# The MI Procedure

### Example 75.3 Monotone Regression Method

This example uses the regression method to impute missing values for all variables in a data set with a monotone missing pattern. The following statements invoke the MI procedure and request the regression method for the variable `Length2` and the predictive mean matching method for variable `Length3`. The resulting data set is named `Outex3`.

```proc mi data=Fish1 round=.1  mu0= 0 35 45
seed=13951639 nimpute=8 out=outex3;
monotone reg(Length2/ details)
regpmm(Length3= Length1 Length2 Length1*Length2/ details);
var Length1 Length2 Length3;
run;
```

The ROUND= option is used to round the imputed values to the same precision as observed values. The values specified with the ROUND= option are matched with the variables `Length1`, `Length2`, and `Length3` in the order listed in the VAR statement. The MU0= option requests t tests for the hypotheses that the population means corresponding to the variables in the VAR statement are `Length2`=35 and `Length3`=45.

The "Missing Data Patterns" table lists distinct missing data patterns with corresponding frequencies and percentages. It is identical to the table in Output 75.2.3 in Example 75.2.

The "Monotone Model Specification" table in Output 75.3.1 displays the model specification.

Output 75.3.1: Monotone Model Specification

The MI Procedure

Monotone Model Specification
Method Imputed Variables
Regression Length2
Regression-PMM( K= 5) Length3

When you use the DETAILS option, the parameters estimated from the observed data and the parameters used in each imputation are displayed in Output 75.3.2 and Output 75.3.3.

Output 75.3.2: Regression Model

Regression Models for Monotone Method
Imputed
Variable
Effect Obs-Data Imputation
1 2 3 4 5 6 7 8
Length2 Intercept -0.04249 -0.049184 -0.055470 -0.051346 -0.064193 -0.030719 -0.030694 -0.050964 -0.017976
Length2 Length1 0.98587 1.001934 0.995275 0.992294 0.983122 0.995883 0.989193 0.968480 0.977476

Output 75.3.3: Regression Predicted Mean Matching Model

Regression Models for Monotone Predicted Mean Matching Method
Imputed
Variable
Effect Obs Data Imputation
1 2 3 4 5 6 7 8
Length3 Intercept -0.01304 0.004134 -0.011417 -0.034177 -0.010532 0.004685 -0.013917 0.012658 -0.020144
Length3 Length1 -0.01332 0.025320 -0.037494 0.308765 0.156606 -0.147118 0.097745 -0.254054 0.086950
Length3 Length2 0.98918 0.955510 1.025741 0.673374 0.828384 1.146440 0.860564 1.226015 0.879854
Length3 Length1*Length2 -0.02521 -0.034964 -0.022017 -0.017919 -0.029335 -0.034671 -0.023384 -0.023829 -0.026785

After the completion of eight imputations (NIMPUTE=8), the "Variance Information" table in Output 75.3.4 displays the between-imputation variance, within-imputation variance, and total variance for combining complete-data inferences. The relative increase in variance due to missingness, the fraction of missing information, and the relative efficiency for each variable are also displayed. These statistics are described in the section Combining Inferences from Multiply Imputed Data Sets.

Output 75.3.4: Variance Information

Variance Information (8 Imputations)
Variable Variance DF Relative
Increase
in Variance
Fraction
Missing
Information
Relative
Efficiency
Between Within Total
Length2 0.000089650 0.439208 0.439309 32.155 0.000230 0.000230 0.999971
Length3 0.000433 0.487356 0.487842 32.13 0.000998 0.000998 0.999875

The "Parameter Estimates" table in Output 75.3.5 displays a 95% mean confidence interval and a t statistic with its associated p-value for each of the hypotheses requested with the MU0= option.

Output 75.3.5: Parameter Estimates

Parameter Estimates (8 Imputations)
Variable Mean Std Error 95% Confidence Limits DF Minimum Maximum Mu0 t for H0:
Mean=Mu0
Pr > |t|
Length2 33.106071 0.662804 31.75624 34.45590 32.155 33.088571 33.117143 35.000000 -2.86 0.0074
Length3 38.416786 0.698457 36.99430 39.83927 32.13 38.382857 38.445714 45.000000 -9.43 <.0001

The following statements list the first 10 observations of the data set `Outex3` in Output 75.3.6. Note that the imputed values of `Length2` are rounded to the same precision as the observed values.

```proc print data=outex3(obs=10);
title 'First 10 Observations of the Imputed Data Set';
run;
```

Output 75.3.6: Imputed Data Set

 First 10 Observations of the Imputed Data Set

Obs _Imputation_ Length1 Length2 Length3
1 1 23.2 25.4 30.0
2 1 24.0 26.3 31.2
3 1 23.9 26.5 31.1
4 1 26.3 29.0 33.5
5 1 26.5 29.0 34.7
6 1 26.8 29.7 34.7
7 1 26.8 28.8 34.7
8 1 27.6 30.0 35.0
9 1 27.6 30.0 35.1
10 1 28.5 30.7 36.2