The MIANALYZE Procedure

The following statements generate five imputed data sets to be used in this section. The data set Fitness1 was created in the section Getting Started: MIANALYZE Procedure. For more information about the MI procedure, See Chapter 75: The MI Procedure.

proc mi data=Fitness1 seed=3237851 noprint out=outmi;
   var Oxygen RunTime RunPulse;
run;

The Fish data described in the STEPDISC procedure are measurements of 159 fish of seven species caught in Finland’s Lake Laengelmaevesi. For each fish, the length, height, and width are measured. See Chapter 108: The STEPDISC Procedure, for more information.

The Fish2 data set is constructed from the Fish data set and contains two species of fish. Some values have been set to missing, and the resulting data set has a monotone missing pattern in the variables Length, Width, and Species.

The following statements create the Fish2 data set. It contains two species of fish in the Fish data set.

*-----------------------------Fish2 Data-----------------------------*
| The data set contains two species of the fish (Parkki and Perch)   |
| and two measurements: Length and Width.                            |
| Some values have been set to missing, and the resulting data set   |
| has a monotone missing pattern in the variables                    |
| Length, Width, and Species.                                        |
*--------------------------------------------------------------------*;
data Fish2;
   title 'Fish Measurement Data';
   input Species $ Length Width @@;
   datalines;
Parkki  16.5  2.3265    Parkki  17.4  2.3142    .      19.8   .     
Parkki  21.3  2.9181    Parkki  22.4  3.2928    .      23.2  3.2944
Parkki  23.2  3.4104    Parkki  24.1  3.1571    .      25.8  3.6636
Parkki  28.0  4.1440    Parkki  29.0  4.2340    Perch   8.8  1.4080
.       14.7  1.9992    Perch   16.0  2.4320    Perch  17.2  2.6316
Perch   18.5  2.9415    Perch   19.2  3.3216    .      19.4   .    
Perch   20.2  3.0502    Perch   20.8  3.0368    Perch  21.0  2.7720
Perch   22.5  3.5550    Perch   22.5  3.3075    .      22.5   .    
Perch   22.8  3.5340    .       23.5   .        Perch  23.5  3.5250
Perch   23.5  3.5250    Perch   23.5  3.5250    Perch  23.5  3.9950
.       24.0   .        Perch   24.0  3.6240    Perch  24.2  3.6300
Perch   24.5  3.6260    Perch   25.0  3.7250    .      25.5  3.7230
Perch   25.5  3.8250    Perch   26.2  4.1658    Perch  26.5  3.6835
.       27.0  4.2390    Perch   28.0  4.1440    Perch  28.7  5.1373
.       28.9  4.3350    .       28.9   .        .      28.9  4.5662
Perch   29.4  4.2042    Perch   30.1  4.6354    Perch  31.6  4.7716
Perch   34.0  6.0180    .       36.5  6.3875    .      37.3  7.7957
.       39.0   .        .       38.3   .        Perch  39.4  6.2646
Perch   39.3  6.3666    Perch   41.4  7.4934    Perch  41.4  6.0030
Perch   41.3  7.3514    .       42.3   .        Perch  42.5  7.2250
Perch   42.4  7.4624    Perch   42.5  6.6300    Perch  44.6  6.8684
Perch   45.2  7.2772    Perch   45.5  7.4165    Perch  46.0  8.1420
Perch   46.6  7.5958
;

The following statements generate 25 imputed data sets to be used in this section. The default regression method is used to impute missing values in continuous variable Width, and the discriminant function method is used to impute the variable Species.

proc mi data=Fish2 seed=1305417 out=outfish2;
   class Species;
   monotone logistic( Species= Length Width);
   var Length Width Species;
run;

The Fish3 data set is constructed from the Fish data set and contains three species of fish. Some values have been set to missing, and the resulting data set has an arbitrary missing pattern in the variables Length, Width, and Species.

The following statements create the Fish3 data set. It contains two species of fish in the Fish data set.

