mri lesional analysis

plots/outcome_plots.m

@@ -244,7 +244,7 @@
              surg_text(strcmp(T.surg_lat,'right')),...
              {'Left','Right'},out_text,'para');
         %}
-        out_lat_stats = unpaired_plot(outcome_num(strcmp(T.surg_lat,'left')),...
+        out_lat_stats = unpaired_plot_temporal_paper(outcome_num(strcmp(T.surg_lat,'left')),...
             outcome_num(strcmp(T.surg_lat,'right')),...
              {'Left','Right'},out_text,'para');
         set(gca().Children(3),'MarkerSize',10)
@@ -359,7 +359,7 @@
         % surgery; probability of right for those with right surgery
         %
 
-        stats = unpaired_plot(concordant_lat_scores(good_outcome),concordant_lat_scores(bad_outcome),...
+        stats = unpaired_plot_temporal_paper(concordant_lat_scores(good_outcome),concordant_lat_scores(bad_outcome),...
         {good_outcome_text,bad_outcome_text},{'Modeled probability of','concordant laterality'},'para');
         set(gca().Children(3),'MarkerSize',10)
         set(gca().Children(4),'MarkerSize',10)
@@ -417,7 +417,7 @@
         laser = strcmp(T.surgery,'Laser ablation');
         resection = contains(T.surgery,'Resection');
         figure
-        type_stats = unpaired_plot(concordant_lat_scores(laser),concordant_lat_scores(resection),...
+        type_stats = unpaired_plot_temporal_paper(concordant_lat_scores(laser),concordant_lat_scores(resection),...
             {'Laser ablation','Resection'},{'Modeled probability of','concordant laterality'},'para');
         set(gca().Children(3),'MarkerSize',10)
         set(gca().Children(4),'MarkerSize',10)

tools/unpaired_plot_temporal_paper.m

@@ -0,0 +1,35 @@
+function stats = unpaired_plot_temporal_paper(a,b,xlabs,ylab,which_test)
+
+na = length(a);
+nb = length(b);
+plot(1+randn(na,1)*0.05,a,'o','linewidth',2)
+hold on
+plot(2+randn(nb,1)*0.05,b,'o','linewidth',2)
+xticks([1 2])
+xticklabels(xlabs)
+ylabel(ylab)
+
+switch which_test
+    case 'non_para'
+        [p,~,stats_stuff] = ranksum(a,b);
+        stats.p = p;
+        stats.ns = [sum(~isnan(a)) sum(~isnan(b))];
+        stats.ranksum = stats_stuff.ranksum;
+    case 'para'
+        [~,p,~,ostats] = ttest2(a,b);
+        stats.p = p;
+        stats.tstat = ostats.tstat;
+        stats.df = ostats.df;
+        stats.means = [nanmean(a) nanmean(b)];
+        stats.sd = [nanstd(a) nanstd(b)];
+end
+yl = ylim;
+ybar = yl(1)+(yl(2)-yl(1))*1.1;
+ytext = yl(1)+(yl(2)-yl(1))*1.15;
+ylnew = [yl(1) yl(1)+(yl(2)-yl(1))*1.2];
+ylim(ylnew)
+plot([1 2],[ybar,ybar],'k','linewidth',2)
+text(1.5,ytext,get_p_text(p),'fontsize',15,'horizontalalignment','center')
+set(gca,'fontsize',15)
+
+end