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% tdcssummary.patient_id = data(:,1);
% tdcssummary.subject_id = data(:,2);
% tdcssummary.sex = categorical(stringVectors(:,1));
% tdcssummary.date_of_birth = datetime([dates{:,1}].', 'ConvertFrom', 'Excel');
% tdcssummary.lesion_side = categorical(stringVectors(:,2));
% tdcssummary.affected_upperlimbside = categorical(stringVectors(:,3));
% tdcssummary.lateralisation = categorical(stringVectors(:,4));
% tdcssummary.stroke_date = datetime([dates{:,2}].', 'ConvertFrom', 'Excel');
% tdcssummary.stroke_location = categorical(stringVectors(:,5));
% tdcssummary.treatment = data(:,3);
% tdcssummary.fma_pre_date = datetime([dates{:,3}].', 'ConvertFrom', 'Excel');
% tdcssummary.fma_prescore = data(:,4);
% tdcssummary.ninehole_pre_date = datetime([dates{:,4}].', 'ConvertFrom', 'Excel');
% tdcssummary.ninehole_comment = stringVectors(:,6);
% tdcssummary.ninehole_Asprescoresec = data(:,5);
% tdcssummary.ninehole_Asprescorepegsec = data(:,6);
% tdcssummary.ninehole_USprescoresec = data(:,7);
% tdcssummary.ninehole_USprescorepegsec = data(:,8);
% tdcssummary.ninehole_prescoreratioASUS = data(:,9);
% tdcssummary.arat_pre_date = datetime([dates{:,5}].', 'ConvertFrom', 'Excel');
% tdcssummary.arat_prescore = data(:,10);
% tdcssummary.jamar_pre_date = datetime([dates{:,6}].', 'ConvertFrom', 'Excel');
% tdcssummary.jamar_comment = stringVectors(:,7);
% tdcssummary.jamar_ASprescoreKgF = data(:,11);
% tdcssummary.jamar_USprescore = data(:,12);
% tdcssummary.mif_pre_date = datetime([dates{:,7}].', 'ConvertFrom', 'Excel');
% tdcssummary.mif_prescore = data(:,13);
% tdcssummary.tms_pre_date = datetime([dates{:,8}].', 'ConvertFrom', 'Excel');
% tdcssummary.tms_ASMTpre = data(:,14);
% tdcssummary.tms_USMTpre = data(:,15);
% tdcssummary.tms_post_date = datetime([dates{:,9}].', 'ConvertFrom', 'Excel');
% tdcssummary.tms_ASMTpost = data(:,16);
% tdcssummary.tms_USMTpost = data(:,17);
% tdcssummary.fma_post_date = datetime([dates{:,10}].', 'ConvertFrom', 'Excel');
% tdcssummary.fma_postscore = data(:,18);
% tdcssummary.ninehole_post_date = datetime([dates{:,11}].', 'ConvertFrom', 'Excel');
% tdcssummary.ninehole_post_comment = stringVectors(:,8);
% tdcssummary.ninehole_ASpostscoresec = data(:,19);
% tdcssummary.ninehole_ASpostscorepegsec = data(:,20);
% tdcssummary.ninehole_USpostscoresec = data(:,21);
% tdcssummary.ninehole_USpostscorepegsec = data(:,22);
% tdcssummary.ninehole_postratioASUS = data(:,23);
% tdcssummary.arat_post_date = datetime([dates{:,12}].', 'ConvertFrom', 'Excel');
% tdcssummary.arat_postscore = data(:,24);
% tdcssummary.jamar_post_date = datetime([dates{:,13}].', 'ConvertFrom', 'Excel');
% tdcssummary.jamar_post_comment = stringVectors(:,9);
% tdcssummary.jamar_AspostscoreKgF = data(:,25);
% tdcssummary.jamarUS_post_scoreKgF = data(:,26);
% tdcssummary.mif_post_date = datetime([dates{:,14}].', 'ConvertFrom', 'Excel');
% tdcssummary.mif_postscore = data(:,27);
% tdcssummary.fma_1monthposttdcs_date = datetime([dates{:,15}].', 'ConvertFrom', 'Excel');
% tdcssummary.fma_comment = stringVectors(:,10);
% tdcssummary.fma_1monthposttdcsscore = data(:,28);
% tdcssummary.ninehole_1monthposttdcs_date = datetime([dates{:,16}].', 'ConvertFrom', 'Excel');
% tdcssummary.ninehole_comment1 = stringVectors(:,11);
% tdcssummary.ninehole_AS1monthposttdcsscoresec = data(:,29);
% tdcssummary.ninehole_AS1monthposttdcsscorepegsec = data(:,30);
% tdcssummary.ninehole_US1monthposttdcsscoresec = data(:,31);
% tdcssummary.ninehole_US1monthposttdcsscorepegsec = data(:,32);
% tdcssummary.ninehole_1monthposttdcs_ratioASUS = data(:,33);
% tdcssummary.arat_1monthposttdcs_date = datetime([dates{:,17}].', 'ConvertFrom', 'Excel');
% tdcssummary.arat_1monthposttdcsscore = data(:,34);
% tdcssummary.jamar_1monthposttdcs_date = datetime([dates{:,18}].', 'ConvertFrom', 'Excel');
% tdcssummary.jamar_comment1 = stringVectors(:,12);
% tdcssummary.jamar_AS1monthposttdcsscoreKgF = data(:,35);
% tdcssummary.jamar_US1monthposttdcsscoreKgF = data(:,36);
% tdcssummary.mif_1monthposttdcs_date = datetime([dates{:,19}].', 'ConvertFrom', 'Excel');
% tdcssummary.mif_1monthposttdcsscore = data(:,37);
% tdcssummary.fma_3monthpoststroke_date = datetime([dates{:,20}].', 'ConvertFrom', 'Excel');
