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Documentation Example 9 for PROC MIANALYZE

/****************************************************************/
/*          S A S   S A M P L E   L I B R A R Y                 */
/*                                                              */
/*    NAME: MIANAEX9                                            */
/*   TITLE: Documentation Example 9 for PROC MIANALYZE          */
/* PRODUCT: STAT                                                */
/*  SYSTEM: ALL                                                 */
/*    KEYS: multiple imputation                                 */
/*   PROCS: MI, MIANALYZE, LOGISTIC                             */
/*    DATA:                                                     */
/*                                                              */
/* SUPPORT: Yang Yuan             UPDATE: Aug 21, 2007          */
/*     REF: PROC MIANALYZE, EXAMPLE 9                           */
/*    MISC:                                                     */
/****************************************************************/

*-----------------------------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
;
;

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

ods select none;
proc logistic data=outfish3;
   class Species;
   model Species= Length Width / link=glogit covb;
   by _Imputation_;
   ods output ParameterEstimates=lgsparms
              CovB=lgscovb;
run;
ods select all;

proc print data=lgsparms (obs=12);
   title 'LOGISTIC Model Coefficients (First Two Imputations)';
run;

proc print data=lgscovb (obs=12);
   title 'LOGISTIC Model Covariance Matrices (First Two Imputations)';
run;

proc mianalyze parms(link=glogit)=lgsparms
               covb(effectvar=stacking)=lgscovb
               mult;
   modeleffects Intercept Length Width;
run;