diff --git a/clustergram/OR_clustergram_peryear_RC_2013.m b/clustergram/OR_clustergram_peryear_RC_2013.m new file mode 100644 index 0000000..df89d19 --- /dev/null +++ b/clustergram/OR_clustergram_peryear_RC_2013.m @@ -0,0 +1,93 @@ +%make heatmaps out of the relative risk matrix +years={'2001', '2003', '2005', '2007', '2009', '2011', '2013'}; +cd .. +cd .. +cd matrices +load OR_2013_110314.mat +load qlabel_090914.mat +load order_090914.mat +cd .. +cd programs +cd clustergrams + +%remake cell for each year +q1=odds_ratio_cell(:,1); +q2=odds_ratio_cell(:,2); +q_num=q2(1:64,1); +xlab=q_num; +rel_risk=double.empty; +xlab_=cell.empty; + +for j=7 + for i=1:length(q_num) + q_char=(q_num{i}) ; + indx1=find(strcmp(q1(:),q_char)==1); + n=1; + for k=1:length(indx1) + indx=indx1(k); + if isempty(odds_ratio_cell{indx,j+2})==0 + rel_risk(n,i)=odds_ratio_cell{indx,j+2}; + end + n=n+1; + end + end + sum_column=sum(rel_risk); + indx_0=find(sum_column>0); + rel_risk_=rel_risk(indx_0, indx_0); + xlab_=xlab(indx_0); + qlabel_=qlabel(indx_0,:); + + [r,c]=size(rel_risk_); + rel_risk2=zeros(r,c); + qlabel2=cell(r,1); + m=1; + for i=1:length(order); + in=find(strcmp(qlabel_(:,2),order{i})==1); + if numel(in)>0 + rel_risk2(m,:)=rel_risk_(in,:); %reorder by group + qlabel2(m,1)=qlabel_(in,1); + m=m+1; + end + end + rel_risk3=rel_risk2; + rawOR=flipud(rel_risk3); +% T=rel_risk2; +% indx=find(isinf(T)==1); +% T(indx)=-1; +% maxv=max(max(T)); +% rel_risk2(indx)=maxv; + for i=1:r + temp_mat=rel_risk2(i,:); + noinf=temp_mat; + indx=find(isinf(noinf)==1); + noinf(indx)=-1; + maxv=max(noinf); + temp_mat(indx)=maxv; + rel_risk2(i,:)=temp_mat; + end + [r,c]=size(rel_risk2); + %med=median(rel_risk_2,2); + for i=1:r + med=median(rel_risk2(i,:)); + rel_risk2(i,:)=rel_risk2(i,:)/med; + %indx2=find(isinf(rel_risk2(:,i))==1); + %rel_risk2(indx2,i)=maxv(i); + end + log_rel_risk=log2(rel_risk2); + normOR=flipud(log_rel_risk); + qlab=qlabel_(:,1); + for i=1:r + temp=log_rel_risk(i,:); + minv=min(temp); + indx3=find(log_rel_risk(i,:)<-100000); + log_rel_risk(i,indx3)=minv; + indx3=find(isinf(log_rel_risk(i,:))==1); + log_rel_risk(i,indx3)=maxv; + end + names=flipud(qlabel2); + cg2=clustergram(log_rel_risk,'ColumnLabels',qlab,'Cluster',2, 'Colormap','jet', 'DisplayRange',3); + fig2=plot(cg2); + set(gcf, 'Renderer', 'Painters'); + print (gcf, '-depsc2', [years{j} '_clustergram_OR']); + saveas (gcf, [years{j} '_clustergram_OR.fig']); +end diff --git a/clustergram/OR_clustergram_peryear_RC_2013_subgroup.m b/clustergram/OR_clustergram_peryear_RC_2013_subgroup.m new file mode 100644 index 0000000..445f012 --- /dev/null +++ b/clustergram/OR_clustergram_peryear_RC_2013_subgroup.m @@ -0,0 +1,94 @@ +%make heatmaps out of the relative risk matrix +lab='hispanic_girls'; +years={'2001', '2003', '2005', '2007', '2009', '2011', '2013'}; +cd .. +cd .. +cd matrices +load OR_2013_HISPANIC_GIRLS.mat +load qlabel_090914.mat +load order_090914.mat +cd .. +cd programs +cd clustergrams + +%remake cell for each year +q1=odds_ratio_cell(:,1); +q2=odds_ratio_cell(:,2); +q_num=q2(1:64,1); +xlab=q_num; +rel_risk=double.empty; +xlab_=cell.empty; + +for j=7 + for i=1:length(q_num) + q_char=(q_num{i}) ; + indx1=find(strcmp(q1(:),q_char)==1); + n=1; + for k=1:length(indx1) + indx=indx1(k); + if isempty(odds_ratio_cell{indx,j+2})==0 + rel_risk(n,i)=odds_ratio_cell{indx,j+2}; + end + n=n+1; + end + end + sum_column=sum(rel_risk); + indx_0=find(sum_column>0); + rel_risk_=rel_risk(indx_0, indx_0); + xlab_=xlab(indx_0); + qlabel_=qlabel(indx_0,:); + + [r,c]=size(rel_risk_); + rel_risk2=zeros(r,c); + qlabel2=cell(r,1); + m=1; + for i=1:length(order); + in=find(strcmp(qlabel_(:,2),order{i})==1); + if numel(in)>0 + rel_risk2(m,:)=rel_risk_(in,:); %reorder by group + qlabel2(m,1)=qlabel_(in,1); + m=m+1; + end + end + rel_risk3=rel_risk2; + rawOR=flipud(rel_risk3); +% T=rel_risk2; +% indx=find(isinf(T)==1); +% T(indx)=-1; +% maxv=max(max(T)); +% rel_risk2(indx)=maxv; + for i=1:r + temp_mat=rel_risk2(i,:); + noinf=temp_mat; + indx=find(isinf(noinf)==1); + noinf(indx)=-1; + maxv=max(noinf); + temp_mat(indx)=maxv; + rel_risk2(i,:)=temp_mat; + end + [r,c]=size(rel_risk2); + %med=median(rel_risk_2,2); + for i=1:r + med=median(rel_risk2(i,:)); + rel_risk2(i,:)=rel_risk2(i,:)/med; + %indx2=find(isinf(rel_risk2(:,i))==1); + %rel_risk2(indx2,i)=maxv(i); + end + log_rel_risk=log2(rel_risk2); + normOR=flipud(log_rel_risk); + qlab=qlabel_(:,1); + for i=1:r + temp=log_rel_risk(i,:); + minv=min(temp); + indx3=find(log_rel_risk(i,:)<-100000); + log_rel_risk(i,indx3)=minv; + indx3=find(isinf(log_rel_risk(i,:))==1); + log_rel_risk(i,indx3)=maxv; + end + names=flipud(qlabel2); + cg2=clustergram(log_rel_risk,'ColumnLabels',qlab,'Cluster',2, 'Colormap','jet', 'DisplayRange',3); + fig2=plot(cg2); + set(gcf, 'Renderer', 'Painters'); + print (gcf, '-dpdf', [years{j} '_clustergram_OR_' lab]); + saveas (gcf, [years{j} '_clustergram_OR_' lab '.fig']); +end diff --git a/clustergram/OR_clustergram_ques_RC_2013_all_questions.m b/clustergram/OR_clustergram_ques_RC_2013_all_questions.m new file mode 100644 index 0000000..d5d2bb9 --- /dev/null +++ b/clustergram/OR_clustergram_ques_RC_2013_all_questions.m @@ -0,0 +1,138 @@ +%make heatmaps out of the relative risk matrix + +cd .. +cd .. +cd matrices +load OR_2013_110314.mat +load qlabel_090914.mat +cd .. +cd programs +cd clustergrams +cd results +xlab={'2013', '2011', '2009', '2007', '2005', '2003', '2001'}; + +ques=input ('Enter in the question number you want to use (ex. Q01): ', 's'); +for i=1:82 + i_char=num2str(i); + q1_=i_char; + num2=i; + if length(i_char)<2 + i_char=['0' i_char]; + end + q1=['Q' i_char]; + if strcmp(q1,ques)==1 + indx=find (strcmp(odds_ratio_cell(:,1),q1)==1); + if isempty(indx)==0 + lab=odds_ratio_cell(indx,2); + P=odds_ratio_cell(indx,3:9); + P2=cell.empty; + qlabel2=cell.empty; + [rl,cl]=size(qlabel); + counter=1; + for j=1:rl + indx=find(strcmp(qlabel{j,2},lab)==1 & strcmp(lab, ques)==0); + if numel(indx)>0 + P2(counter,:)=P(indx,:); + qlabel2(counter,:)=qlabel(j,:); + counter=counter+1; + end + + end + %replace NaN with -10000 + indx=find(strcmp(P2,'NaN')==1); + for j=1:numel(indx) + P2{indx(j)}=-10000; + end + indx=find(strcmp(P2,'Inf')==1); + for j=1:numel(indx) + P2{indx(j)}=10000; + end + emptycells=cellfun(@isempty, P2); + [r,c]=size(emptycells); + for j=1:r + for k=1:c + if (emptycells(j,k)==1) + P2{j,k}=-10000; + end + end + end + plot_mat=cell2mat(P2); + [r,c]=size(plot_mat); + + q2=odds_ratio_cell(indx,2); + q2_=cell(length(q2)-1,1); + for p=1:length(q2)-1 + s=q2{p,1}; + q2_{p,1}=s(2:3); + end + plot_mat=rot90(plot_mat); + [r,c]=size(plot_mat); + plot_mat(plot_mat==-10000)=NaN; + plot_mat3=plot_mat; + xlab_new=xlab; + qlab_new=qlabel2(:,1); +% %create second matrix without NaN +% plot_mat2=double.empty; +% xlab_new=cell.empty; +% counter=1; +% for j=1:r +% indx=find(isnan(plot_mat(j,:))==0) ; +% if isempty(indx)==0 %entire row is NOT nan +% plot_mat2(counter,:)=plot_mat(j,:); +% xlab_new{counter}=xlab{j}; +% counter=counter+1; +% end +% end +% %remove questions that don't have all of the same years +% [r,c]=size(plot_mat2); +% plot_mat3=double.empty; +% qlab_new=cell.empty; +% counter=1; +% for j=1:c +% indx=find(isnan(plot_mat2(:,j))==1); +% if numel(indx)==0 +% plot_mat3(:,counter)=plot_mat2(:,j); +% qlab_new{counter}=qlabel2{j,:}; +% counter=counter+1; +% end +% end + + %create 3rd matrix without the maximum values + [r,c]=size(plot_mat3); + plot_mat3(isinf(plot_mat3)==1)=-10000 ; + plot_mat3(plot_mat3==10000)=-10000; + for j=1:r + maxv=nanmax(plot_mat3(j,:)); + indx_inf=find(plot_mat3(j,:)==-10000); + if numel(indx_inf)>0 + plot_mat3(j,indx_inf)=maxv; + end + end + %median center + for j=1:r + med=nanmedian(plot_mat3(j,:)); + plot_mat3(j,:)=plot_mat3(j,:)/med; + end + log_rel_risk=log2(plot_mat3); + for j=1:r + temp=log_rel_risk(j,:); + minv=min(temp); + indx3=find(log_rel_risk(j,:)<-100000); + log_rel_risk(j,indx3)=minv; + end + for j=1:r + for k=1:c + if isnan(log_rel_risk(j,k))==1 + log_rel_risk(j,k)=nanmean(log_rel_risk(:,k)); + end + end + end + cg=clustergram(log_rel_risk,'RowLabels', xlab_new, 'ColumnLabels',qlab_new,'Cluster',2, 'Colormap','jet', 'DisplayRange',3, 'Symmetric','true'); + fig=plot(cg); + %tightfig; + print (gcf,'-dpng',[q1 '_clustermap_OR_RC_2013.png']); + saveas(gcf,[q1 '_clustermap_OR_RC_2013.fig']); + end + %close + end +end \ No newline at end of file diff --git a/clustergram/OR_clustergram_ques_RC_2013_mental_health.m b/clustergram/OR_clustergram_ques_RC_2013_mental_health.m new file mode 100644 index 0000000..96372ff --- /dev/null +++ b/clustergram/OR_clustergram_ques_RC_2013_mental_health.m @@ -0,0 +1,206 @@ +%make heatmaps out of the relative risk matrix + +cd .. +cd .. +cd matrices +load OR_2013_HISPANIC_GIRLS_MentalHealth +load qlabel_090914.mat +cd .. +cd programs +cd clustergrams +cd results +xlab={'2013', '2011', '2009', '2007', '2005', '2003'}; +ques={'Q11'; 'Q12'; 'Q13'; 'Q14'; 'Q15'; 'Q16'}; + + +lab=odds_ratio_cell(:,2); +P=odds_ratio_cell(:,4:9); +P2=cell.empty; +qlabel2=cell.empty; +[rl,cl]=size(qlabel); +counter=1; +for j=1:rl + Q=qlabel{j,2}; + indx1=find(strcmp(Q,lab)==1); + indx2=find(strcmp(Q,ques)==1); + if (numel(indx1)>0 && numel(indx2)==0) + P2(counter,:)=P(indx1,:); + qlabel2(counter,:)=qlabel(j,:); + counter=counter+1; + end + +end +%replace NaN with -10000 +indx=find(strcmp(P2,'NaN')==1); +for j=1:numel(indx) + P2{indx(j)}=-10000; +end +indx=find(strcmp(P2,'Inf')==1); +for j=1:numel(indx) + P2{indx(j)}=10000; +end +emptycells=cellfun(@isempty, P2); +[r,c]=size(emptycells); +for j=1:r + for k=1:c + if (emptycells(j,k)==1) + P2{j,k}=-10000; + end + end +end +plot_mat=cell2mat(P2); +[r,c]=size(plot_mat); + +q2=odds_ratio_cell(indx,2); +q2_=cell(length(q2)-1,1); +for p=1:length(q2)-1 + s=q2{p,1}; + q2_{p,1}=s(2:3); +end +plot_mat=rot90(plot_mat); +[r,c]=size(plot_mat); +plot_mat(plot_mat==-10000)=NaN; +plot_mat3=plot_mat; +xlab_new=xlab; +qlab_new=qlabel2(:,1); + +%create 3rd matrix without the maximum values +[r,c]=size(plot_mat3); +plot_mat3(isinf(plot_mat3)==1)=-10000 ; +plot_mat3(plot_mat3==10000)=-10000; +for j=1:r + maxv=nanmax(plot_mat3(j,:)); + indx_inf=find(plot_mat3(j,:)==-10000); + if numel(indx_inf)>0 + plot_mat3(j,indx_inf)=maxv; + end +end +%median center +for j=1:r + med=nanmedian(plot_mat3(j,:)); + plot_mat3(j,:)=plot_mat3(j,:)/med; +end +log_rel_risk=log2(plot_mat3); +for j=1:r + temp=log_rel_risk(j,:); + minv=min(temp); + indx3=find(log_rel_risk(j,:)<-100000); + log_rel_risk(j,indx3)=minv; +end +for j=1:r + for k=1:c + if isnan(log_rel_risk(j,k))==1 + log_rel_risk(j,k)=nanmean(log_rel_risk(:,k)); + end + end +end +cg=clustergram(log_rel_risk,'RowLabels', xlab_new, 'ColumnLabels',qlab_new,'Cluster',2, 'Colormap','jet', 'DisplayRange',3, 'Symmetric','true'); +fig=plot(cg); +%tightfig; +print (gcf,'-dpng','clustermap_OR_mentalhealth_HG.png'); +saveas(gcf,'clustermap_OR_mentalhealth_HG.fig'); +close all + +%%FOR ALL---------------------------------------------------------------- +cd .. +cd .. +cd .. +cd matrices +load OR_2013_ALL_MentalHealth +load qlabel_090914.mat +cd .. +cd programs +cd clustergrams +cd results +xlab={'2013', '2011', '2009', '2007', '2005', '2003'}; +ques={'Q11'; 'Q12'; 'Q13'; 'Q14'; 'Q15'; 'Q16'}; + + +lab=odds_ratio_cell(:,2); +P=odds_ratio_cell(:,4:9); +P2=cell.empty; +qlabel2=cell.empty; +[rl,cl]=size(qlabel); +counter=1; +for j=1:rl + Q=qlabel{j,2}; + indx1=find(strcmp(Q,lab)==1); + indx2=find(strcmp(Q,ques)==1); + if (numel(indx1)>0 && numel(indx2)==0) + P2(counter,:)=P(indx1,:); + qlabel2(counter,:)=qlabel(j,:); + counter=counter+1; + end + +end +%replace NaN with -10000 +indx=find(strcmp(P2,'NaN')==1); +for j=1:numel(indx) + P2{indx(j)}=-10000; +end +indx=find(strcmp(P2,'Inf')==1); +for j=1:numel(indx) + P2{indx(j)}=10000; +end +emptycells=cellfun(@isempty, P2); +[r,c]=size(emptycells); +for j=1:r + for k=1:c + if (emptycells(j,k)==1) + P2{j,k}=-10000; + end + end +end +plot_mat=cell2mat(P2); +[r,c]=size(plot_mat); + +q2=odds_ratio_cell(indx,2); +q2_=cell(length(q2)-1,1); +for p=1:length(q2)-1 + s=q2{p,1}; + q2_{p,1}=s(2:3); +end +plot_mat=rot90(plot_mat); +[r,c]=size(plot_mat); +plot_mat(plot_mat==-10000)=NaN; +plot_mat3=plot_mat; +xlab_new=xlab; +qlab_new=qlabel2(:,1); + +%create 3rd matrix without the maximum values +[r,c]=size(plot_mat3); +plot_mat3(isinf(plot_mat3)==1)=-10000 ; +plot_mat3(plot_mat3==10000)=-10000; +for j=1:r + maxv=nanmax(plot_mat3(j,:)); + indx_inf=find(plot_mat3(j,:)==-10000); + if numel(indx_inf)>0 + plot_mat3(j,indx_inf)=maxv; + end +end +%median center +for j=1:r + med=nanmedian(plot_mat3(j,:)); + plot_mat3(j,:)=plot_mat3(j,:)/med; +end +log_rel_risk=log2(plot_mat3); +for j=1:r + temp=log_rel_risk(j,:); + minv=min(temp); + indx3=find(log_rel_risk(j,:)<-100000); + log_rel_risk(j,indx3)=minv; +end +for j=1:r + for k=1:c + if isnan(log_rel_risk(j,k))==1 + log_rel_risk(j,k)=nanmean(log_rel_risk(:,k)); + end + end +end +cg=clustergram(log_rel_risk,'RowLabels', xlab_new, 'ColumnLabels',qlab_new,'Cluster',2, 'Colormap','jet', 'DisplayRange',3, 'Symmetric','true'); +fig=plot(cg); +%tightfig; +print (gcf,'-dpng','clustermap_OR_mentalhealth_all.png'); +saveas(gcf,'clustermap_OR_mentalhealth_all.fig'); + + diff --git a/heatmaps/create_hm_graph_2003_2013.m b/heatmaps/create_hm_graph_2003_2013.m new file mode 100644 index 0000000..3bb1c29 --- /dev/null +++ b/heatmaps/create_hm_graph_2003_2013.m @@ -0,0 +1,130 @@ + function [per_mat_map] = create_hm_graph_2003_2013( question_mat, filename, race, sex, weight ) +% CREATE_HM_GRAPH takes in the question binary matrix (rows= years, +% columns=subjects) and outputs and outputs the summary matrix with the percentage of "yes" in each subgroup. +% also makes a heatmap and/or graph out of the output matrix matrix and saves them as PDFs (or whatever other file type you chose) +% Input variables: +% QUESTION_MAT: binary matrix for question (row=years, column=subjects) +% FILENAME: name of the question for file saving +% HEATMAP: 1 for yes, 0 for no +% GRAPH: 1 for yes, 0 for no + + +% TOTAL=importdata('TOTAL.txt', '\t'); +% ^ not necessary for this program because we're only interested in the students who answered and didn't leave out the Q + +[r,c]=size(question_mat); +label_=question_mat(2:r,1); +question_mat=question_mat(2:r,2:c); +sex=sex(2:r,2:c); +race=race(2:r,2:c); +weight=weight(2:r,2:c); +[r,c]=size(question_mat); + +per_mat = zeros(16,r); +if r==6 + for i=1:r %start at second location becuase that is year 2003 (don't use 2001) + % total(i)=TOTAL(i,1); + index_yes{i}=find(question_mat(i,:)==1); + index_girls{i}=find(sex(i,:)==1); + index_boys{i}=find(sex(i,:)==2); + index_W{i}=find(race(i,:)== 1 ); + index_B{i}=find(race(i,:)== 2 ); + index_H{i}=find(race(i,:)== 3 ); + index_O{i}=find(race(i,:)== 4 ); + index_missQ{i}=find(question_mat(i,:)==9); %students who didn't answer the Q + index_nomiss{i}=find(question_mat(i,:)==0 | question_mat(i,:)==1); %answers that were NOT missing (ie. 0's and 1's / no's and yes's) + missQ(i)=length(index_missQ{i}); %number of students who answered the question each year + index_total_b{i}=intersect(index_nomiss{i},index_boys{i}); %index of all boys who answered + index_total_g{i}=intersect(index_nomiss{i},index_girls{i}); %index of all girls who answered + w=weight(i,:)'; + total_ans(i)=nansum(w(index_nomiss{i})); + total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered + total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered + total_W{i}=nansum(w(intersect(index_nomiss{i}, index_W{i}))); %total # of white students who answered + total_B{i}=nansum(w(intersect(index_nomiss{i}, index_B{i}))); %total # of black students who answered + total_H{i}=nansum(w(intersect(index_nomiss{i}, index_H{i}))); %total # of hispanic students who answered + total_O{i}=nansum(w(intersect(index_nomiss{i}, index_O{i}))); %total # of "other" students who answered + total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i}))); + total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i}))); + total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i}))); + total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i}))); + total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i}))); + total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i}))); + total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i}))); + total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i}))); + + w=weight(i,:)'; + index_yesgirls{i}=intersect(index_yes{i},index_girls{i}); + index_yesboys{i}=intersect(index_yes{i},index_boys{i}); + yes_girls(i)=nansum(w(index_yesgirls{i})); + yes_boys(i)=nansum(w(index_yesboys{i})); + yes_W(i)=nansum(w(intersect(index_yes{i}, index_W{i}))); + yes_B(i)=nansum(w(intersect(index_yes{i}, index_B{i}))); + yes_H(i)=nansum(w(intersect(index_yes{i}, index_H{i}))); + yes_O(i)=nansum(w(intersect(index_yes{i}, index_O{i}))); + yes_WG(i)=nansum(w(intersect(index_yesgirls{i},index_W{i}))); + yes_BG(i)=nansum(w(intersect(index_yesgirls{i},index_B{i}))); + yes_HG(i)=nansum(w(intersect(index_yesgirls{i},index_H{i}))); + yes_OG(i)=nansum(w(intersect(index_yesgirls{i},index_O{i}))); + yes_WB(i)=nansum(w(intersect(index_yesboys{i},index_W{i}))); + yes_BB(i)=nansum(w(intersect(index_yesboys{i},index_B{i}))); + yes_HB(i)=nansum(w(intersect(index_yesboys{i},index_H{i}))); + yes_OB(i)=nansum(w(intersect(index_yesboys{i},index_O{i}))); + total_yes(i)=nansum(w(index_yes{i})); + total_w(i)=total_W{i}; + total_b(i)=total_B{i}; + total_h(i)=total_H{i}; + total_o(i)=total_O{i}; + per_mat(15, i)=total_yes(i)/total_ans(i)*100; %total + per_mat(14, i)=yes_boys(i)/total_boys(i)*100; %boys + per_mat(13, i)=yes_girls(i)/total_girls(i)*100; %girls + per_mat(12, i)=yes_W(i)/total_w(i)*100; %whites + per_mat(11, i)=yes_B(i)/total_b(i)*100; %blacks + per_mat(10, i)=yes_H(i)/total_h(i)*100; %hispanics + per_mat(9, i)=yes_O(i)/total_o(i)*100; %other + per_mat(8, i)=yes_WB(i)/total_Wb(i)*100; %WB + per_mat(7, i)=yes_WG(i)/total_Wg(i)*100; %WG + per_mat(6, i)=yes_BB(i)/total_Bb(i)*100; %BB + per_mat(5, i)=yes_BG(i)/total_Bg(i)*100; %BG + per_mat(4, i)=yes_HB(i)/total_Hb(i)*100; %HB + per_mat(3, i)=yes_HG(i)/total_Hg(i)*100; %HG + per_mat(2, i)=yes_OB(i)/total_Ob(i)*100; %OB + per_mat(1, i)=yes_OG(i)/total_Og(i)*100; %OG + end + + %Make heatmap + + + label_year=num2cell(label_); + label_cell2={'Total', 'Boys', 'Girls', 'W', 'B', 'H', 'O', 'W Boys', 'W Girls', 'B Boys', 'B Girls', 'H Boys', 'H Girls', 'O Boys', 'O Girls'}; + per_mat_map(1:15,1:r)=per_mat(1:15,1:r); + per_mat_map=flipdim(per_mat_map,1); + max_mat=max(max(per_mat_map)); + if max_mat>75 + M=100; + elseif max_mat>50 + M=75; + elseif max_mat>25 + M=50; + else + M=25; + end + %get rid of deimals + per_mat_map=per_mat_map*10; + per_mat_map=round(per_mat_map); + per_mat_map=per_mat_map/10; + h=figure; + [hImage]=heatmap_rb(per_mat_map, label_year, label_cell2, 1, M, 0, 'Colormap','money', 'UseLogColormap', false, 'ShowAllTicks',true, 'Colorbar',true,'TextColor','k', 'FontSize', 12); + %title (title1, 'FontSize', 12); + set (gca, 'FontSize',12); + cd results + saveas (gcf, [ filename '_heatmap_2003_2013.fig'] ); %can make pdf, jnp, or jpg + print (gcf, '-dpng', [ filename '_heatmap_2003_2013.png']); + cd .. + close all +else + per_mat_map=double.empty; + +end + + end %end of function diff --git a/heatmaps/demographics_2013_CI.m b/heatmaps/demographics_2013_CI.m index 5a0a61a..ab2e840 100644 --- a/heatmaps/demographics_2013_CI.m +++ b/heatmaps/demographics_2013_CI.m @@ -2,11 +2,6 @@ %resize and deal with the number of years in each [r,c]=size(question_mat); - label_=question_mat(:,1); - question_mat=question_mat(:,2:c); - sex=sex(:,2:c); - race=race(:,2:c); - weight=weight(:,2:c); [r,c]=size(question_mat); minimum=1; maximum=max(max(question_mat)); diff --git a/heatmaps/demographics_2013_CI_cutoff.m b/heatmaps/demographics_2013_CI_cutoff.m new file mode 100644 index 0000000..f092d9a --- /dev/null +++ b/heatmaps/demographics_2013_CI_cutoff.m @@ -0,0 +1,599 @@ + function [conf_mat, total, x_mat, n_mat] = demographics_2013_CI_cutoff( question_mat, race, sex, grade, weight, years, cutoff, direction ) + + + %resize and deal with the number of years in each + [r,c]=size(question_mat); + conf_mat=cell(76,r+1); %(demographics, years) + n_mat=zeros(75,r); %total number for conf_mat calculation + x_mat=zeros(75,r); %number in that categoriy for conf_mat + + %set up matrices + conf_mat{2,1}='total'; + conf_mat{3,1}='girls'; + conf_mat{4,1}='boys'; + conf_mat{5,1}='W'; + conf_mat{6,1}='B'; + conf_mat{7,1}='H'; + conf_mat{8,1}='O'; + conf_mat{9,1}='9'; + conf_mat{10,1}='10'; + conf_mat{11,1}='11'; + conf_mat{12,1}='12'; + + conf_mat{13,1}='Wg'; + conf_mat{14,1}='Wb'; + conf_mat{15,1}='Bg'; + conf_mat{16,1}='Bb'; + conf_mat{17,1}='Hg'; + conf_mat{18,1}='Hb'; + conf_mat{19,1}='Og'; + conf_mat{20,1}='Ob'; + + conf_mat{21,1}='9g'; + conf_mat{22,1}='9b'; + conf_mat{23,1}='10g'; + conf_mat{24,1}='10b'; + conf_mat{25,1}='11g'; + conf_mat{26,1}='11b'; + conf_mat{27,1}='12g'; + conf_mat{28,1}='12b'; + + conf_mat{29,1}='W9'; + conf_mat{30,1}='W10'; + conf_mat{31,1}='W11'; + conf_mat{32,1}='W12'; + conf_mat{33,1}='B9'; + conf_mat{34,1}='B10'; + conf_mat{35,1}='B11'; + conf_mat{36,1}='B12'; + conf_mat{37,1}='H9'; + conf_mat{38,1}='H10'; + conf_mat{39,1}='H11'; + conf_mat{40,1}='H12'; + conf_mat{41,1}='O9'; + conf_mat{42,1}='O10'; + conf_mat{43,1}='O11'; + conf_mat{44,1}='O12'; + + conf_mat{45,1}='W9g'; + conf_mat{46,1}='W10g'; + conf_mat{47,1}='W11g'; + conf_mat{48,1}='W12g'; + conf_mat{49,1}='W9b'; + conf_mat{50,1}='W10b'; + conf_mat{51,1}='W11b'; + conf_mat{52,1}='W12b'; + + conf_mat{53,1}='B9g'; + conf_mat{54,1}='B10g'; + conf_mat{55,1}='B11g'; + conf_mat{56,1}='B12g'; + conf_mat{57,1}='B9b'; + conf_mat{58,1}='B10b'; + conf_mat{59,1}='B11b'; + conf_mat{60,1}='B12b'; + + conf_mat{61,1}='H9g'; + conf_mat{62,1}='H10g'; + conf_mat{63,1}='H11g'; + conf_mat{64,1}='H12g'; + conf_mat{65,1}='H9b'; + conf_mat{66,1}='H10b'; + conf_mat{67,1}='H11b'; + conf_mat{68,1}='H12b'; + + conf_mat{69,1}='O9g'; + conf_mat{70,1}='O10g'; + conf_mat{71,1}='O11g'; + conf_mat{72,1}='O12g'; + conf_mat{73,1}='O9b'; + conf_mat{74,1}='O10b'; + conf_mat{75,1}='O11b'; + conf_mat{76,1}='O12b'; + + total=conf_mat; + + for i=1:r + conf_mat{1,i+1}=years(i); + index_girls{i}=find(sex(i,:)==1); + index_boys{i}=find(sex(i,:)==2); + index_W{i}=find(race(i,:)== 1 ); + index_B{i}=find(race(i,:)== 2 ); + index_H{i}=find(race(i,:)== 3 ); + index_O{i}=find(race(i,:)== 4 ); + index_9{i}=find(grade(i,:)== 1 ); + index_10{i}=find(grade(i,:)== 2 ); + index_11{i}=find(grade(i,:)== 3 ); + index_12{i}=find(grade(i,:)== 4 ); + + index_W9{i}=intersect(index_9{i},index_W{i}); + index_W10{i}=intersect(index_10{i},index_W{i}); + index_W11{i}=intersect(index_11{i},index_W{i}); + index_W12{i}=intersect(index_12{i},index_W{i}); + + index_B9{i}=intersect(index_9{i},index_B{i}); + index_B10{i}=intersect(index_10{i},index_B{i}); + index_B11{i}=intersect(index_11{i},index_B{i}); + index_B12{i}=intersect(index_12{i},index_B{i}); + + index_H9{i}=intersect(index_9{i},index_H{i}); + index_H10{i}=intersect(index_10{i},index_H{i}); + index_H11{i}=intersect(index_11{i},index_H{i}); + index_H12{i}=intersect(index_12{i},index_H{i}); + + index_O9{i}=intersect(index_9{i},index_O{i}); + index_O10{i}=intersect(index_10{i},index_O{i}); + index_O11{i}=intersect(index_11{i},index_O{i}); + index_O12{i}=intersect(index_12{i},index_O{i}); + + index_missQ{i}=find(question_mat(i,:)== 0); %students who didn't answer the Q + index_nomiss{i}=find(question_mat(i,:)>0); %answers that were NOT missing (ie. 0's and 1's / no's and yes's) + + index_total_b{i}=intersect(index_nomiss{i},index_boys{i}); %index of all boys who answered + index_total_g{i}=intersect(index_nomiss{i},index_girls{i}); %index of all girls who answered + + w=weight(i,:)'; + total_ans(i)=nansum(w(index_nomiss{i})); + total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered + total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered + total_W{i}=nansum(w(intersect(index_nomiss{i}, index_W{i}))); %total # of white students who answered + total_B{i}=nansum(w(intersect(index_nomiss{i}, index_B{i}))); %total # of black students who answered + total_H{i}=nansum(w(intersect(index_nomiss{i}, index_H{i}))); %total # of hispanic students who answered + total_O{i}=nansum(w(intersect(index_nomiss{i}, index_O{i}))); %total # of "other" students who answered + + total_w(i)=total_W{i}; + total_b(i)=total_B{i}; + total_h(i)=total_H{i}; + total_o(i)=total_O{i}; + + total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i}))); + total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i}))); + total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i}))); + total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i}))); + total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i}))); + total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i}))); + total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i}))); + total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i}))); + + total_9(i)=nansum(w(intersect((index_9{i}),index_nomiss{i}))); + total_10(i)=nansum(w(intersect((index_10{i}),index_nomiss{i}))); + total_11(i)=nansum(w(intersect((index_11{i}),index_nomiss{i}))); + total_12(i)=nansum(w(intersect((index_12{i}),index_nomiss{i}))); + + total_9G(i)=nansum(w(intersect(index_9{i},index_total_g{i}))); + total_10G(i)=nansum(w(intersect(index_10{i},index_total_g{i}))); + total_11G(i)=nansum(w(intersect(index_11{i},index_total_g{i}))); + total_12G(i)=nansum(w(intersect(index_12{i},index_total_g{i}))); + + total_9B(i)=nansum(w(intersect(index_9{i},index_total_b{i}))); + total_10B(i)=nansum(w(intersect(index_10{i},index_total_b{i}))); + total_11B(i)=nansum(w(intersect(index_11{i},index_total_b{i}))); + total_12B(i)=nansum(w(intersect(index_12{i},index_total_b{i}))); + + total_9G_W(i)=nansum(w(intersect(index_W9{i},index_total_g{i}))); + total_9B_W(i)=nansum(w(intersect(index_W9{i},index_total_b{i}))); + total_9G_B(i)=nansum(w(intersect(index_B9{i},index_total_g{i}))); + total_9B_B(i)=nansum(w(intersect(index_B9{i},index_total_b{i}))); + + total_9G_H(i)=nansum(w(intersect(index_H9{i},index_total_g{i}))); + total_9B_H(i)=nansum(w(intersect(index_H9{i},index_total_b{i}))); + total_9G_O(i)=nansum(w(intersect(index_O9{i},index_total_g{i}))); + total_9B_O(i)=nansum(w(intersect(index_O9{i},index_total_b{i}))); + + total_10G_W(i)=nansum(w(intersect(index_W10{i},index_total_g{i}))); + total_10B_W(i)=nansum(w(intersect(index_W10{i},index_total_b{i}))); + total_10G_B(i)=nansum(w(intersect(index_B10{i},index_total_g{i}))); + total_10B_B(i)=nansum(w(intersect(index_B10{i}, index_total_b{i}))); + + total_10G_H(i)=nansum(w(intersect(index_H10{i},index_total_g{i}))); + total_10B_H(i)=nansum(w(intersect(index_H10{i},index_total_b{i}))); + total_10G_O(i)=nansum(w(intersect(index_O10{i},index_total_g{i}))); + total_10B_O(i)=nansum(w(intersect(index_O10{i},index_total_b{i}))); + + total_11G_W(i)=nansum(w(intersect(index_W11{i},index_total_g{i}))); + total_11B_W(i)=nansum(w(intersect(index_W11{i},index_total_b{i}))); + total_11G_B(i)=nansum(w(intersect(index_B11{i},index_total_g{i}))); + total_11B_B(i)=nansum(w(intersect(index_B11{i},index_total_b{i}))); + + total_11G_H(i)=nansum(w(intersect(index_H11{i},index_total_g{i}))); + total_11B_H(i)=nansum(w(intersect(index_H11{i},index_total_b{i}))); + total_11G_O(i)=nansum(w(intersect(index_O11{i},index_total_g{i}))); + total_11B_O(i)=nansum(w(intersect(index_O11{i},index_total_b{i}))); + + total_12G_W(i)=nansum(w(intersect(index_W12{i},index_total_g{i}))); + total_12B_W(i)=nansum(w(intersect(index_W12{i},index_total_b{i}))); + total_12G_B(i)=nansum(w(intersect(index_B12{i},index_total_g{i}))); + total_12B_B(i)=nansum(w(intersect(index_B12{i},index_total_b{i}))); + + total_12G_H(i)=nansum(w(intersect(index_H12{i},index_total_g{i}))); + total_12B_H(i)=nansum(w(intersect(index_H12{i},index_total_b{i}))); + total_12G_O(i)=nansum(w(intersect(index_O12{i},index_total_g{i}))); + total_12B_O(i)=nansum(w(intersect(index_O12{i},index_total_b{i}))); + + total_9_W(i)=nansum(w(intersect(index_nomiss{i},index_W9{i}))); + total_9_B(i)=nansum(w(intersect(index_nomiss{i},index_B9{i}))); + total_9_H(i)=nansum(w(intersect(index_nomiss{i},index_H9{i}))); + total_9_O(i)=nansum(w(intersect(index_nomiss{i},index_O9{i}))); + + total_10_W(i)=nansum(w(intersect(index_nomiss{i},index_W10{i}))); + total_10_B(i)=nansum(w(intersect(index_nomiss{i},index_B10{i}))); + total_10_H(i)=nansum(w(intersect(index_nomiss{i},index_H10{i}))); + total_10_O(i)=nansum(w(intersect(index_nomiss{i},index_O10{i}))); + + total_11_W(i)=nansum(w(intersect(index_nomiss{i},index_W11{i}))); + total_11_B(i)=nansum(w(intersect(index_nomiss{i},index_B11{i}))); + total_11_H(i)=nansum(w(intersect(index_nomiss{i},index_H11{i}))); + total_11_O(i)=nansum(w(intersect(index_nomiss{i},index_O11{i}))); + + total_12_W(i)=nansum(w(intersect(index_nomiss{i},index_W12{i}))); + total_12_B(i)=nansum(w(intersect(index_nomiss{i},index_B12{i}))); + total_12_H(i)=nansum(w(intersect(index_nomiss{i},index_H12{i}))); + total_12_O(i)=nansum(w(intersect(index_nomiss{i},index_O12{i}))); + + %for stats fihnd total in each year + n_mat (1,i)= total_ans(i); + n_mat (2,i)=total_girls(i); + n_mat (3,i)=total_boys(i); + n_mat (4,i)=total_w(i); + n_mat (5,i)=total_b(i); + n_mat (6,i)=total_h(i); + n_mat (7,i)=total_o(i); + n_mat (8,i)=total_9(i); + n_mat (9,i)=total_10(i); + n_mat (10,i)=total_11(i); + n_mat (11,i)=total_12(i); + + n_mat (12,i)=total_Wg(i); + n_mat (13,i)=total_Wb(i); + n_mat (14,i)=total_Bg(i); + n_mat (15,i)=total_Bb(i); + n_mat (16,i)=total_Hg(i); + n_mat (17,i)=total_Hb(i); + n_mat (18,i)=total_Og(i); + n_mat (19,i)=total_Ob(i); + + n_mat (20,i)=total_9G(i); + n_mat (21,i)=total_9B(i); + n_mat (22,i)=total_10G(i); + n_mat (23,i)=total_10B(i); + n_mat (24,i)=total_11G(i); + n_mat (25,i)=total_11B(i); + n_mat (26,i)=total_12G(i); + n_mat (27,i)=total_12B(i); + + n_mat (28,i)=total_9_W(i); + n_mat (29,i)=total_10_W(i); + n_mat (30,i)=total_11_W(i); + n_mat (31,i)=total_12_W(i); + n_mat (32,i)=total_9_B(i); + n_mat (33,i)=total_10_B(i); + n_mat (34,i)=total_11_B(i); + n_mat (35,i)=total_12_B(i); + n_mat (36,i)=total_9_H(i); + n_mat (37,i)=total_10_H(i); + n_mat (38,i)=total_11_H(i); + n_mat (39,i)=total_12_H(i); + n_mat (40,i)=total_9_O(i); + n_mat (41,i)=total_10_O(i); + n_mat (42,i)=total_11_O(i); + n_mat (43,i)=total_12_O(i); + + n_mat (44,i)=total_9G_W(i); + n_mat (45,i)=total_10G_W(i); + n_mat (46,i)=total_11G_W(i); + n_mat (47,i)=total_12G_W(i); + n_mat (48,i)=total_9B_W(i); + n_mat (49,i)=total_10B_W(i); + n_mat (50,i)=total_11B_W(i); + n_mat (51,i)=total_12B_W(i); + + n_mat (52,i)=total_9G_B(i); + n_mat (53,i)=total_10G_B(i); + n_mat (54,i)=total_11G_B(i); + n_mat (55,i)=total_12G_B(i); + n_mat (56,i)=total_9B_B(i); + n_mat (57,i)=total_10B_B(i); + n_mat (58,i)=total_11B_B(i); + n_mat (59,i)=total_12B_B(i); + + n_mat (60,i)=total_9G_H(i); + n_mat (61,i)=total_10G_H(i); + n_mat (62,i)=total_11G_H(i); + n_mat (63,i)=total_12G_H(i); + n_mat (64,i)=total_9B_H(i); + n_mat (65,i)=total_10B_H(i); + n_mat (66,i)=total_11B_H(i); + n_mat (67,i)=total_12B_H(i); + + n_mat (68,i)=total_9G_O(i); + n_mat (69,i)=total_10G_O(i); + n_mat (70,i)=total_11G_O(i); + n_mat (71,i)=total_12G_O(i); + n_mat (72,i)=total_9B_O(i); + n_mat (73,i)=total_10B_O(i); + n_mat (74,i)=total_11B_O(i); + n_mat (75,i)=total_12B_O(i); + + %************************************** + w=weight(i,:)'; + count=1; + + if strcmp(direction,'G')==1 + index_yes{i}=find(question_mat(i,:)>cutoff); + else + index_yes{i}=find(question_mat(i,:)0); + end + + index_yesgirls{i}=intersect(index_yes{i},index_girls{i}); + index_yesboys{i}=intersect(index_yes{i},index_boys{i}); + yes_girls(i)=nansum(w(index_yesgirls{i})); + yes_boys(i)=nansum(w(index_yesboys{i})); + total_yes(i)=nansum(w(index_yes{i})); + yes_W(i)=nansum(w(intersect(index_yes{i}, index_W{i}))); + yes_B(i)=nansum(w(intersect(index_yes{i}, index_B{i}))); + yes_H(i)=nansum(w(intersect(index_yes{i}, index_H{i}))); + yes_O(i)=nansum(w(intersect(index_yes{i}, index_O{i}))); + yes_WG(i)=nansum(w(intersect(index_yesgirls{i},index_W{i}))); + yes_BG(i)=nansum(w(intersect(index_yesgirls{i},index_B{i}))); + yes_HG(i)=nansum(w(intersect(index_yesgirls{i},index_H{i}))); + yes_OG(i)=nansum(w(intersect(index_yesgirls{i},index_O{i}))); + yes_WB(i)=nansum(w(intersect(index_yesboys{i},index_W{i}))); + yes_BB(i)=nansum(w(intersect(index_yesboys{i},index_B{i}))); + yes_HB(i)=nansum(w(intersect(index_yesboys{i},index_H{i}))); + yes_OB(i)=nansum(w(intersect(index_yesboys{i},index_O{i}))); + yes_9(i)=nansum(w(intersect(index_yes{i},index_9{i}))); + yes_10(i)=nansum(w(intersect(index_yes{i},index_10{i}))); + yes_11(i)=nansum(w(intersect(index_yes{i},index_11{i}))); + yes_12(i)=nansum(w(intersect(index_yes{i},index_12{i}))); + yes_9b(i)=nansum(w(intersect(index_yesboys{i},index_9{i}))); + yes_10b(i)=nansum(w(intersect(index_yesboys{i},index_10{i}))); + yes_11b(i)=nansum(w(intersect(index_yesboys{i},index_11{i}))); + yes_12b(i)=nansum(w(intersect(index_yesboys{i},index_12{i}))); + yes_9g(i)=nansum(w(intersect(index_yesgirls{i},index_9{i}))); + yes_10g(i)=nansum(w(intersect(index_yesgirls{i},index_10{i}))); + yes_11g(i)=nansum(w(intersect(index_yesgirls{i},index_11{i}))); + yes_12g(i)=nansum(w(intersect(index_yesgirls{i},index_12{i}))); + + + yes_9WB(i)=nansum(w(intersect(index_yesboys{i},index_W9{i}))); + yes_10WB(i)=nansum(w(intersect(index_yesboys{i},index_W10{i}))); + yes_11WB(i)=nansum(w(intersect(index_yesboys{i},index_W11{i}))); + yes_12WB(i)=nansum(w(intersect(index_yesboys{i},index_W12{i}))); + yes_9WG(i)=nansum(w(intersect(index_yesgirls{i},index_W9{i}))); + yes_10WG(i)=nansum(w(intersect(index_yesgirls{i},index_W10{i}))); + yes_11WG(i)=nansum(w(intersect(index_yesgirls{i},index_W11{i}))); + yes_12WG(i)=nansum(w(intersect(index_yesgirls{i},index_W12{i}))); + + yes_9W(i)=nansum(w(intersect(index_yes{i},index_W9{i}))); + yes_10W(i)=nansum(w(intersect(index_yes{i},(index_W10{i})))); + yes_11W(i)=nansum(w(intersect(index_yes{i},(index_W11{i})))); + yes_12W(i)=nansum(w(intersect(index_yes{i},(index_W12{i})))); + + yes_9B(i)=nansum(w(intersect(index_yes{i},(index_B9{i})))); + yes_10B(i)=nansum(w(intersect(index_yes{i},(index_B10{i})))); + yes_11B(i)=nansum(w(intersect(index_yes{i},(index_B11{i})))); + yes_12B(i)=nansum(w(intersect(index_yes{i},(index_B12{i})))); + + yes_9H(i)=nansum(w(intersect(index_yes{i},(index_H9{i})))); + yes_10H(i)=nansum(w(intersect(index_yes{i},(index_H10{i})))); + yes_11H(i)=nansum(w(intersect(index_yes{i},(index_H11{i})))); + yes_12H(i)=nansum(w(intersect(index_yes{i},(index_H12{i})))); + + yes_9O(i)=nansum(w(intersect(index_yes{i},(index_O9{i})))); + yes_10O(i)=nansum(w(intersect(index_yes{i},(index_O10{i})))); + yes_11O(i)=nansum(w(intersect(index_yes{i},(index_O11{i})))); + yes_12O(i)=nansum(w(intersect(index_yes{i},(index_O12{i})))); + + yes_9BB(i)=nansum(w(intersect(index_yesboys{i},index_B9{i}))); + yes_10BB(i)=nansum(w(intersect(index_yesboys{i},index_B10{i}))); + yes_11BB(i)=nansum(w(intersect(index_yesboys{i},index_B11{i}))); + yes_12BB(i)=nansum(w(intersect(index_yesboys{i},index_B12{i}))); + yes_9BG(i)=nansum(w(intersect(index_yesgirls{i},index_B9{i}))); + yes_10BG(i)=nansum(w(intersect(index_yesgirls{i},index_B10{i}))); + yes_11BG(i)=nansum(w(intersect(index_yesgirls{i},index_B11{i}))); + yes_12BG(i)=nansum(w(intersect(index_yesgirls{i},index_B12{i}))); + + yes_9HB(i)=nansum(w(intersect(index_yesboys{i},index_H9{i}))); + yes_10HB(i)=nansum(w(intersect(index_yesboys{i},index_H10{i}))); + yes_11HB(i)=nansum(w(intersect(index_yesboys{i},index_H11{i}))); + yes_12HB(i)=nansum(w(intersect(index_yesboys{i},index_H12{i}))); + yes_9HG(i)=nansum(w(intersect(index_yesgirls{i},index_H9{i}))); + yes_10HG(i)=nansum(w(intersect(index_yesgirls{i},index_H10{i}))); + yes_11HG(i)=nansum(w(intersect(index_yesgirls{i},index_H11{i}))); + yes_12HG(i)=nansum(w(intersect(index_yesgirls{i},index_H12{i}))); + + yes_9OB(i)=nansum(w(intersect(index_yesboys{i},index_O9{i}))); + yes_10OB(i)=nansum(w(intersect(index_yesboys{i},index_O10{i}))); + yes_11OB(i)=nansum(w(intersect(index_yesboys{i},index_O11{i}))); + yes_12OB(i)=nansum(w(intersect(index_yesboys{i},index_O12{i}))); + yes_9OG(i)=nansum(w(intersect(index_yesgirls{i},index_O9{i}))); + yes_10OG(i)=nansum(w(intersect(index_yesgirls{i},index_O10{i}))); + yes_11OG(i)=nansum(w(intersect(index_yesgirls{i},index_O11{i}))); + yes_12OG(i)=nansum(w(intersect(index_yesgirls{i},index_O12{i}))); + + %find prevelance values******************************* + total{2,i}=total_yes(i)/total_ans(i)*100; %total + total{3,i}=yes_girls(i)/total_girls(i)*100; %girls + total{4,i}=yes_boys(i)/total_boys(i)*100; %boys + total{5,i}=yes_W(i)/total_w(i)*100; %whites + total{6,i}=yes_B(i)/total_b(i)*100; %blacks + total{7,i}=yes_H(i)/total_h(i)*100; %hispanics + total{8,i}=yes_O(i)/total_o(i)*100; %other + total{9,i}=yes_9(i)/total_9(i)*100; + total{10,i}=yes_10(i)/total_10(i)*100; + total{11,i}=yes_11(i)/total_11(i)*100; + total{12,i}=yes_12(i)/total_12(i)*100; + + total{13,i}=yes_WG(i)/total_Wg(i)*100; %WG + total{14,i}=yes_WB(i)/total_Wb(i)*100; %WB + total{15,i}=yes_BG(i)/total_Bg(i)*100; %BG + total{16,i}=yes_BB(i)/total_Bb(i)*100; %BB + total{17,i}=yes_HG(i)/total_Hg(i)*100; %HG + total{18,i}=yes_HB(i)/total_Hb(i)*100; %HB + total{19,i}=yes_OG(i)/total_Og(i)*100; %OG + total{20,i}=yes_OB(i)/total_Ob(i)*100; %OB + + total{21,i}=yes_9g(i)/total_9G(i)*100; + total{22,i}=yes_9b(i)/total_9B(i)*100; + total{23,i}=yes_10g(i)/total_10G(i)*100; + total{24,i}=yes_10b(i)/total_10B(i)*100; + total{25,i}=yes_11g(i)/total_11G(i)*100; + total{26,i}=yes_11b(i)/total_11B(i)*100; + total{27,i}=yes_12g(i)/total_12G(i)*100; + total{28,i}=yes_12b(i)/total_12B(i)*100; + + total{29,i}=yes_9W(i)/total_9_W(i)*100; + total{30,i}=yes_10W(i)/total_10_W(i)*100; + total{31,i}=yes_11W(i)/total_11_W(i)*100; + total{32,i}=yes_12W(i)/total_12_W(i)*100; + total{33,i}=yes_9B(i)/total_9_B(i)*100; + total{34,i}=yes_10B(i)/total_10_B(i)*100; + total{35,i}=yes_11B(i)/total_11_B(i)*100; + total{36,i}=yes_12B(i)/total_12_B(i)*100; + total{37,i}=yes_9H(i)/total_9_H(i)*100; + total{38,i}=yes_10H(i)/total_10_H(i)*100; + total{39,i}=yes_11H(i)/total_11_H(i)*100; + total{40,i}=yes_12H(i)/total_12_H(i)*100; + total{41,i}=yes_9O(i)/total_9_O(i)*100; + total{42,i}=yes_10O(i)/total_10_O(i)*100; + total{43,i}=yes_11O(i)/total_11_O(i)*100; + total{44,i}=yes_12O(i)/total_12_O(i)*100; + + total{45,i}=yes_9WG(i)/total_9G_W(i)*100; + total{46,i}=yes_10WG(i)/total_10G_W(i)*100; + total{47,i}=yes_11WG(i)/total_11G_W(i)*100; + total{48,i}=yes_12WG(i)/total_12G_W(i)*100; + total{49,i}=yes_9WB(i)/total_9B_W(i)*100; + total{50,i}=yes_10WB(i)/total_10B_W(i)*100; + total{51,i}=yes_11WB(i)/total_11B_W(i)*100; + total{52,i}=yes_12WB(i)/total_12B_W(i)*100; + + total{53,i}=yes_9BG(i)/total_9G_B(i)*100; + total{54,i}=yes_10BG(i)/total_10G_B(i)*100; + total{55,i}=yes_11BG(i)/total_11G_B(i)*100; + total{56,i}=yes_12BG(i)/total_12G_B(i)*100; + total{57,i}=yes_9BB(i)/total_9B_B(i)*100; + total{58,i}=yes_10BB(i)/total_10B_B(i)*100; + total{59,i}=yes_11BB(i)/total_11B_B(i)*100; + total{60,i}=yes_12BB(i)/total_12B_B(i)*100; + + total{61,i}=yes_9HG(i)/total_9G_H(i)*100; + total{62,i}=yes_10HG(i)/total_10G_H(i)*100; + total{63,i}=yes_11HG(i)/total_11G_H(i)*100; + total{64,i}=yes_12HG(i)/total_12G_H(i)*100; + total{65,i}=yes_9HB(i)/total_9B_H(i)*100; + total{66,i}=yes_10HB(i)/total_10B_H(i)*100; + total{67,i}=yes_11HB(i)/total_11B_H(i)*100; + total{68,i}=yes_12HB(i)/total_12B_H(i)*100; + + total{69,i}=yes_9OG(i)/total_9G_O(i)*100; + total{70,i}=yes_10OG(i)/total_10G_O(i)*100; + total{71,i}=yes_11OG(i)/total_11G_O(i)*100; + total{72,i}=yes_12OG(i)/total_12G_O(i)*100; + total{73,i}=yes_9OB(i)/total_9B_O(i)*100; + total{74,i}=yes_10OB(i)/total_10B_O(i)*100; + total{75,i}=yes_11OB(i)/total_11B_O(i)*100; + total{76,i}=yes_12OB(i)/total_12B_O(i)*100; + + %for confidence intervals + x_mat(1,i)=total_yes(i); + x_mat(2,i)=yes_girls(i); + x_mat(3,i)=yes_boys(i); + x_mat(4,i)=yes_W(i); + x_mat(5,i)=yes_B(i); + x_mat(6,i)=yes_H(i); + x_mat(7,i)=yes_O(i); + x_mat(8,i)=yes_9(i); + x_mat(9,i)=yes_10(i); + x_mat(10,i)=yes_11(i); + x_mat(11,i)=yes_12(i); + + x_mat(12,i)=yes_WG(i); + x_mat(13,i)=yes_WB(i); + x_mat(14,i)=yes_BG(i); + x_mat(15,i)=yes_BB(i); + x_mat(16,i)=yes_HG(i); + x_mat(17,i)=yes_HB(i); + x_mat(18,i)=yes_OG(i); + x_mat(19,i)=yes_OB(i); + + x_mat(20,i)=yes_9g(i); + x_mat(21,i)=yes_9b(i); + x_mat(22,i)=yes_10g(i); + x_mat(23,i)=yes_10b(i); + x_mat(24,i)=yes_11g(i); + x_mat(25,i)=yes_11b(i); + x_mat(26,i)=yes_12g(i); + x_mat(27,i)=yes_12b(i); + + x_mat(28,i)=yes_9W(i); + x_mat(29,i)=yes_10W(i); + x_mat(30,i)=yes_11W(i); + x_mat(31,i)=yes_12W(i); + x_mat(32,i)=yes_9B(i); + x_mat(33,i)=yes_10B(i); + x_mat(34,i)=yes_11B(i); + x_mat(35,i)=yes_12B(i); + x_mat(36,i)=yes_9H(i); + x_mat(37,i)=yes_10H(i); + x_mat(38,i)=yes_11H(i); + x_mat(39,i)=yes_12H(i); + x_mat(40,i)=yes_9O(i); + x_mat(41,i)=yes_10O(i); + x_mat(42,i)=yes_11O(i); + x_mat(43,i)=yes_12O(i); + + x_mat(44,i)=yes_9WG(i); + x_mat(45,i)=yes_10WG(i); + x_mat(46,i)=yes_11WG(i); + x_mat(47,i)=yes_12WG(i); + x_mat(48,i)=yes_9WB(i); + x_mat(49,i)=yes_10WB(i); + x_mat(50,i)=yes_11WB(i); + x_mat(51,i)=yes_12WB(i); + + x_mat(52,i)=yes_9BG(i); + x_mat(53,i)=yes_10BG(i); + x_mat(54,i)=yes_11BG(i); + x_mat(55,i)=yes_12BG(i); + x_mat(56,i)=yes_9BB(i); + x_mat(57,i)=yes_10BB(i); + x_mat(58,i)=yes_11BB(i); + x_mat(59,i)=yes_12BB(i); + + x_mat(60,i)=yes_9HG(i); + x_mat(61,i)=yes_10HG(i); + x_mat(62,i)=yes_11HG(i); + x_mat(63,i)=yes_12HG(i); + x_mat(64,i)=yes_9HB(i); + x_mat(65,i)=yes_10HB(i); + x_mat(66,i)=yes_11HB(i); + x_mat(67,i)=yes_12HB(i); + + x_mat(68,i)=yes_9OG(i); + x_mat(69,i)=yes_10OG(i); + x_mat(70,i)=yes_11OG(i); + x_mat(71,i)=yes_12OG(i); + x_mat(72,i)=yes_9OB(i); + x_mat(73,i)=yes_10OB(i); + x_mat(74,i)=yes_11OB(i); + x_mat(75,i)=yes_12OB(i); + end + + %confidence interval + z=1.96; + for i=1:75 + for j=1:r + n=n_mat(i,j); + x=x_mat(i,j); %x_mat=zeros(59,5,r); + p=x/n; %x is the number of subjects saying "yes", n is the total subjects + upper=((p+z*sqrt(p*(1-p)/n))*100); + lower=((p-z*sqrt(p*(1-p)/n))*100); + upper=sprintf('%0.1f',round(upper*10)/10); + lower=sprintf('%0.1f',round(lower*10)/10); + n_round=total{i+1, j}; + n_round=sprintf('%0.1f',round(n_round*10)/10); + conf_mat{i+1,j+1}=[n_round ' (' lower ', ' upper ')']; + end + end + end diff --git a/heatmaps/metalhealth_heatmap.m b/heatmaps/metalhealth_heatmap.m new file mode 100644 index 0000000..491d6b6 --- /dev/null +++ b/heatmaps/metalhealth_heatmap.m @@ -0,0 +1,158 @@ + +Q={'Q11', 'Q12', 'Q13', 'Q14', 'Q16'}; + +heatmap=1; +graph=0; + +files1=dir(fullfile('C:','Users','rugglk01','Dropbox (Personal)','CDC','data','results_053015','NaN', '*.txt')); +N=length(files1); +QUEST_MAT=double.empty; +count=1; +for j=1:N + cd .. + cd .. + cd data + cd Controls_061514 + if j==1 + sex=importdata('sex-NaN.txt', '\t'); + race=importdata('race-NaN.txt', '\t'); + weight=importdata('weights-NaN.txt','\t'); + end + cd .. + cd results_053015 + cd NaN + question_mat=importdata(files1(j).name, '\t'); + cd .. + cd .. + cd .. + cd programs + cd heatmaps + filename=''; + a=char(files1(j).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename=[filename c]; + end + + indx=find(strcmp(filename, Q)==1); + if numel(indx)>0 + [r,c]=size(question_mat); + label_=question_mat(2:r,1); + question_mat=question_mat(2:r,2:c); + QUEST_MAT(:,:,count)=question_mat; + count=count+1; + end +end + +[r,c]=size(sex); +sex=sex(2:r,2:c); +race=race(2:r,2:c); +weight=weight(2:r,2:c); + +[r,c,z]=size(QUEST_MAT); +per_mat = zeros(16,r); +for i=1:r %start at second location becuase that is year 2003 (don't use 2001) + + index_yes{i}=find( QUEST_MAT(i,:,1)==1 & QUEST_MAT(i,:,2)==1 & QUEST_MAT(i,:,3)==1 & QUEST_MAT(i,:,4)==1 & QUEST_MAT(i,:,5)==1 ); + index_girls{i}=find( sex(i, :) == 1 ); + index_boys{i}=find( sex(i, :) == 2 ); + index_W{i}=find( race(i,:) == 1 ); + index_B{i}=find( race(i,:) == 2 ); + index_H{i}=find( race(i,:) == 3 ); + index_O{i}=find( race(i,:) == 4 ); + index_missQ{i}=find( QUEST_MAT(i,:,1)==9 | QUEST_MAT(i,:,2)==9 | QUEST_MAT(i,:,3)==9 | QUEST_MAT(i,:,4)==9 | QUEST_MAT(i,:,5)==9 ); %students who didn't answer the Q + index_nomiss{i}=find( QUEST_MAT(i,:,1)<2 & QUEST_MAT(i,:,2)<2 & QUEST_MAT(i,:,3)<2 & QUEST_MAT(i,:,4)<2 & QUEST_MAT(i,:,5)<2 ) ; %answers that were NOT missing (ie. 0's and 1's / no's and yes's) + missQ(i)=length(index_missQ{i}); %number of students who answered the question each year + index_total_b{i}=intersect(index_nomiss{i},index_boys{i}); %index of all boys who answered + index_total_g{i}=intersect(index_nomiss{i},index_girls{i}); %index of all girls who answered + + w=weight(i,:)'; + total_ans(i)=nansum(w(index_nomiss{i})); + total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered + total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered + total_W{i}=nansum(w(intersect(index_nomiss{i}, index_W{i}))); %total # of white students who answered + total_B{i}=nansum(w(intersect(index_nomiss{i}, index_B{i}))); %total # of black students who answered + total_H{i}=nansum(w(intersect(index_nomiss{i}, index_H{i}))); %total # of hispanic students who answered + total_O{i}=nansum(w(intersect(index_nomiss{i}, index_O{i}))); %total # of "other" students who answered + total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i}))); + total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i}))); + total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i}))); + total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i}))); + total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i}))); + total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i}))); + total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i}))); + total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i}))); + + index_yesgirls{i}=intersect(index_yes{i},index_girls{i}); + index_yesboys{i}=intersect(index_yes{i},index_boys{i}); + yes_girls(i)=nansum(w(index_yesgirls{i})); + yes_boys(i)=nansum(w(index_yesboys{i})); + yes_W(i)=nansum(w(intersect(index_yes{i}, index_W{i}))); + yes_B(i)=nansum(w(intersect(index_yes{i}, index_B{i}))); + yes_H(i)=nansum(w(intersect(index_yes{i}, index_H{i}))); + yes_O(i)=nansum(w(intersect(index_yes{i}, index_O{i}))); + yes_WG(i)=nansum(w(intersect(index_yesgirls{i},index_W{i}))); + yes_BG(i)=nansum(w(intersect(index_yesgirls{i},index_B{i}))); + yes_HG(i)=nansum(w(intersect(index_yesgirls{i},index_H{i}))); + yes_OG(i)=nansum(w(intersect(index_yesgirls{i},index_O{i}))); + yes_WB(i)=nansum(w(intersect(index_yesboys{i},index_W{i}))); + yes_BB(i)=nansum(w(intersect(index_yesboys{i},index_B{i}))); + yes_HB(i)=nansum(w(intersect(index_yesboys{i},index_H{i}))); + yes_OB(i)=nansum(w(intersect(index_yesboys{i},index_O{i}))); + total_yes(i)=nansum(w(index_yes{i})); + total_w(i)=total_W{i}; + total_b(i)=total_B{i}; + total_h(i)=total_H{i}; + total_o(i)=total_O{i}; + + per_mat(15, i)=total_yes(i)/total_ans(i)*100; %total + per_mat(14, i)=yes_boys(i)/total_boys(i)*100; %boys + per_mat(13, i)=yes_girls(i)/total_girls(i)*100; %girls + per_mat(12, i)=yes_W(i)/total_w(i)*100; %whites + per_mat(11, i)=yes_B(i)/total_b(i)*100; %blacks + per_mat(10, i)=yes_H(i)/total_h(i)*100; %hispanics + per_mat(9, i)=yes_O(i)/total_o(i)*100; %other + per_mat(8, i)=yes_WB(i)/total_Wb(i)*100; %WB + per_mat(7, i)=yes_WG(i)/total_Wg(i)*100; %WG + per_mat(6, i)=yes_BB(i)/total_Bb(i)*100; %BB + per_mat(5, i)=yes_BG(i)/total_Bg(i)*100; %BG + per_mat(4, i)=yes_HB(i)/total_Hb(i)*100; %HB + per_mat(3, i)=yes_HG(i)/total_Hg(i)*100; %HG + per_mat(2, i)=yes_OB(i)/total_Ob(i)*100; %OB + per_mat(1, i)=yes_OG(i)/total_Og(i)*100; %OG + +end + + +%Make heatmap + +[r,c]=size(per_mat); +per_mat_map=zeros(15,6); +label_year=num2cell(label_); +label_cell2={'Total', 'Boys', 'Girls', 'W', 'B', 'H', 'O', 'W Boys', 'W Girls', 'B Boys', 'B Girls', 'H Boys', 'H Girls', 'O Boys', 'O Girls'}; +per_mat_map(1:15,1:c)=per_mat(1:15,1:c); +per_mat_map=flipdim(per_mat_map,1); +max_mat=max(max(per_mat_map)); +if max_mat>75 + M=100; +elseif max_mat>50 + M=75; +elseif max_mat>25 + M=50; +else + M=25; +end +%get rid of deimals +per_mat_map=per_mat_map*10; +per_mat_map=round(per_mat_map); +per_mat_map=per_mat_map/10; +h=figure; +[hImage]=heatmap_rb(per_mat_map, label_year, label_cell2, 1, M, 0, 'Colormap','money', 