% tdcssummary.fma_3monthpoststrokescore = data(:,38);
% tdcssummary.ninehole_3monthpoststroke_date = datetime([dates{:,21}].', 'ConvertFrom', 'Excel');
% tdcssummary.ninehole_comment2 = stringVectors(:,13);
% tdcssummary.ninehole_AS3monthpoststrokescoresec = data(:,39);
% tdcssummary.ninehole_AS3monthspoststrokescorepegsec = data(:,40);
% tdcssummary.ninehole_US3monthpoststrokescoresec = data(:,41);
% tdcssummary.ninehole_US3monthspoststrokescorepegsec = data(:,42);
% tdcssummary.ninehole_3monthspoststroke_ratioASUS = data(:,43);
% tdcssummary.arat_3monthpoststroke_date = datetime([dates{:,22}].', 'ConvertFrom', 'Excel');
% tdcssummary.arat_3monthpoststrokescore = data(:,44);
% tdcssummary.jamar_3monthpoststroke_date = datetime([dates{:,23}].', 'ConvertFrom', 'Excel');
% tdcssummary.jamar_comment2 = stringVectors(:,14);
% tdcssummary.jamar_AS3monthpoststrokescoreKgF = data(:,45);
% tdcssummary.jamar_US3monthpoststrokescoreKgF = data(:,46);
% tdcssummary.mif_3monthpoststroke_date = datetime([dates{:,24}].', 'ConvertFrom', 'Excel');
% tdcssummary.mif_3monthpoststrokescore = data(:,47);
% tdcssummary.comments = stringVectors(:,15);
function [values] = fmaAnalyses(tdcssummary, figuresPath, current_dir)
%% Get the data per assessment
% Get the columns
fma_columns = [1 2 10 12 35 53 70];
%% Get FMA
fma = tdcssummary(:,fma_columns);
% Get sample size
n_sham = sum(fma.treatment == 'sham')
n_ipsi = sum(fma.treatment == 'ipsi')
n_bihem = sum(fma.treatment == 'bihem')
n_hd = sum(fma.treatment == 'hd')
n = sum(fma.subject_id ~= 0)
% Get sample size with excluded patient that were having upper limb
% difficulties
patientId_2exclude = [7 13];
fma(fma.patient_id ~= patientId_2exclude,:)
fma(~[7 13],:)
n_sham_patientExcluded = sum(fma.treatment(fma.patient_id ~= patientId_2exclude) == 'sham')
n_conv = sum(fma.treatment == 'conv')
n_bihem = sum(fma.treatment == 'bihem')
n_hd = sum(fma.treatment == 'hd')
%% 4 plot to compare pre fma : to show
% clear g_fma
% g_fma(1,1) = gramm('x', fma.treatment, 'y', fma.fma_prescore, 'color', fma.treatment);
% g_fma(1,2)=copy(g_fma(1));
% g_fma(2,1)=copy(g_fma(1));
% g_fma(2,2)=copy(g_fma(1));
%
% %Raw data as scatter plot
% g_fma(1,1).geom_point();
% g_fma(1,1).set_title('geom_point()');
% %Jittered scatter plot
% g_fma(1,2).geom_jitter('width',0.4,'height',0);
% g_fma(1,2).set_title('geom_jitter()');
% %Averages with confidence interval
% g_fma(2,1).stat_summary('geom',{'bar','black_errorbar'});
% g_fma(2,1).set_title('stat_summary()');
% %Boxplots
% g_fma(2,2).stat_boxplot();
% g_fma(2,2).set_title('stat_boxplot()');
%
% % g_fma.axe_property('XTickLabel',{'bh','hd','ipsi', 'sh'}); % change the name of the x labels
% % g_fma.axe_property('XTickLabelRotation',30); % change the rotation of the
% % x labels
% g_fma.set_names('x','Group allocation','y','FMA-UE','color','group');
% g_fma.set_title('tDCS FMA pre');
% figure('Position',[100 100 800 550]);
% g_fma.draw();
% g_fma(1,1).legend_axe_handle.Children(1).String='Q';
% g_fma.legend_axe_handle.Children(1).String
% g_fma(1,1).legend_axe_handle.Children(1).String = {'C'};
%% Statistical test [h,p] = ttest2(x,y);
% Compute data of group against sham
fma_postVSpre = fma.fma_postscore - fma.fma_prescore;
fma_1moVSpre = fma.fma_1monthposttdcsscore - fma.fma_prescore;
fma_3moVSpre = fma.fma_3monthpoststrokescore-fma.fma_prescore;
fma_recovery_percentage_postVSpre = 100 * (fma.fma_postscore - fma.fma_prescore) ./ (66 - fma.fma_prescore);
fma_recovery_percentage_1monthVSpre = 100 * (fma.fma_1monthposttdcsscore - fma.fma_prescore) ./ (66 - fma.fma_prescore);
fma_recovery_percentage_3monthVSpre = 100 * (fma.fma_3monthpoststrokescore - fma.fma_prescore) ./ (66 - fma.fma_prescore);
fma.fma_postVSpre = fma_postVSpre;
fma.fma_1moVSpre = fma_3moVSpre;
fma.fma_3moVSpre = fma_3moVSpre;
fma.fma_recovery_percentage_postVSpre = fma_recovery_percentage_postVSpre;
fma.fma_recovery_percentage_1monthVSpre = fma_recovery_percentage_postVSpre;
fma.fma_recovery_percentage_3monthVSpre = fma_recovery_percentage_3monthVSpre;
% Compute stat analyses against sham
% ttest on FMA score
[h_post_conv, p_post_conv, ci_post_conv, stats_post_conv] = ttest2(fma.fma_postVSpre(fma.treatment == 'conv'),...