*-----------------------------Fish3 Data-----------------------------*
| The data set contains three species of the fish                    |
| (Parkki, Perch, and Roach) and two measurements: Length and Width. |
| Some values have been set to missing, and the resulting data set   |
| has an arbitrary missing pattern in the variables                  |
| Length, Width, and Species.                                        |
*--------------------------------------------------------------------*;
data Fish3;
   title 'Fish Measurement Data';
   input Species $ Length Width @@;
   datalines;
Roach   16.2  2.2680    Roach   20.3  2.8217    Roach   21.2   .    
Roach     .   3.1746    Roach   22.2  3.5742    Roach   22.8  3.3516
Roach   23.1  3.3957    .       23.7   .        Roach   24.7  3.7544
Roach   24.3  3.5478    Roach   25.3   .        Roach   25.0  3.3250
Roach   25.0  3.8000    Roach   27.2  3.8352    Roach   26.7  3.6312
Roach   26.8  4.1272    Roach   27.9  3.9060    Roach   29.2  4.4968
Roach   30.6  4.7736    Roach   35.0  5.3550    Parkki  16.5  2.3265
Parkki  17.4   .        Parkki  19.8  2.6730    Parkki  21.3  2.9181
Parkki  22.4  3.2928    Parkki  23.2  3.2944    Parkki  23.2  3.4104
Parkki  24.1  3.1571    .         .   3.6636    Parkki  28.0  4.1440
Parkki  29.0  4.2340    Perch    8.8  1.4080    .       14.7  1.9992
Perch   16.0  2.4320    Perch   17.2  2.6316    Perch   18.5  2.9415
Perch   19.2  3.3216    .       19.4  3.1234    Perch   20.2   .    
Perch   20.8  3.0368    Perch   21.0  2.7720    Perch   22.5  3.5550
Perch   22.5  3.3075    Perch   22.5  3.6675    Perch     .   3.5340
Perch   23.5  3.4075    Perch   23.5  3.5250    Perch   23.5  3.5250
.       23.5  3.5250    Perch   23.5  3.9950    Perch   24.0  3.6240
Perch   24.0  3.6240    Perch   24.2  3.6300    Perch   24.5  3.6260
Perch   25.0  3.7250    Perch     .   3.7230    Perch   25.5  3.8250
Perch     .   4.1658    Perch   26.5  3.6835    .       27.0  4.2390
Perch     .   4.1440    Perch   28.7  5.1373    .       28.9  4.3350
Perch   28.9  4.3350    Perch   28.9  4.5662    Perch   29.4  4.2042
Perch   30.1  4.6354    Perch   31.6  4.7716    Perch   34.0  6.0180
Perch   36.5  6.3875    Perch   37.3  7.7957    Perch   39.0   .    
Perch   38.3  6.7408    Perch     .   6.2646    .       39.3   .    
Perch   41.4  7.4934    Perch   41.4  6.0030    Perch   41.3  7.3514
Perch   42.3  7.1064    Perch   42.5  7.2250    Perch   42.4  7.4624
Perch   42.5  6.6300    Perch   44.6  6.8684    Perch   45.2  7.2772
Perch   45.5  7.4165    Perch   46.0  8.1420    .       46.6  7.5958
;

The following statements generate 25 imputed data sets to be used in this section. The default regression method is used to impute missing values in continuous variable Width, and the nominal logistic regression method is used to impute the variable Species.

proc mi data=Fish3 seed=30535 out=outfish3;
   class Species;
   fcs logistic ( Species= Length Width / link=glogit);
   var Length Width Species;
run;

Example 76.1 through Example 76.7 use different input option combinations to combine parameter estimates computed from different procedures. Example 76.8 combines parameter estimates with classification variables, and Example 76.9 combines nominal logistic regression parameter estimates Example 76.10 shows the use of a TEST statement, and Example 76.11 combines statistics that are not directly derived from procedures.

The MI procedure provides sensitivity analysis for the MAR assumption. Example 76.12 illustrate sensitivity analysis by using the pattern-mixture model approach, and Example 76.13 performs sensitivity analysis by searching and examining the tipping point that reverses the study conclusion.