Fig1NormLearningScoreAllData.m 1.6 KB

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  1. load('CtrlDreaddCumValsNew.mat', 'untrained','pre_norm_last', 'post_norm_last','Ctrl','Exp')
  2. %%
  3. %% % Mean
  4. %dci = std(data)*tinv(0.975,size(data,1)-1); % Confidence Intervals
  5. xt = [1:5]; % X-Ticks
  6. lb = [xt'-ones(5,1)*0.2, xt'+ones(5,1)*0.2]; % Long Bar X
  7. figure
  8. plot(xt(1), untrained, 'o', 'color', [0.5 0.5 0.5],'MarkerSize', 10)
  9. hold on
  10. plot(xt(2),Ctrl , 'ko','MarkerSize', 10)
  11. plot(xt(3), Exp, 'ro','MarkerSize', 10)
  12. plot(xt(4), pre_norm_last, 'ro','MarkerSize', 10)
  13. plot(xt(5), post_norm_last, 'ro','MarkerSize', 10)
  14. dmean = [mean(untrained), mean(Ctrl), mean(Exp), mean(pre_norm_last), mean(post_norm_last)];
  15. %dmedian = [median(untrained), median(Ctrl), median(Exp), median(expert)];
  16. for k1 = 1:5
  17. %plot(lb(k1,:), [1 1]*dmedian(k1), '-k')
  18. plot(lb(k1,:), [1 1]*dmean(k1), '-b')
  19. end
  20. hold off
  21. set(gca, 'XTick', xt, 'XTickLabel', {'Untrained','Ctrl','hM4Di/CNO', 'Expert preCNO','Expert postCNO'})
  22. xlabel('Group')
  23. ylabel('Normalized learning score')
  24. xlim([0.5 5.5])
  25. ylim([-1 1])
  26. set(gca,'YTick', [-1:0.5:1])
  27. %% Statistics
  28. [h, p] = swtest(Ctrl)
  29. [h, p] = swtest(Exp)
  30. % Parametric
  31. group = [ones(20,1); ones(12,1)+1; ones(5,1)+2];
  32. [p,tbl,stats] = anova1([Ctrl; Exp ; untrained],group,'off')
  33. c = multcompare(stats,'CType', 'bonferroni')
  34. [h p] =ttest2(Ctrl, Exp)
  35. %% non-parametric
  36. [p,tbl,stats] = kruskalwallis([Ctrl; Exp ; untrained],group,'off')
  37. c = multcompare(stats,'CType', 'dunn-sidak')
  38. [p h] =ranksum(Ctrl, Exp)
  39. [p h] = ranksum(Ctrl, untrained)
  40. [p h] = ranksum(untrained, Exp)
  41. [p h] = ranksum(Ctrl, post_norm_last)