/* Create theoretical Poisson Data */
data TheoreticalPoisson;
   mincount = 0;
   maxcount = 30;
   do count= minCount to maxCount;
      p1=pdf('Poisson', count, 0.5);
      p2=pdf('Poisson', count, 1);
      p3=pdf('Poisson', count, 5);
      p4=pdf('Poisson', count, 10);
      output;
   end;
run;


title 'Theoretical Poisson distributions';
proc sgplot data=TheoreticalPoisson noautolegend;
   ****vbarparm category=count response=freq / legendlabel='Count Data';
   series x=count y=p1 / markers markerattrs=(symbol=circlefilled color=red) 
                         lineattrs=(color=red)legendlabel='Fitted Poisson Lambda=0.5 ';
   series x=count y=p2 / markers markerattrs=(symbol=squarefilled color=yellow) 
                             lineattrs=(color=yellow)legendlabel='Fitted Poisson Lambda=1';
   series x=count y=p3 / markers markerattrs=(symbol=squarefilled color=green) 
                             lineattrs=(color=green)legendlabel='Fitted Poisson Lambda=5';                          
   series x=count y=p4 / markers markerattrs=(symbol=squarefilled color=blue) 
                             lineattrs=(color=blue)legendlabel='Fitted Poisson Lambda=10';                          
   ***xaxis display=(nolabel);xaxis label = 'NUMBER OF EGGS COUNTED' fitpolicy=THIN;
   ***yaxis display=(nolabel);yaxis label = 'PROBABILITY MASS FUNCTION';
   keylegend / location=inside position=NE across=1;
run;
title;