S_base: in contr_ga:



     2 :     (* ave. similarity to outgoing neighbours           *)
         if (comp0[0] <= 0.1) then
           begin
           if (comp1[0] <= 0.1) then
           Contr_ga := 0.0
          else
           Contr_ga := comp1[2]/comp1[0];
           end
          else
           begin
           if (comp1[0] <= 0.1) then
           Contr_ga := -comp0[2]/comp0[0]
           else
           Contr_ga := comp1[2]/comp1[0] - comp0[2]/comp0[0];
           end;


gratbeh

zie abcdef

bijj Calccomponents_fa zal wel iets mis gaan.
zie s_base en eight.

gebruikt in UtilityComponents_ya

ActionStatistics 4180


4302        if (delta <0) (* decrease in behaviour h ? *) then
          begin
          CalcComponents_fa(i,NetworkEffects_f,ActionEffects_f,nz,h,k,0,true,
                        Effects_sg,compos_init,outf);
die (initial = true) past het toe op mat = matym[per]
          CalcComponents_fa(i,NetworkEffects_f,ActionEffects_f,nz,h,k,0,false,
                        Effects_sg,compos,outf);
die (initial = false) past het toe op mat = matyn
          for j :=1 to 14 do if effects_sg[firstef+j] then
            gratbeh[j] := gratbeh[j] + Contr_ga(i,j,h,k,delta,
                                 compos_init,compos,halter,outf,outf);


maar vanwege voorbereidingen voor actionstatistics is matyn = matym[per].
dus true / false mag niet uitmaken. OK.

Maar waarom maakt het true maken van alle effects_s en effects_sg wel wat uit?





endowment werkt niet bij nza > 1.
Probleem zit waarschijnlijk bij StoreScoreII




  FunctionEvaluations(ordgs,halter,outf,lf,targets);
  
  for i:=1 to ActiveFunctions do begin
    x := format('%3d%s%s',[i,'. ',StatsDescriptions[i]]);
    outf.Write(pchar(x)^,length(x));
    if endowmentfunction[i] then begin
      x := format('%15.4f',[-Targets[i]])+eol;
      outf.Write(pchar(x)^,length(x));
    end
     else begin
      x := format('%15.4f',[Targets[i]])+eol;
      outf.Write(pchar(x)^,length(x));
     end;
   end;



For the multi-group option, the following arrays are used.

period h is defined as the transition from from observation h to observation h+1.
skipi[h] means that period h is skipped.
nni[h] is the number of actors for period h,
  which is equal to the number of actors in observation h and in observation h+1.
Thus, (not skipi[h]) implies (nni[h] = nni[h+1]).
nmax = max_h nni[h].
Procedure initialise_actors(k) includes setting n := nni[k].
ActualObservation[h] = observation number that is the start of the h'th non-skipped period.

The adaptations of the program in view of allowing the multi-group option are the following.
1. Simulations and other period-depending calculations are skipped if skipi[h].
2. n must be set to its proper value.
3. Arrays with a dimension depending on n that are used for all periods,
   are initialized at the corresponding value equal to nmax.
4. The basic rate parameters and corresponding distance statistics have different positions.
   This is done in NetworkStatistics, ActionStatistics, StorescoreIV, E_Score_Rates, Scores.
   In Scores it uses ActualObservation. (Could be done also in the others.)   

Er zit een fout in ML multi-group, die op lijkt te treden
bij MH_step voor gedrag. (Bij netwerken alleen lijkt het wel goed te gaan.)
Misschien was dit wel de fout in SetAlpha. 

multi niet geimplementeerd in definitie position in MH_P_Step, used only for Bayesian.