fma.fma_postVSpre(fma.treatment == 'sham'));p_post_conv
[h_post_bihem, p_post_bihem, ci_post_bihem, stats_post_bihem] = ttest2(fma.fma_postVSpre(fma.treatment == 'bihem'),...
fma.fma_postVSpre(fma.treatment == 'sham'));p_post_bihem
[h_post_hd, p_post_hd, ci_post_hd, stats_post_hd] = ttest2(fma.fma_postVSpre(fma.treatment == 'hd'),...
fma.fma_postVSpre(fma.treatment == 'sham'));p_post_hd
[h_1mo_conv, p_1mo_conv, ci_1mo_conv, stats_1mo_conv] = ttest2(fma.fma_1moVSpre(fma.treatment == 'conv'),...
fma.fma_1moVSpre(fma.treatment == 'sham'));p_1mo_conv
[h_1mo_bihem, p_1mo_bihem, ci_1mo_bihem, stats_1mo_bihem] = ttest2(fma.fma_1moVSpre(fma.treatment == 'bihem'),...
fma.fma_1moVSpre(fma.treatment == 'sham'));p_1mo_bihem
[h_1mo_hd, p_1mo_hd, ci_1mo_hd, stats_1mo_hd] = ttest2(fma.fma_1moVSpre(fma.treatment == 'hd'),...
fma.fma_1moVSpre(fma.treatment == 'sham'));p_1mo_hd
[h_3mo_conv, p_3mo_conv, ci_3mo_conv, stats_3mo_conv] = ttest2(fma.fma_3moVSpre(fma.treatment == 'conv'),...
fma.fma_3moVSpre(fma.treatment == 'sham'));p_3mo_conv
[h_3mo_bihem, p_3mo_bihem, ci_3mo_bihem, stats_3mo_bihem] = ttest2(fma.fma_3moVSpre(fma.treatment == 'bihem'),...
fma.fma_3moVSpre(fma.treatment == 'sham'));p_3mo_bihem
[h_3mo_hd, p_3mo_hd, ci_3mo_hd, stats_3mo_hd] = ttest2(fma.fma_3moVSpre(fma.treatment == 'hd'),...
fma.fma_3moVSpre(fma.treatment == 'sham'));p_3mo_hd
% ttest on FMA percentage of recovery
[h_percrecovpost_conv, p_percrecovpost_conv, ci_percrecovpost_conv, stats_percrecovpost_conv] = ...
ttest2(fma.fma_recovery_percentage_postVSpre(fma.treatment == 'conv'),...
fma.fma_recovery_percentage_postVSpre(fma.treatment == 'sham')); p_percrecovpost_conv
[h_percrecovpost_bihem, p_percrecovpost_bihem, ci_percrecovpost_bihem, stats_percrecovpost_bihem] = ...
ttest2(fma.fma_recovery_percentage_postVSpre(fma.treatment == 'bihem'),...
fma.fma_recovery_percentage_postVSpre(fma.treatment == 'sham')); p_percrecovpost_bihem
[h_percrecovpost_hd, p_percrecovpost_hd, ci_percrecovpost_hd, stats_percrecovpost_hd] = ...
ttest2(fma.fma_recovery_percentage_postVSpre(fma.treatment == 'hd'),...
fma.fma_recovery_percentage_postVSpre(fma.treatment == 'sham')); p_percrecovpost_hd
[h_percrecov1mo_conv, p_percrecov1mo_conv, ci_percrecov1mo_conv, stats_percrecov1mo_conv] = ...
ttest2(fma.fma_recovery_percentage_1monthVSpre(fma.treatment == 'conv'),...
fma.fma_recovery_percentage_1monthVSpre(fma.treatment == 'sham')); p_percrecov1mo_conv
[h_percrecov1mo_bihem, p_percrecov1mo_bihem, ci_percrecov1mo_bihem, stats_percrecov1mo_bihem] = ...
ttest2(fma.fma_recovery_percentage_1monthVSpre(fma.treatment == 'bihem'),...
fma.fma_recovery_percentage_1monthVSpre(fma.treatment == 'sham')); p_percrecov1mo_bihem
[h_percrecov1mo_hd, p_percrecov1mo_hd, ci_percrecov1mo_hd, stats_percrecov1mo_hd] = ...
ttest2(fma.fma_recovery_percentage_1monthVSpre(fma.treatment == 'hd'),...
fma.fma_recovery_percentage_1monthVSpre(fma.treatment == 'sham')); p_percrecov1mo_hd
[h_percrecov3mo_conv, p_percrecov3mo_conv, ci_percrecov3mo_conv, stats_percrecov3mo_conv] = ...
ttest2(fma.fma_recovery_percentage_3monthVSpre(fma.treatment == 'conv'),...
fma.fma_recovery_percentage_3monthVSpre(fma.treatment == 'sham')); p_percrecov3mo_conv
[h_percrecov3mo_bihem, p_percrecov3mo_bihem, ci_percrecov3mo_bihem, stats_percrecov3mo_bihem] = ...
ttest2(fma.fma_recovery_percentage_3monthVSpre(fma.treatment == 'bihem'),...
fma.fma_recovery_percentage_3monthVSpre(fma.treatment == 'sham')); p_percrecov3mo_bihem
[h_percrecov3mo_hd, p_percrecov3mo_hd, ci_percrecov3mo_hd, stats_percrecov3mo_hd] = ...
