$title Ranking Multidimensional Data

set     t       Set on which random data are defined /1*30/,

*       Other sets:

        p       /p1*p3/,
        rep     /rep1*rep2/
        vcp     /vcp1*vcp2/
        ares    /ares1*ares3/;

set     k       Ordered set over all observations in all realizations /k1*k1080/,
        k1(k)   First element of the ordered set of realizations /k1/;

parameter       StratanimB(P,rep,vcp,Ares,t)    Computed data to be sorted,
                v(k)                            Values to be ranked (one-dimensional!),
                r(k)                            Rank values (one-dimensional!),
                pct(*)                          Percentile to be computed /median 50/;

*       Run the model to generate some data.  Here we simply randomize:

loop((p,rep,vcp,ares,t), StratanimB(p,rep,vcp,ares,t) = uniform(0,1););

*       Transfer data from the multidimensional set to a single dimensional set:

scalar  count   Counter on set element in set k /0/;

loop(k1(k),
  loop((p,rep,vcp,ares,t), 
    v(k+count) = StratanimB(p,rep,vcp,ares,t);
    count = count + 1;
  );
);

*       Initialize the ranking routine:

$libinclude rank v k r pct

parameter       rank(p,rep,vcp,ares,t)  Rank order of multidimensional data;

count = 0;
loop(k1(k),
  loop((p,rep,vcp,ares,t), 
    rank(p,rep,vcp,ares,t) = r(k+count);
    count = count + 1;
  );
);

parameter       rankv(k,p,rep,vcp,ares,t)       Ranked outcomes;
loop(k1(k),
        rankv(k+rank(p,rep,vcp,ares,t),p,rep,vcp,ares,t) = StratanimB(p,rep,vcp,ares,t);
);
option rankv:3:0:1;
display rankv,pct;

$exit


$title Ranking Arrays Within Multidimensional Data

set     t       Set on which random data are defined /1*30/,

*       Other sets:

        p       /p1*p3/,
        rep     /rep1*rep2/
        vcp     /vcp1*vcp2/
        ares    /ares1*ares3/;

parameter
       StratanimB(P,rep,vcp,Ares,t)    Computed data to be sorted
        median(p,rep,vcp,ares)          Median values for items,
        v(t)                            Values to be ranked (one-dimensional!),
        r(t)                            Rank values (one-dimensional!),
        pct(*)                          Percentile to be computed;

*       Run the model to generate some data.  Here we simply randomize:

loop((p,rep,vcp,ares,t), StratanimB(p,rep,vcp,ares,t) = uniform(0,1););


*       Initialize the ranking routine:

$libinclude rank 

loop((p,rep,vcp,ares),

*       Extract the single dimensional data from this point:

        v(t) = StratanimB(P,rep,vcp,Ares,t);

*       NB  pct() is used both for input and output.
*       Define which precentile is to be returned:

        pct("median") = 50;

$libinclude rank v t r pct

*       Retrieve the computed value:

        median(p,rep,vcp,ares) = pct("median");
);
option median:3:0:1;
display median;