Resources

Documentation Example 8 for PROC MIANALYZE


/****************************************************************/
/*          S A S   S A M P L E   L I B R A R Y                 */
/*                                                              */
/*    NAME: MIANAEX8                                            */
/*   TITLE: Documentation Example 8 for PROC MIANALYZE          */
/* PRODUCT: STAT                                                */
/*  SYSTEM: ALL                                                 */
/*    KEYS: multiple imputation                                 */
/*   PROCS: MI, MIANALYZE, MIXED                                */
/*    DATA:                                                     */
/*                                                              */
/* SUPPORT: Yang Yuan             UPDATE: Aug 21, 2007          */
/*     REF: PROC MIANALYZE, EXAMPLE 8                           */
/*    MISC:                                                     */
/****************************************************************/

*-----------------------------Fish2 Data-----------------------------*
| The data set contains two species of the fish (Bream and Pike)     |
| and three measurements: Length, Height, Width.                     |
| Some values have been set to missing, and the resulting data set   |
| has a monotone missing pattern in the variables                    |
| Length, Height, Width, and Species.                                |
*--------------------------------------------------------------------*;
data Fish2;
   title 'Fish Measurement Data';
   input Species $ Length Height Width @@;
   datalines;
Bream   30.0  11.520  4.020         .   31.2  12.480  4.306
Bream   31.1  12.378  4.696     Bream   33.5  12.730  4.456
    .   34.0  12.444   .        Bream   34.7  13.602  4.927
Bream   34.5  14.180  5.279     Bream   35.0  12.670  4.690
Bream   35.1  14.005  4.844     Bream   36.2  14.227  4.959
    .   36.2  14.263   .        Bream   36.2  14.371  4.815
Bream   36.4  13.759  4.368     Bream   37.3  13.913  5.073
Bream   37.2  14.954  5.171     Bream   37.2  15.438  5.580
Bream   38.3  14.860  5.285     Bream   38.5  14.938  5.198
    .   38.6  15.633  5.134     Bream   38.7  14.474  5.728
Bream   39.5  15.129  5.570         .   39.2  15.994   .
Bream   39.7  15.523  5.280     Bream   40.6  15.469  6.131
    .   40.5    .      .        Bream   40.9  16.360  6.053
Bream   40.6  16.362  6.090     Bream   41.5  16.517  5.852
Bream   41.6  16.890  6.198     Bream   42.6  18.957  6.603
Bream   44.1  18.037  6.306     Bream   44.0  18.084  6.292
Bream   45.3  18.754  6.750     Bream   45.9  18.635  6.747
Bream   46.5  17.624  6.371
Pike    34.8   5.568  3.376     Pike    37.8   5.708  4.158
Pike    38.8   5.936  4.384        .    39.8    .      .
Pike    40.5   7.290  4.577     Pike    41.0   6.396  3.977
   .    45.5   7.280  4.323     Pike    45.5   6.825  4.459
Pike    45.8   7.786  5.130     Pike    48.0   6.960  4.896
Pike    48.7   7.792  4.870     Pike    51.2   7.680  5.376
Pike    55.1   8.926  6.171        .    59.7  10.686   .
Pike    64.0   9.600  6.144     Pike    64.0   9.600  6.144
Pike    68.0  10.812  7.480
;


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

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

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

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