0.858463 2.83597 6.202545 2794.9 4.013624 5.162463 0 5.26 <.0001
Species Parkki 1.277902 0.778475 -0.24924 2.805039 1364.1 0.647898 1.710559 0 1.64 0.1009
Species Perch 0 . . . . 0 0 0 . .
Width   5.284285 0.168056 4.95478 5.613786 3416 5.206516 5.369231 0 31.44 <.0001