ttest2(fma.fma_recovery_percentage_3monthVSpre(fma.treatment == 'hd'),...
fma.fma_recovery_percentage_3monthVSpre(fma.treatment == 'sham')); p_percrecov3mo_hd
values.p_post_conv = p_post_conv;
values.p_post_bihem = p_post_bihem;
values.p_post_hd = p_post_hd;
values.p_1mo_conv = p_1mo_conv;
values.p_1mo_bihem = p_1mo_bihem;
values.p_1mo_hd = p_1mo_hd;
values.p_3mo_conv = p_3mo_conv;
values.p_3mo_bihem = p_3mo_bihem;
values.p_3mo_hd = p_3mo_hd;
values.p_percrecovpost_conv = p_percrecovpost_conv;
values.p_percrecovpost_bihem = p_percrecovpost_bihem;
values.p_percrecovpost_hd = p_percrecovpost_hd;
values.p_percrecov1mo_conv = p_percrecov1mo_conv;
values.p_percrecov1mo_bihem = p_percrecov1mo_bihem;
values.p_percrecov1mo_hd = p_percrecov1mo_hd;
values.p_percrecov3mo_conv = p_percrecov3mo_conv;
values.p_percrecov3mo_bihem = p_percrecov3mo_bihem;
values.p_percrecov3mo_hd = p_percrecov3mo_hd;
%% Geom_jitter plot pre-post-1month-3month : to show
% g_fma_evolution(1,1) = gramm('x', fma.treatment, 'y', fma.fma_prescore, 'color', fma.treatment);
% g_fma_evolution(1,2)= gramm('x', fma.treatment, 'y', fma.fma_postscore, 'color', fma.treatment);
% g_fma_evolution(2,1)= gramm('x', fma.treatment, 'y', fma.fma_1monthposttdcsscore, 'color', fma.treatment);
% g_fma_evolution(2,2)= gramm('x', fma.treatment, 'y', fma.fma_3monthpoststrokescore, 'color', fma.treatment);
%
% g_fma_evolution(1,1).geom_jitter('width',0.4,'height',0);
% g_fma_evolution(1,1).set_title('FMA pre');
%
% g_fma_evolution(1,2).geom_jitter('width',0.4,'height',0);
% g_fma_evolution(1,2).set_title('FMA post');
%
% g_fma_evolution(2,1).geom_jitter('width',0.4,'height',0);
% g_fma_evolution(2,1).set_title('FMA 1 month post ttt');
%
% g_fma_evolution(2,2).geom_jitter('width',0.4,'height',0);
% g_fma_evolution(2,2).set_title('FMA 3 months post stroke');
%
% g_fma_evolution.set_names('x','Group allocation','y','ARAT','color','group');
% g_fma_evolution.set_title('tDCS FMA pre/post/1month/3months');
% figure('Position',[100 100 800 550]);
% g_fma_evolution.draw();
%% Summary plot pre-post-1month-3month : not to show ( not enough patient and plot to be improved )
% clear g_fma_evolution
% g_fma_evolution(1,1) = gramm('x', fma.treatment, 'y', fma.fma_prescore, 'color', fma.treatment);
% g_fma_evolution(1,2)= gramm('x', fma.treatment, 'y', fma.fma_postscore, 'color', fma.treatment);
% g_fma_evolution(2,1)= gramm('x', fma.treatment, 'y', fma.fma_1monthposttdcsscore, 'color', fma.treatment);
% g_fma_evolution(2,2)= gramm('x', fma.treatment, 'y', fma.fma_3monthpoststrokescore, 'color', fma.treatment);
%
% g_fma_evolution(1,1).stat_summary('geom',{'bar','black_errorbar'});
% % g_mif_evolution(1,1).stat_summary('geom',{'point', 'line'});
% g_fma_evolution(1,1).set_title('FMA pre');
%
% g_fma_evolution(1,2).stat_summary('geom',{'bar','black_errorbar'});
% g_fma_evolution(1,2).set_title('FMA post');
%
% g_fma_evolution(2,1).stat_summary('geom',{'bar','black_errorbar'});
% g_fma_evolution(2,1).set_title('FMA 1 month post ttt');
%
% g_fma_evolution(2,2).stat_summary('geom',{'bar','black_errorbar'});
% g_fma_evolution(2,2).set_title('FMA 3 months post stroke');
%
% % g_fma_evolution.axe_property('XTickLabel',{'BH','CA','HD', 'SH'}); % change the name of the x labels
% g_fma_evolution.set_names('x','Group allocation','y','FMA-UE','color','group');
% g_fma_evolution.set_title('tDCS FMA pre/post/1month/3months');
% figure('Position',[100 100 800 550]);
% g_fma_evolution.draw();
%% Boxplot plot pre-post-1month-3month : not to show (raw FMA)
% clear g_fma_evolution
% g_fma_evolution(1,1) = gramm('x', fma.treatment, 'y', fma.fma_prescore, 'color', fma.treatment);
% g_fma_evolution(1,2)= gramm('x', fma.treatment, 'y', fma.fma_postscore, 'color', fma.treatment);
% g_fma_evolution(2,1)= gramm('x', fma.treatment, 'y', fma.fma_1monthposttdcsscore, 'color', fma.treatment);
% g_fma_evolution(2,2)= gramm('x', fma.treatment, 'y', fma.fma_3monthpoststrokescore, 'color', fma.treatment);
%
% g_fma_evolution(1,1).stat_boxplot();
% g_fma_evolution(1,1).set_title('FMA pre');
%
% g_fma_evolution(1,2).stat_boxplot();
% g_fma_evolution(1,2).set_title('FMA post');
%
% g_fma_evolution(2,1).stat_boxplot();
% g_fma_evolution(2,1).set_title('FMA 1 month post ttt');
%
% g_fma_evolution(2,2).stat_boxplot();
% g_fma_evolution(2,2).set_title('FMA 3 months post stroke');
%
% g_fma_evolution.axe_property('YLim',[0 66]);
% % g_fma_evolution.axe_property('XTickLabelRotation',30); % change the rotation of the
% % x labels