'UseLogColormap', false, 'ShowAllTicks',true, 'Colorbar',true,'TextColor','k', 'FontSize', 12); +%title (title1, 'FontSize', 12); +set (gca, 'FontSize',12); +cd results +saveas (gcf, 'mental_health_all.fig' ); %can make pdf, jnp, or jpg +print (gcf, '-dpng', 'mental_health_all.png'); +cd .. +close all diff --git a/heatmaps/run_all_hm_graph_2003_2013.m b/heatmaps/run_all_hm_graph_2003_2013.m new file mode 100644 index 0000000..af2f6ae --- /dev/null +++ b/heatmaps/run_all_hm_graph_2003_2013.m @@ -0,0 +1,37 @@ +heatmap=1; +graph=0; + +files1=dir(fullfile('C:','Users','rugglk01','Dropbox (Personal)','CDC','data','results_053015','NaN', '*.txt')); + +% reads all the text files in the folder 'binary_NaN_files' and saves them in an array called files +% make sure that folder contains only the NaN files for the questions you want to run create_hm_graph for +N=length(files1); + +for i=1:N + cd .. + cd .. + cd data + cd Controls_061514 + sex=importdata('sex-NaN.txt', '\t'); + race=importdata('race-NaN.txt', '\t'); + weight=importdata('weights-NaN.txt','\t'); + cd .. + cd results_053015 + cd NaN + question_mat=importdata(files1(i).name, '\t'); + %%% + cd .. + cd .. + cd .. + cd programs + cd heatmaps + filename=''; + a=char(files1(i).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename=[filename c]; + end + %%% ^ this piece of the program just makes the variable 'filename' out of everything before the '-' in 'Q#--NaN' + [ per_mat_map ] = create_hm_graph_2003_2013 ( question_mat, filename, race, sex, weight ); +end \ No newline at end of file diff --git a/heatmaps/run_all_hm_graph_2013.m b/heatmaps/run_all_hm_graph_2013.m index a07eb8e..f477b6c 100644 --- a/heatmaps/run_all_hm_graph_2013.m +++ b/heatmaps/run_all_hm_graph_2013.m @@ -1,13 +1,13 @@ heatmap=1; graph=0; -files1=dir(fullfile('C:','Users','kruggles7','Dropbox (Personal)','CDC','data','results_091614','NaN', '*.txt')); +files1=dir(fullfile('C:','Users','rugglk01','Dropbox (Personal)','CDC','data','results_103114','NaN', '*.txt')); % reads all the text files in the folder 'binary_NaN_files' and saves them in an array called files % make sure that folder contains only the NaN files for the questions you want to run create_hm_graph for N=length(files1); -for i=1:N +for i=15%1:N cd .. cd .. cd data @@ -16,7 +16,7 @@ race=importdata('race-NaN.txt', '\t'); weight=importdata('weights-NaN.txt','\t'); cd .. - cd results_091614 + cd results_103114 cd NaN question_mat=importdata(files1(i).name, '\t'); %%% diff --git a/heatmaps/run_demographics_2013_cutoff.m b/heatmaps/run_demographics_2013_cutoff.m new file mode 100644 index 0000000..4a1aaf7 --- /dev/null +++ b/heatmaps/run_demographics_2013_cutoff.m @@ -0,0 +1,48 @@ +cd .. +cd .. +cd data +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +weight=importdata('weights-NaN.txt','\t'); +grade=importdata('grade-NaN.txt','\t'); +cd .. +cd results_103114 +cd cat +ques=input ('Enter in the question number you want to use (ex. Q01): ', 's'); +cutoff=input ('Enter the cutoff answer choice (not including that choice): '); +direction=input ('Enter the direction (G for greater than, L for less than): ', 's'); + +question_mat=importdata([ques '-cat-NaN.txt'], '\t'); +[r,c]=size(question_mat); +labels=question_mat(:,1); +question_mat=question_mat(:,2:c); +years=[2001 2003 2005 2007 2009 2011 2013]; +start_year=0; +end_year=0; +years2=double.empty; +count=1; +for i=1:numel(years) + indx=find(labels==years(i)); + if numel(indx)==1 + if start_year==0 + start_year=i; + else + end_year=i; + end + years2(count)=years(i); + count=count+1; + end +end + +[r,c]=size(sex); +weight=weight(start_year:end_year,2:c); +race=race(start_year:end_year,2:c); +sex=sex(start_year:end_year,2:c); +grade=grade(start_year:end_year,2:c); +cd .. +cd .. +cd .. +cd programs +cd heatmaps +[conf_mat, total, x_mat, n_mat] = demographics_2013_CI_cutoff( question_mat, race, sex, grade, weight, years2, cutoff, direction ); \ No newline at end of file diff --git a/odds_ratio/OR_2013_categorical.m b/odds_ratio/OR_2013_categorical.m index e3a43c9..b0bfa0a 100644 --- a/odds_ratio/OR_2013_categorical.m +++ b/odds_ratio/OR_2013_categorical.m @@ -78,7 +78,7 @@ for i=1:r %each year %Q1: index_yes_1{i}=find(quest_1F(i,:)>=CAT); %students who answered yes to Q1 - index_no_1{i}=find(quest_1F0); %students who answered no to Q1 index_miss_1{i}=find(quest_1F(i,:)==0); %students who didn't answer Q1 %Q2: diff --git a/odds_ratio/OR_2013_categoricalv2.m b/odds_ratio/OR_2013_categoricalv2.m new file mode 100644 index 0000000..d3e133f --- /dev/null +++ b/odds_ratio/OR_2013_categoricalv2.m @@ -0,0 +1,158 @@ + + function [] = OR_2013_categoricalv2(ques1) + + k=1; %counter for rows in rel_risk_cell and odds_ratio_cell + load reverse_code_091914 + cd .. + cd results_103114 + cd cat + file1=[ques1 '-cat-NaN.txt']; + quest_1=importdata(file1, '\t'); + filename1=ques1; + a1=char(ques1); + ct=''; + for q=2:numel(a1) + c1=a1(q) ; + ct=[ct c1]; + end + ct=str2num(ct); + q1_RC=reverse_code(ct,1) ; + [r,c]=size(quest_1); + max_q1=max(max(quest_1(:,2:c))); + + %filename2-- make a matrix with all of the other stories + files2=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_103114/NaN/', '*.txt')); + cd .. + cd NaN + N=length(files2); + quest_1F=nan(7,16411,N); + quest_2F=nan(7,16411,N); + year_final=double.empty; + for n=1:N %for each question 2 + quest_2=importdata(files2(n).name, '\t'); + filename2=''; + a2=char(files2(n).name); + b2=strfind(a2,'-'); + for m=1:(b2(1)-1) + c2=a2(m); + filename2=[filename2 c2]; + end + ct=[]; + for m=2:(b2(1)-1) + c2=a2(m); + ct=[ct c2]; + end + files2(n).name; + ct=str2num(ct); + q2_RC=reverse_code(ct,1) ; + if (isnan(q2_RC)==0 && isnan(q1_RC)==0) + [r1,c1]=size(quest_1); + [r2,c2]=size(quest_2) ; + year=[2001 2003 2005 2007 2009 2011 2013]; + year1=quest_1(:,1); + year2=quest_2(:,1); + counter=1; + s=0; %start for 1 + e=1; %start for 2 + for ii=1:numel(year) + y=year(ii); + indx1=find(year1==y); + indx2=find(year2==y); + if numel(indx1)>0 && numel(indx2)>0 %both in the matrix + quest_1F(counter,1:c1-1,n)=quest_1(indx1,2:c1); + quest_2F(counter,1:c2-1,n)=quest_2(indx2,2:c2); %for each question! + e=ii; + if (s==0) + s=ii; + end + end + counter=counter+1; + end + year_final(1,n)=s; + year_final(2,n)=e; + end + end + + for CAT=1%:max_q1 %for each categorical response for question 1 + [r,c,z]=size(quest_1F); + for n=78%1:z + K=year_final(1,n); %start year for each question + K_end=year_final(2,n); + if K>0 + for i=K:K_end %each year + %Q1: + index_yes_1{i}=find(quest_1F(i,:,n)>=CAT); %students who answered yes to Q1 + index_no_1{i}=find(quest_1F(i,:,n)0); %students who answered no to Q1 + index_miss_1{i}=find(quest_1F(i,:,n)==0); %students who didn't answer Q1 + + %Q2: + if q2_RC==1 + index_no_2{i}=find(quest_2F(i,:,n)==1); + index_yes_2{i}=find(quest_2F(i,:,n)==0); + index_miss_2{i}=find(quest_2F(i,:,n)==9); + else + index_yes_2{i}=find(quest_2F(i,:,n)==1); + index_no_2{i}=find(quest_2F(i,:,n)==0); + index_miss_2{i}=find(quest_2F(i,:,n)==9); + end + + + index_yes_both{i}=intersect(index_yes_1{i}, index_yes_2{i}); %students who said yes to both Qs + index_no_both{i}=intersect(index_no_1{i}, index_no_2{i}); %student who said no to both Qs + index_yes1_no2{i}=intersect(index_yes_1{i}, index_no_2{i}); %students who said yes to Q1 and no to Q2 + index_no1_yes2{i}=intersect(index_no_1{i}, index_yes_2{i}); %students who said no to Q1 and yes to Q2 + index_miss_both{i}=intersect(index_miss_1{i}, index_miss_2{i}); %students who left out both qs + + + total_yes_both(i)=length(index_yes_both{i}); %total who said yes to both questions + total_no_both(i)=length(index_no_both{i}); %total who said no to both questions + total_yes1_no2(i)=length(index_yes1_no2{i}); %total who said yes to Q1 and no to Q2 + total_no1_yes2(i)=length(index_no1_yes2{i}); %total who said no to Q1 and yes to Q2 + % Q1 + % + % | yes | no + % ______|_______|_________ + % Q2 yes | a | b + % ______|_______|_________ + % no | c | d + + a=total_yes_both(i); + d=total_no_both(i); + c=total_yes1_no2(i); + b=total_no1_yes2(i); + + %formula