The MIANALYZE Procedure

Examples: MIANALYZE Procedure

The following statements generate five imputed data sets to be used in this section. The data set FitMiss was created in the section Getting Started: MIANALYZE Procedure. See "The MI Procedure" chapter for details concerning the MI procedure.

   proc mi data=FitMiss 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 Laengelmavesi. For each fish, the length, height, and width are measured. Three different length measurements are recorded: from the nose of the fish to the beginning of its tail (Length1), from the nose to the notch of its tail (Length2), and from the nose to the end of its tail (Length3). See Chapter 82, 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 Length3, Height, Width, and Species. Note that some values of the variable Species have also been altered in the data set.

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

   /*-------- Fish of Species Bream and Roach --------*/
    data Fish2 (drop=HtPct WidthPct);
    title 'Fish Measurement Data';
    input Species $ Length3 HtPct WidthPct @@;
    Height= HtPct*Length3/100;
    Width= WidthPct*Length3/100;
    datalines;
    Gp1  30.0 38.4 13.4   Gp1  31.2 40.0 13.8   Gp1  31.1 39.8 15.1
      .  33.5 38.0   .      .  34.0 36.6 15.1   Gp1  34.7 39.2 14.2
    Gp1  34.5 41.1 15.3   Gp1  35.0 36.2 13.4   Gp1  35.1 39.9 13.8
      .  36.2 39.3 13.7   Gp1  36.2 39.4 14.1     .  36.2 39.7 13.3
    Gp1  36.4 37.8 12.0     .  37.3 37.3 13.6   Gp1  37.2 40.2 13.9
    Gp1  37.2 41.5 15.0   Gp1  38.3 38.8 13.8   Gp1  38.5 38.8 13.5
    Gp1  38.6 40.5 13.3   Gp1  38.7 37.4 14.8   Gp1  39.5 38.3 14.1
    Gp1  39.2 40.8 13.7     .  39.7 39.1   .    Gp1  40.6 38.1 15.1
    Gp1  40.5 40.1 13.8   Gp1  40.9 40.0 14.8   Gp1  40.6 40.3 15.0
    Gp1  41.5 39.8 14.1   Gp2  41.6 40.6 14.9   Gp1  42.6 44.5 15.5
    Gp1  44.1 40.9 14.3   Gp1  44.0 41.1 14.3   Gp1  45.3 41.4 14.9
    Gp1  45.9 40.6 14.7   Gp1  46.5 37.9 13.7
    Gp2  16.2 25.6 14.0   Gp2  20.3 26.1 13.9   Gp2  21.2 26.3 13.7
    Gp2  22.2 25.3 14.3   Gp2  22.2 28.0 16.1   Gp2  22.8 28.4 14.7
    Gp2  23.1 26.7 14.7     .  23.7 25.8 13.9   Gp2  24.7 23.5 15.2
    Gp2  24.3 27.3 14.6   Gp2  25.3 27.8 15.1   Gp2  25.0 26.2 13.3
    Gp2  25.0 25.6 15.2   Gp2  27.2 27.7 14.1   Gp2  26.7 25.9 13.6
      .  26.8 27.6 15.4   Gp2  27.9 25.4 14.0   Gp2  29.2 30.4 15.4
    Gp2  30.6 28.0 15.6   Gp2  35.0 27.1 15.3
    ;

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

   proc mi data=Fish2 seed=1305417 out=outfish;
      class Species;
      monotone reg (Width)
               discrim( Species= Length3 Height Width);
      var Length3 Height Width Species;
   run;

Example 55.1 through Example 55.6 use different input option combinations to combine parameter estimates computed from different procedures. Example 55.7 and Example 55.8 combine parameter estimates with classification variables. Example 55.9 shows the use of a TEST statement, and Example 55.10 combines statistics that are not directly derived from procedures.

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