% g_fma_evolution.set_names('x','Group allocation','y','FMA-UE','color','group');
% g_fma_evolution.set_title('tDCS FMA pre/post/1month/3months');
% figure('Position',[100 100 800 550]);
% g_fma_evolution.draw();
%% Geom_jitter plot delta fma pre post-1month-3month - pre : version 1
% g_fmaJitter(1,1) = gramm('x', fma.treatment, 'y', fma.fma_prescore, 'color', fma.treatment);
% g_fmaJitter(1,2)= gramm('x', fma.treatment, 'y', fma.fma_postscore-fma.fma_prescore, 'color', fma.treatment);
% g_fmaJitter(2,1)= gramm('x', fma.treatment, 'y', fma.fma_1monthposttdcsscore-fma.fma_prescore, 'color', fma.treatment);
% g_fmaJitter(2,2)= gramm('x', fma.treatment, 'y', fma.fma_3monthpoststrokescore-fma.fma_prescore, 'color', fma.treatment);
%
% g_fmaJitter(1,1).geom_jitter('width',0.4,'height',0);
% g_fmaJitter(1,1).set_title('FMA pre');
%
% g_fmaJitter(1,2).geom_jitter('width',0.4,'height',0);
% g_fmaJitter(1,2).set_title('FMA post');
%
% g_fmaJitter(2,1).geom_jitter('width',0.4,'height',0);
% g_fmaJitter(2,1).set_title('FMA 1 month post ttt');
%
% g_fmaJitter(2,2).geom_jitter('width',0.4,'height',0);
% g_fmaJitter(2,2).set_title('FMA 3 months post stroke');
%
% g_fmaJitter.axe_property('YLim',[0 66]);
% g_fmaJitter.set_names('x','Group allocation','y','FMA-UE','color','group');
% g_fmaJitter.set_title('tDCS delta FMA post/1month/3months - pre');
% figure('Position',[100 100 800 550]);
% g_fmaJitter.draw();
%% Geom_jitter plot delta fma pre post-1month-3month - pre : version 2 : to show
% Patient sample
n_sham = sum(fma.treatment == 'sham')
n_conv = sum(fma.treatment == 'conv')
n_bihem = sum(fma.treatment == 'bihem')
n_hd = sum(fma.treatment == 'hd')
n = sum(fma.subject_id ~= 0)
g_fmaJitter_deltaFMA(1,1)= gramm('x', fma.treatment, 'y', fma_postVSpre, 'color', fma.treatment);
g_fmaJitter_deltaFMA(1,2)= gramm('x', fma.treatment, 'y', fma_1moVSpre, 'color', fma.treatment);
g_fmaJitter_deltaFMA(1,3)= gramm('x', fma.treatment, 'y', fma_3moVSpre, 'color', fma.treatment);
g_fmaJitter_deltaFMA(1,1).geom_jitter('width',0.4,'height',0);
g_fmaJitter_deltaFMA(1,1).set_title('post - pre', 'FontSize', 9);
g_fmaJitter_deltaFMA(1,2).geom_jitter('width',0.4,'height',0);
g_fmaJitter_deltaFMA(1,2).set_title('1 month post ttt - pre', 'FontSize', 9);
g_fmaJitter_deltaFMA(1,3).geom_jitter('width',0.4,'height',0);
g_fmaJitter_deltaFMA(1,3).set_title('3 months post stroke - pre', 'FontSize', 9);
g_fmaJitter_deltaFMA.axe_property('YLim',[-15 66]);
g_fmaJitter_deltaFMA.set_names('x','Group allocation','y','FMA-UE','color','group');
title = 'tDCS delta FMA post/1month/3months - pre';
nInfo_str = ['n = ' num2str(n) ' ; sham = ' num2str(n_sham) ' ; conv = ' num2str(n_conv) ' ; bihem = ' num2str(n_bihem) ' ; hd = ' num2str(n_hd)];
g_fmaJitter_deltaFMA.set_title([title ' | ' nInfo_str], 'FontSize', 11);
figure('Position',[80 180 1100 350]);
g_fmaJitter_deltaFMA.draw();
%% Summary plot pre-post-1month-3month : not to show (not enough patient)
% clear g_fma_evolution
% g_fma_evolution(1,1) = gramm('x', fma.treatment, 'y', fma.fma_prescore, 'color', fma.treatment);
% g_fma_evolution(1,2)= gramm('x', fma.treatment, 'y', fma.fma_postscore-fma.fma_prescore, 'color', fma.treatment);
% g_fma_evolution(2,1)= gramm('x', fma.treatment, 'y', fma.fma_1monthposttdcsscore-fma.fma_prescore, 'color', fma.treatment);
% g_fma_evolution(2,2)= gramm('x', fma.treatment, 'y', fma.fma_3monthpoststrokescore-fma.fma_prescore, 'color', fma.treatment);
%
% g_fma_evolution(1,1).stat_summary('geom',{'bar','black_errorbar'});
% % g_fma_evolution(1,1).stat_summary('type', 'std');
% g_fma_evolution(1,1).set_title('pre');
%
% g_fma_evolution(1,2).stat_summary('geom',{'bar','black_errorbar'});
% g_fma_evolution(1,2).set_title('post VS pre');
%
% g_fma_evolution(2,1).stat_summary('geom',{'bar','black_errorbar'});
% g_fma_evolution(2,1).set_title('1 month VS pre');
%
% g_fma_evolution(2,2).stat_summary('geom',{'bar','black_errorbar'});
% g_fma_evolution(2,2).set_title('3 months VS pre');
%
% g_fma_evolution.set_names('x','Group allocation','y','FMA-UE','color','group');
% g_fma_evolution.set_title('tDCS FMA post/1month/3months VS pre');
% figure('Position',[100 100 800 550]);
% g_fma_evolution.draw();
%% Boxplot plot delta FMA post-1month-3month - pre: version 1
% clear g_fma_evolution
% g_fma_evolution(1,1) = gramm('x', fma.treatment, 'y', fma.fma_prescore, 'color', fma.treatment);
% g_fma_evolution(1,2)= gramm('x', fma.treatment, 'y', fma.fma_postscore-fma.fma_prescore, 'color', fma.treatment);
