printout.m

function printout(out_ticker,MaxDup,contrib_out,prey_num,runique, Lmax)
% generate results table for pop-up message box...
%__________________________________________________________________________

S1=('             %%%%% DIAGNOSTICS AND RESULTS %%%%%');
lnspace=(' ');%line spacer
aa=('Does the number of iterations seem appropriate?');
S1 = strvcat(S1,lnspace,aa);
if out_ticker > 1000
    aa=['     Probably! - there are ',num2str(out_ticker), ' posterior draws (more than 1000)'];
else
    aa=['     Probably not! - there are ',num2str(out_ticker), ' posterior draws (less than 1000)'];
end
S1 = strvcat(S1,aa);
if MaxDup < 2
    aa=['     Probably! - there are no duplicate draws in the posterior chain'];
else
    aa=['     Probably not! - a single posterior draw has been duplicated ',num2str(MaxDup), ' times in the posterior chain'];
end
S1 = strvcat(S1,aa,lnspace);
%put one line spacers here on concat... then
aa =['unique parameter vectors in resample = ',num2str(runique),' out of ' num2str(out_ticker)];
S1 = strvcat(S1,aa);
aa = ['ratio between the posterior at the best draw, and the total posterior density is ', num2str(Lmax)];
S1 = strvcat(S1,aa);
aa = ('   **NOTE- this ratio should be less than .01');
S1 = strvcat(S1,aa,lnspace,lnspace);
%ut two line spaces here on concat... then

 aa = ('Value at each percentile of the posterior proportional contribution of each source (median values @ 50%):');
 S1 = strvcat(S1,aa,lnspace);
 aa= ('5%        25%         50%          75%          95%');
  S1 = strvcat(S1,aa);
 aa=('---------------------------------------------------------------------------');
  S1 = strvcat(S1,aa);
 aa = (prctile(contrib_out,[5 25 50 75 95])'); % a useful summary of x
 aa = num2str(round(aa.*1000) /1000); %rounds to 3 decimal places
 S1 = strvcat(S1,aa,lnspace,lnspace);
%put two line spacers here on concat... then
%fit a beta distrib Beta(A,B) to each posterior source proportion chain,
%and show the results here
aa=('Beta parameter fits (A & B) for each source contribution:');
S1 = strvcat(S1,aa);
aa=('    A                B');
S1 = strvcat(S1,aa);
aa=('-------------------------------');
S1 = strvcat(S1,aa);
for iii = 1:prey_num %for each source
            tempfit = betafit(contrib_out(:,iii)); %get A and B from betafit function based on data
            beta_out(iii,1)= tempfit(1,1);
            beta_out(iii,2)= tempfit(1,2);
end; 
aa = num2str(beta_out);
S1 = strvcat(S1,aa);

msgbox(S1,'Model Run Results');