options ls=80;

* Original Data;

data orig;
    input cross $ genotype $ n mt1 mt2;
    cards;
bc AaBb 162 1 -1
bc Aabb 40  0  1
bc aaBb 40  0  1
bc aabb 158 1 -1

* Some bookkeeping;
    
proc sort;by cross;
proc means noprint;by cross;
var n;
output sum=tot out=total;
data orig;
    retain rcum 0;
    merge orig total;by cross;
    r=n/tot;
    rcum=rcum+r;

* Our Bootstrap sample;
    
data boot;set orig;
    do rep=1 to 100;
        do i=1 to tot;
           x=ranuni(0);
           if x > 0 and x <= r then output;
           n=1;
           x=x-r;
           end;
       end;
proc sort;by rep cross genotype;
proc means noprint;by rep cross genotype;
    var n mt1 mt2 tot;
    output sum=n smt1 smt2 stot mean=nr mt1 mt2 tot;
data;set;r=n/tot;
ods listing close;
proc print;
    var rep cross genotype n mt1 mt2 tot r ;

* The analysis;

proc nlin nohalve sigsq=1 outest=results; by rep cross;
    parms theta .2;
    model r=.5*(mt1+mt2*theta);
    _weight_=n/model.r;
    _loss_=r*model.r*log(model.r);
    run;
    ods listing;
proc print data=results;where _TYPE_='FINAL';
    var rep cross theta;
proc sort;by cross;

* Bootstrap mean and variance;
    
proc means;where _type_='FINAL';by cross;
    var theta;
    run;