% g_fma_evolution(2,1)= gramm('x', fma.treatment, 'y', fma.fma_1monthposttdcsscore-fma.fma_prescore, 'color', fma.treatment);
% g_fma_evolution(2,2)= gramm('x', fma.treatment, 'y', fma.fma_3monthpoststrokescore-fma.fma_prescore, 'color', fma.treatment);
%
% g_fma_evolution(1,1).stat_boxplot();
% g_fma_evolution(1,1).set_title('pre');
%
% g_fma_evolution(1,2).stat_boxplot();
% g_fma_evolution(1,2).set_title('post VS pre');
%
% g_fma_evolution(2,1).stat_boxplot();
% g_fma_evolution(2,1).set_title('1 month VS pre');
%
% g_fma_evolution(2,2).stat_boxplot();
% g_fma_evolution(2,2).set_title('3 months VS pre');
%
% g_fma_evolution.axe_property('YLim',[0 66]);
% % g_fma_evolution.axe_property('XTickLabelRotation',30); % change the rotation of the
% % g_fma_evolution.axe_property('XTickLabel',{'BH','CA','HD', 'SH'}); % change the name of the x labels
%
% % x labels
% g_fma_evolution.set_names('x','Group allocation','y','FMA-UE','color','group');
% g_fma_evolution.set_title('tDCS delta FMA : ( post/1month/3months - pre )');
% figure('Position',[100 100 800 550]);
% g_fma_evolution.draw();
%% Boxplot plot delta FMA post-1month-3month - pre: version 2 : Show
% Check the median
% sham = fma_3moVSpre(fma.treatment=='sham');
% median(sham(sham>0))
% bihem = fma_3moVSpre(fma.treatment=='bihem');
% median(bihem(bihem>0))
% All patients
% g_fma_evolution(1,1)= gramm('x', fma.treatment, 'y', fma_evolution_postVSpre, 'color', fma.treatment);
% g_fma_evolution(1,2)= gramm('x', fma.treatment, 'y', fma_evolution_1monthVSpre, 'color', fma.treatment);
% g_fma_evolution(1,3)= gramm('x', fma.treatment, 'y', fma_evolution_3monthVSpre, 'color', fma.treatment);
% Excluding patients with upper limb difficulties
g_fma_evolution(1,1)= gramm('x', fma.treatment(fma_postVSpre>=0), 'y', fma_postVSpre(fma_postVSpre>=0), 'color', fma.treatment(fma_postVSpre>=0));
g_fma_evolution(1,2)= gramm('x', fma.treatment(fma_1moVSpre>=0), 'y', fma_1moVSpre(fma_1moVSpre>=0), 'color', fma.treatment(fma_1moVSpre>=0));
g_fma_evolution(1,3)= gramm('x', fma.treatment(fma_3moVSpre>=0), 'y', fma_3moVSpre(fma_3moVSpre>=0), 'color', fma.treatment(fma_3moVSpre>=0));
g_fma_evolution(1,1).stat_boxplot();
g_fma_evolution(1,1).set_title('post VS pre');
g_fma_evolution(1,2).stat_boxplot();
g_fma_evolution(1,2).set_title('1 month VS pre');
g_fma_evolution(1,3).stat_boxplot();
g_fma_evolution(1,3).set_title('3 months VS pre');
g_fma_evolution.axe_property('YLim',[-15 66]);
% x labels
g_fma_evolution.set_names('x','Group allocation','y','FMA-UE','color','group');
% g_fma_evolution.set_title('tDCS delta FMA All Patients: ( post/1month/3months - pre )');
g_fma_evolution.set_title('tDCS delta FMA Excluding Patients with Upper limb difficulties: ( post/1month/3months - pre )');
figure('Position',[80 180 1100 350]);
g_fma_evolution.draw();
%% Boxplot plot pourcentage de r馗up駻ation FMA post-1month-3month - pre: version 2 : Show
% Pourcentage de r馗up駻ation 100* (FMA(t) - FMA(0)) /(66-FMA(0))
fma_recovery_percentage_postVSpre = 100 * (fma.fma_postscore - fma.fma_prescore) ./ (66 - fma.fma_prescore);
fma_recovery_percentage_1monthVSpre = 100 * (fma.fma_1monthposttdcsscore-fma.fma_prescore) ./ (66 - fma.fma_prescore);
fma_recovery_percentage_3monthVSpre = 100 * (fma.fma_3monthpoststrokescore-fma.fma_prescore) ./ (66 - fma.fma_prescore);
clear g_fma_evolution
g_fma_evolution(1,1)= gramm('x', fma.treatment, 'y', fma_recovery_percentage_postVSpre , 'color', fma.treatment);
g_fma_evolution(1,2)= gramm('x', fma.treatment, 'y', fma_recovery_percentage_1monthVSpre, 'color', fma.treatment);
g_fma_evolution(1,3)= gramm('x', fma.treatment, 'y', fma_recovery_percentage_3monthVSpre, 'color', fma.treatment);
g_fma_evolution(1,1).stat_boxplot();
g_fma_evolution(1,1).set_title('post VS pre');
g_fma_evolution(1,2).stat_boxplot();
g_fma_evolution(1,2).set_title('1 month VS pre');
g_fma_evolution(1,3).stat_boxplot();
g_fma_evolution(1,3).set_title('3 months VS pre');
g_fma_evolution.axe_property('YLim',[-50 100]);
% x labels
g_fma_evolution.set_names('x','Group allocation','y','FMA-UE','color','group');
g_fma_evolution.set_title('tDCS recovery percentage FMA : ( post/1month/3months - pre )');
figure('Position',[100 100 800 550]);
g_fma_evolution.draw();
%% multiple Plot the 70% of proportional recovery and the participants: Show
% x : initial impairment (66 - FMA_initial) FMA(0) : FMA_initial
% y : change in upper limb impairment on Fugl-Meyer assessment relative to
% baseline (Byblow, 2015, Ann. Neu.)
