function bubble(data);

% This program is part of the GRASP package [EXPLORATORY META ANALYSIS ON UNIFORM ASSESSMENT BATTERY 
% ACROSS AGING STUDIES]
 
% Copyright Notice
% [EXPLORATORY META ANALYSIS ON UNIFORM ASSESSMENT BATTERY ACROSS AGING STUDIES]
% ? 2009 Wake Forest University Health Sciences. All Rights Reserved.

% Disclaimer
% [EXPLORATORY META ANALYSIS ON UNIFORM ASSESSMENT BATTERY ACROSS AGING STUDIES] IS AN EXPERIMENTAL 
% RESEARCH PRODUCT. WAKE FOREST UNIVERSITY HEALTH SCIENCES (WFUHS) EXPRESSLY DISCLAIMS ANY WARRANTY 
% FOR [EXPLORATORY META ANALYSIS ON UNIFORM ASSESSMENT BATTERY ACROSS AGING STUDIES], ANY RELATED 
% DOCUMENTATION, AND RELATED MATERIALS ARE PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER 
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% FITNESS FOR A PARTICULAR PURPOSE, OR NONINFRINGEMENT. WFUHS MAKES NO WARRANTY WITH RESPECT TO THE 
% ACCURACY OR COMPLETENESS OF [EXPLORATORY META ANALYSIS ON UNIFORM ASSESSMENT BATTERY ACROSS AGING 
% STUDIES], AND SHALL HAVE NO LIABILITY BASED ON CLAIMS OF ERRORS.  THE ENTIRE RISK ARISING OUT OF 
% THE USE OR PERFORMANCE OF [EXPLORATORY META ANALYSIS ON UNIFORM ASSESSMENT BATTERY ACROSS AGING 
% STUDIES] REMAINS WITH THE USER OF SUCH PROGRAMS.
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% In no event shall WFUHS  be liable for any damages whatsoever (including, without limitation, 
% damages for loss of life or other pecuniary loss) arising out of the use of or inability to use 
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% ACROSS AGING STUDIES].


% HELP
% INPUT:
% data: specify input data (output from SAS) and location.
% For example: data = 'C:\tt.xls';
% OUTPUT:
% bubble plots for biomarkers or other measures.



[num, txt, M]=xlsread(data);
M = M(2:end,:);

b = num(:,1); % Reg coeff;
p = num(:,2); % P value;
ss = num(:,3); % Sample Size;
Ntotal = length(b);

studyid = M(:,1);
studyidu = unique(studyid);
Nstudy = length(studyidu);
study = nan(Ntotal,1);
for s = 1:Nstudy;
  id = strcmp(studyid, studyidu(s));
  study(id) = s;
end;

outcome = M(:,6);
outcomeu = unique(outcome);
Noutcome = length(outcomeu);
y = nan(Ntotal,1);
for i = 1:Noutcome;
  id = strcmp(outcome, outcomeu(i));
  y(id) = i;
end;


bm = M(:,2); %biomarker;
bmu = unique(bm);
Nbm = length(bmu);
bmy = nan(Ntotal,1);


Xset = 1:Noutcome;

whitebg('k');
C = [255 255 255;
  153 0 0;
  225 204 0;
  0 153 0;
  0 0 204]/255;
ttl = bmu;
MS = 7;
for m = 1:length(bmu);
  figure(m);
  clf; hold on;

  for i = 1:Noutcome;
    lg(i) = sum(y ==i & strcmp(bm,bmu(m)) & isnan(ss)==0);
    [aa bb]=max(lg);
  end;
  obs0 = find(y == bb & strcmp(bm,bmu(m)) & isnan(ss)==0);
  [a c]=sort(ss(obs0),'descend');
  so = study(obs0);
  porder = so(c); %plot order;

  for i = 1:Noutcome;
    for n = [porder(2:end)]';
      plot(100,100,'o','MarkerSize',15,'MarkerFaceColor',C(n,:),'MarkerEdgeColor','none');
      hold on;
    end;
    plot(100,100,'w-','LineWidth',2);
    plot([0.5,5],[0 0],'w:'); hold on;
    for j = [porder(2:end)]';
      obs = find(study==j & y ==i & strcmp(bm,bmu(m)));
      if ss(obs) > 0;
        plot(y(obs),b(obs), 'o','MarkerSize',ss(obs)/MS,'MarkerEdgeColor','none','MarkerFaceColor',C(j,:));
      end;
    end;
    for j = porder(1);
      obs = find(study==j & y ==i & strcmp(bm,bmu(m)));
      plot([y(obs)-.25,y(obs)+0.25], [b(obs) b(obs)],'-','LineWidth',2,'Color',C(j,:));
      text(y(obs)+0.3, b(obs), ['p = ' num2str(p(obs),'%6.4f')], 'FontSize',10);
    end;
  end;
  set(gca, 'XTick',[1 2 3 4], 'XTickLabel', outcomeu,'FontSize',12, 'Xlim',[0.5 5],'Ylim',[min(b)-1 max(b)+1]);
  ylabel('Regression Coefficient','FontSize',12);
  legend(studyidu([porder(2:end)' porder(1)]), 'Orientation', ...
    'Horizantal', 'Location',[0.15 0.01 0.7 0.05]);
  %     legend('boxoff');
  title(ttl(m));
  box on;
  set(gcf,'Color','k','InvertHardcopy','off');
end;

return;