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- load('CtrlDreaddCumValsNew.mat', 'untrained','pre_norm_last', 'post_norm_last','Ctrl','Exp')
- %%
- %% % Mean
- %dci = std(data)*tinv(0.975,size(data,1)-1); % Confidence Intervals
- xt = [1:5]; % X-Ticks
- lb = [xt'-ones(5,1)*0.2, xt'+ones(5,1)*0.2]; % Long Bar X
- figure
- plot(xt(1), untrained, 'o', 'color', [0.5 0.5 0.5],'MarkerSize', 10)
- hold on
- plot(xt(2),Ctrl , 'ko','MarkerSize', 10)
- plot(xt(3), Exp, 'ro','MarkerSize', 10)
- plot(xt(4), pre_norm_last, 'ro','MarkerSize', 10)
- plot(xt(5), post_norm_last, 'ro','MarkerSize', 10)
- dmean = [mean(untrained), mean(Ctrl), mean(Exp), mean(pre_norm_last), mean(post_norm_last)];
- %dmedian = [median(untrained), median(Ctrl), median(Exp), median(expert)];
- for k1 = 1:5
- %plot(lb(k1,:), [1 1]*dmedian(k1), '-k')
- plot(lb(k1,:), [1 1]*dmean(k1), '-b')
- end
- hold off
- set(gca, 'XTick', xt, 'XTickLabel', {'Untrained','Ctrl','hM4Di/CNO', 'Expert preCNO','Expert postCNO'})
- xlabel('Group')
- ylabel('Normalized learning score')
- xlim([0.5 5.5])
- ylim([-1 1])
- set(gca,'YTick', [-1:0.5:1])
- %% Statistics
- [h, p] = swtest(Ctrl)
- [h, p] = swtest(Exp)
- % Parametric
- group = [ones(20,1); ones(12,1)+1; ones(5,1)+2];
- [p,tbl,stats] = anova1([Ctrl; Exp ; untrained],group,'off')
- c = multcompare(stats,'CType', 'bonferroni')
- [h p] =ttest2(Ctrl, Exp)
- %% non-parametric
- [p,tbl,stats] = kruskalwallis([Ctrl; Exp ; untrained],group,'off')
- c = multcompare(stats,'CType', 'dunn-sidak')
- [p h] =ranksum(Ctrl, Exp)
- [p h] = ranksum(Ctrl, untrained)
- [p h] = ranksum(untrained, Exp)
- [p h] = ranksum(Ctrl, post_norm_last)
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