fma_residuelinit = 66-fma.fma_prescore;
deltafma_postpre = fma.fma_postscore - fma.fma_prescore;
deltafma_1mopre = fma.fma_1monthposttdcsscore - fma.fma_prescore;
deltafma_3mopre = fma.fma_3monthpoststrokescore - fma.fma_prescore;
clear g_70prediction;
g_70prediction(1, 1) = gramm('x', fma_residuelinit, 'y', deltafma_postpre , 'color', fma.treatment);
g_70prediction(1, 2) = copy(g_70prediction(1));
g_70prediction(2, 1) = copy(g_70prediction(1));
g_70prediction(2, 2) = copy(g_70prediction(1));
% 70% proportional recovery line
g_70prediction.geom_funline( 'fun', @(x)0.7*x+0.4); %('style' 'k--')
g_70prediction(1,1).geom_point();
g_70prediction(1,1).set_title('geom_point()');
g_70prediction(1,2).stat_glm();
g_70prediction(1,2).set_title('stat_glm()');
g_70prediction(2,1).stat_fit('fun',@(a,b,x)a.*x+b,'intopt','functional');
g_70prediction(2,1).set_title('stat_fit()');
g_70prediction(2,2).stat_summary();
g_70prediction(2,2).set_title('stat_summary()');
% graph aesthetics
g_70prediction.axe_property('XLim',[0 66], 'YLim',[0 66]);
g_70prediction.set_names('x','initial impairment (66 - FMA(0)','y','FMA(t) - FMA(0)','color','group');
g_70prediction.set_title('FMA prediction geom_point()');
figure('Position',[100 100 800 550]);
g_70prediction.draw();
%% geom_point() Plot the 70% of proportional recovery and the participants: Show
% x : initial impairment (66 - FMA_initial) FMA(0) : FMA_initial
% y : change in upper limb impairment on Fugl-Meyer assessment relative to
% baseline (Byblow, 2015, Ann. Neu.)
fma_residuelinit = 66 - fma.fma_prescore;
deltafma_postpre = fma.fma_postscore - fma.fma_prescore;
deltafma_1mopre = fma.fma_1monthposttdcsscore - fma.fma_prescore;
deltafma_3mopre = fma.fma_3monthpoststrokescore - fma.fma_prescore;
clear g_70prediction;
% g_70prediction(1, 1) = gramm('x', fma.fma_prescore, 'y', fma.fma_postscore, 'color', fma.treatment);
% All patients
% g_70prediction(1, 1) = gramm('x', fma_residuelinit, 'y', deltafma_postpre , 'color', fma.treatment);
% g_70prediction(1, 2) = gramm('x', fma_residuelinit, 'y', deltafma_1mopre, 'color', fma.treatment);
% g_70prediction(1, 3) = gramm('x', fma_residuelinit, 'y', deltafma_3mopre, 'color', fma.treatment);
% Excluded patients with upper limb difficulties
g_70prediction(1, 1) = gramm('x', fma_residuelinit, 'y', deltafma_postpre , 'color', fma.treatment);
g_70prediction(1, 2) = gramm('x', fma_residuelinit(deltafma_1mopre>=0), 'y', deltafma_1mopre(deltafma_1mopre>=0), 'color', fma.treatment(deltafma_1mopre>=0));
g_70prediction(1, 3) = gramm('x', fma_residuelinit(deltafma_3mopre>=0), 'y', deltafma_3mopre(deltafma_3mopre>=0), 'color', fma.treatment(deltafma_3mopre>=0));
% 70% proportional recovery line
g_70prediction.geom_funline( 'fun', @(x)0.7*x+0.4); %('style' 'k--')
% g_70prediction(1,1).geom_point();
% g_70prediction(1,1).set_title('pre and post abs score');
g_70prediction(1,1).geom_point();
g_70prediction(1,1).set_title('t = post-treatment');
g_70prediction(1,2).geom_point();
g_70prediction(1,2).set_title('t = 1 month');
g_70prediction(1,3).geom_point();
g_70prediction(1,3).set_title('t = 3 month');
% graph aesthetics
g_70prediction.axe_property('XLim',[0 66], 'YLim',[-15 66]);
g_70prediction.set_names('x','initial impairment (66 - FMA(0))','y','FMA(t) - FMA(0)','color','group');
% g_70prediction.set_title('FMA prediction geom_point() - All patients');
g_70prediction.set_title('FMA prediction geom_point() - Excluded patients with upper limb difficulties');
figure('Position',[100 100 800 550]);
g_70prediction.draw();
%% stat_glm() Plot the 70% of proportional recovery and the participants: Show
% x : initial impairment (66 - FMA_initial) FMA(0) : FMA_initial
% y : change in upper limb impairment on Fugl-Meyer assessment relative to
% baseline (Byblow, 2015, Ann. Neu.)
fma_residuelinit = 66-fma.fma_prescore;
deltafma_postpre = fma.fma_postscore - fma.fma_prescore;
deltafma_1mopre = fma.fma_1monthposttdcsscore - fma.fma_prescore;
deltafma_3mopre = fma.fma_3monthpoststrokescore - fma.fma_prescore;
clear g_70prediction;
% g_70prediction(1, 1) = gramm('x', fma.fma_prescore, 'y', fma.fma_postscore, 'color', fma.treatment);
% All patients
% g_70prediction(1, 1) = gramm('x', fma_residuelinit, 'y', deltafma_postpre , 'color', fma.treatment);
% g_70prediction(1, 2) = gramm('x', fma_residuelinit, 'y', deltafma_1mopre, 'color', fma.treatment);
% g_70prediction(1, 3) = gramm('x', fma_residuelinit, 'y', deltafma_3mopre, 'color', fma.treatment);
% Excluded patients with upper limb difficulties
g_70prediction(1, 1) = gramm('x', fma_residuelinit, 'y', deltafma_postpre , 'color', fma.treatment);
g_70prediction(1, 2) = gramm('x', fma_residuelinit(deltafma_1mopre>=0), 'y', deltafma_1mopre(deltafma_1mopre>=0), 'color', fma.treatment(deltafma_1mopre>=0));
g_70prediction(1, 3) = gramm('x', fma_residuelinit(deltafma_3mopre>=0), 'y', deltafma_3mopre(deltafma_3mopre>=0), 'color', fma.treatment(deltafma_3mopre>=0));
% 70% proportional recovery line
g_70prediction.geom_funline( 'fun', @(x)0.7*x+0.4); %('style' 'k--')
% g_70prediction(1,1).stat_glm();
% g_70prediction(1,1).set_title('t = post-treatment');
g_70prediction(1,1).stat_glm('geom', 'line');
g_70prediction(1,1).set_title('t = post-treatment');
g_70prediction(1,2).stat_glm('geom', 'line');
g_70prediction(1,2).set_title('t = 1 month');
g_70prediction(1,3).stat_glm('geom', 'line');
g_70prediction(1,3).set_title('t = 3 month');
% graph aesthetics
g_70prediction.axe_property('XLim',[0 66], 'YLim',[-10 66]);
g_70prediction.set_names('x','initial impairment (66 - FMA(0)','y','FMA(t) - FMA(0)','color','group');
% g_70prediction.set_title('FMA prediction stat_glm() - All patients');
g_70prediction.set_title('FMA prediction stat_glm() - Excluded patients with upper limb difficulties');
figure('Position',[100 100 800 550]);
g_70prediction.draw();
%% stat_smooth() Plot the 70% of proportional recovery and the participants: No show
% x : initial impairment (66 - FMA_initial) FMA(0) : FMA_initial
% y : change in upper limb impairment on Fugl-Meyer assessment relative to
% baseline (Byblow, 2015, Ann. Neu.)
fma_residuelinit = 66-fma.fma_prescore;
deltafma_postpre = fma.fma_postscore - fma.fma_prescore;
deltafma_1mopre = fma.fma_1monthposttdcsscore - fma.fma_prescore;
deltafma_3mopre = fma.fma_3monthpoststrokescore - fma.fma_prescore;
clear g_70prediction;
% g_70prediction = gramm('x', fma.fma_prescore, 'y', fma.fma_postscore, 'color', fma.treatment);
g_70prediction(1, 1) = gramm('x', fma.fma_prescore, 'y', fma.fma_postscore, 'color', fma.treatment);
g_70prediction(1, 2) = gramm('x', fma_residuelinit, 'y', deltafma_postpre , 'color', fma.treatment);
g_70prediction(2, 1) = gramm('x', fma_residuelinit, 'y', deltafma_1mopre, 'color', fma.treatment);
g_70prediction(2, 2) = gramm('x', fma_residuelinit, 'y', deltafma_3mopre, 'color', fma.treatment);
% 70% proportional recovery line
g_70prediction.geom_funline( 'fun', @(x)0.7*x+0.4); %('style' 'k--')
% g_70prediction.stat_smooth();
% g_70prediction.set_title('t = post-treatment');
g_70prediction(1,1).stat_smooth();
g_70prediction(1,1).set_title('t = post-treatment');
g_70prediction(1,2).stat_smooth();
g_70prediction(1,2).set_title('t = post-treatment');
g_70prediction(2,1).stat_smooth();
g_70prediction(2,1).set_title('t = 1 month');
g_70prediction(2,2).stat_smooth();
g_70prediction(2,2).set_title('t = 3 month');
% graph aesthetics
g_70prediction.axe_property('XLim',[0 66], 'YLim',[0 66]);
g_70prediction.set_names('x','initial impairment (66 - FMA(0)','y','FMA(t) - FMA(0)','color','group');
g_70prediction.set_title('FMA prediction stat_glm()');
figure('Position',[100 100 800 550]);
g_70prediction.draw();
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