for odds ratio: + OR= (a*d)/(b*c); + + %find confidence interval for OR + lnOR=log(OR); + seOR=sqrt((1/a)+(1/b)+(1/c)+(1/d)); + CI_upper=lnOR+1.96*seOR; + CI_lower=lnOR-1.96*seOR; + CI_upper=exp(CI_upper); + CI_lower=exp(CI_lower); + %odds_ratio cell matrix: + odds_ratio_cell{k,1}=[filename1]; + odds_ratio_cell{k,2}=[filename2]; + x=num2cell(OR); + odds_ratio_cell(k,i+2)=x; + OR_CI{k,1}=filename1; + OR_CI{k,2}=filename2; + upper=sprintf('%0.2f',round(CI_upper*100)/100); + lower=sprintf('%0.2f',round(CI_lower*100)/100); + OR_CI{k,i+2}=[lower ', ' upper]; + end + k=k+1; + end + end + end + cd .. + cd .. + cd OR_results + CAT_char=num2str(CAT); + matsave=[ques1 '_response_' CAT_char '_OR_2013_cat.mat']; + matsave_CI=[ques1 '_response_' CAT_char '_OR_2013_cat_CI.mat']; + save(matsave, 'odds_ratio_cell'); + save(matsave_CI, 'OR_CI'); + cd .. +end diff --git a/odds_ratio/create_OR_2013_CI_highrisk_mentalhealth.m b/odds_ratio/create_OR_2013_CI_highrisk_mentalhealth.m new file mode 100644 index 0000000..7c466a6 --- /dev/null +++ b/odds_ratio/create_OR_2013_CI_highrisk_mentalhealth.m @@ -0,0 +1,189 @@ +%has reverse code information incorporated into this analysis + +% This program goes through all the binary_NaN files and gets the relative risk of every combination +% of variables and displays them in a chart as follows: + +% V1 | V2 | RR2001 | RR 2003 | RR2005 | RR2007 | RR2009 | RR2011 +%____|____|________|_________|________|________|________|________ +% | | | | | | | + +% It then does the same thing for odds ratio + +cd .. +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +cd .. +cd matlab +filename1='Q11-Q16'; + +Q={'Q11', 'Q12', 'Q13', 'Q14', 'Q16'}; +files1=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_053015/NaN/', '*.txt')); +load reverse_code_091914 + +N=length(files1); +QUEST_MAT=double.empty; +count=1; +k=1; +for j=1:N + cd results_053015 + cd NaN + question_mat=importdata(files1(j).name, '\t'); + cd .. + cd .. + filename=''; + a=char(files1(j).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename=[filename c]; + end + + indx=find(strcmp(filename, Q)==1); + if numel(indx)>0 + [r,c]=size(question_mat); + label_=question_mat(2:r,1); + QUEST_MAT(:,:,count)=question_mat; + count=count+1; + end +end + +for j=1:N + cd results_053015 + cd NaN + quest_2=importdata(files1(j).name, '\t'); + cd .. + cd .. + filename2=''; + a=char(files1(j).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename2=[filename2 c]; + end + ct=[]; + for q=2:(b(1)-1) + c1=a(q) ; + if c1~=0 + ct=[ct c1]; + end + end + ct=str2num(ct); + q2_RC=reverse_code(ct,1); + + %PROCESS THE COMBO + if (isnan(q2_RC)==0) + + [r1,c1, z]=size(QUEST_MAT); + [r2,c2]=size(quest_2) ; + year=[2001 2003 2005 2007 2009 2011 2013]; + quest_1=QUEST_MAT(:,:,1); + year1=quest_1(:,1); + year2=quest_2(:,1); + year_final=double.empty; + quest_1F=double.empty; + quest_2F=double.empty; + counter=1; + s=0; %start for 1 + e=1; %start for 2 + for ii=1:numel(year) + y=year(ii); + indx1=find(year1==y); + indx2=find(year2==y); + if numel(indx1)>0 && numel(indx2)>0 %both in the matrix + year_final(counter,1)=y; + quest_1F(counter,:)=quest_1(indx1,2:c1); + quest_2F(counter,:)=quest_2(indx2,2:c2); + e=ii; + if (counter==1) + s=ii; + end + counter=counter+1; + end + end + [r1,c1]=size(quest_1F); + K=s+2; + for i=1:r1 + %Q1: + index_yes{i}=find(QUEST_MAT(i,2:c1,1)==1 & QUEST_MAT(i,2:c1,2)==1 & QUEST_MAT(i,2:c1,3)==1 & QUEST_MAT(i,2:c1,4)==1 & QUEST_MAT(i,2:c1,5)==1 ); %students who answered yes to Q1 + index_no{i}=find(QUEST_MAT(i,2:c1,1)==0 & QUEST_MAT(i,2:c1,2)==0 & QUEST_MAT(i,2:c1,3)==0 & QUEST_MAT(i,2:c1,4)==0 & QUEST_MAT(i,2:c1,5)==0); %students who answered no to Q1 + index_yes_1{i}=index_yes{i}; + index_no_1{i}=index_no{i}; + index_miss{i}=find(QUEST_MAT(i,2:c1,1)==9 | QUEST_MAT(i,2:c1,2)==9 | QUEST_MAT(i,2:c1,3)==9 | QUEST_MAT(i,2:c1,4)==9 | QUEST_MAT(i,2:c1,5)==9 ); %students who didn't answer Q1 + index_miss_1{i}=index_miss{i}; + %Q2: + if q2_RC==1 + index_no{i}=find(quest_2F(i,:)==1); + index_yes{i}=find(quest_2F(i,:)==0); + index_yes_2{i}=index_yes{i}; + index_no_2{i}=index_no{i}; + index_miss{i}=find(quest_2F(i,:)==9); + index_miss_2{i}=index_miss{i}; + else + index_yes{i}=find(quest_2F(i,:)==1); + index_no{i}=find(quest_2F(i,:)==0); + index_yes_2{i}=index_yes{i}; + index_no_2{i}=index_no{i}; + index_miss{i}=find(quest_2F(i,:)==9); + index_miss_2{i}=index_miss{i}; + end + + index_yes_both{i}=intersect(index_yes_1{i}, index_yes_2{i}); %students who said yes to both Qs + index_no_both{i}=intersect(index_no_1{i}, index_no_2{i}); %student who said no to both Qs + index_yes1_no2{i}=intersect(index_yes_1{i}, index_no_2{i}); %students who said yes to Q1 and no to Q2 + index_no1_yes2{i}=intersect(index_no_1{i}, index_yes_2{i}); %students who said no to Q1 and yes to Q2 + index_miss_both{i}=intersect(index_miss_1{i}, index_miss_2{i}); %students who left out both qs + + + total_yes_both(i)=length(index_yes_both{i}); %total who said yes to both questions + total_no_both(i)=length(index_no_both{i}); %total who said no to both questions + total_yes1_no2(i)=length(index_yes1_no2{i}); %total who said yes to Q1 and no to Q2 + total_no1_yes2(i)=length(index_no1_yes2{i}); %total who said no to Q1 and yes to Q2 + % Q1 + % + % | yes | no + % ______|_______|_________ + % Q2 yes | a | b + % ______|_______|_________ + % no | c | d + + a=total_yes_both(i); + d=total_no_both(i); + c=total_yes1_no2(i); + b=total_no1_yes2(i); + + %formula for odds ratio: + OR= (a*d)/(b*c); + + %find confidence interval for OR + lnOR=log(OR); + seOR=sqrt((1/a)+(1/b)+(1/c)+(1/d)); + CI_upper=lnOR+1.96*seOR; + CI_lower=lnOR-1.96*seOR; + CI_upper=exp(CI_upper); + CI_lower=exp(CI_lower); + %odds_ratio cell matrix: + odds_ratio_cell{k,1}=[filename1]; + odds_ratio_cell{k,2}=[filename2]; + x=num2cell(OR); + odds_ratio_cell(k,K)=x; + OR_CI{k,1}=[filename1]; + OR_CI{k,2}=[filename2]; + upper=sprintf('%0.2f',round(CI_upper*100)/100); + lower=sprintf('%0.2f',round(CI_lower*100)/100); + OR_CI{k,K}=[lower ', ' upper]; + + K=K+1 ; + end + k=k+1; + end +end + +cd OR_results + +save('OR_2013_ALL_MentalHealth', 'odds_ratio_cell'); +save('OR_CI_2013_ALL_MentalHealth', 'OR_CI'); + +OR_CI=cell.empty; +odds_ratio_cell=cell.empty; +cd .. \ No newline at end of file diff --git a/odds_ratio/create_OR_2013_CI_highrisk_mentalhealth_hispgirls.m b/odds_ratio/create_OR_2013_CI_highrisk_mentalhealth_hispgirls.m new file mode 100644 index 0000000..8d09885 --- /dev/null +++ b/odds_ratio/create_OR_2013_CI_highrisk_mentalhealth_hispgirls.m @@ -0,0 +1,193 @@ +%has reverse code information incorporated into this analysis + +% This program goes through all the binary_NaN files and gets the relative risk of every combination +% of variables and displays them in a chart as follows: + +% V1 | V2 | RR2001 | RR 2003 | RR2005 | RR2007 | RR2009 | RR2011 +%____|____|________|_________|________|________|________|________ +% | | | | | | | + +% It then does the same thing for odds ratio + +%Hispanic Girls only +R=3; +G=1; +filename1='Q11-Q16'; +k=1; + +cd .. +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +cd .. +cd matlab + +Q={'Q11', 'Q12', 'Q13', 'Q14', 'Q16'}; +files1=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_053015/NaN/', '*.txt')); +%files1=dir(fullfile('C:/Users/rugglk01/Dropbox (Personal)/CDC/data/results_053015/NaN/', '*.txt')); +load reverse_code_091914 + +N=length(files1); +QUEST_MAT=double.empty; +count=1; +for j=1:N + cd results_053015 + cd NaN + question_mat=importdata(files1(j).name, '\t'); + cd .. + cd .. + filename=''; + a=char(files1(j).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename=[filename c]; + end + + indx=find(strcmp(filename, Q)==1); + if numel(indx)>0 + [r,c]=size(question_mat); + label_=question_mat(2:r,1); + QUEST_MAT(:,:,count)=question_mat; + count=count+1; + end +end + +for j=1:N + cd results_053015 + cd NaN + quest_2=importdata(files1(j).name, '\t'); + cd .. + cd .. + filename2=''; + a=char(files1(j).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename2=[filename2 c]; + end + ct=[]; + for q=2:(b(1)-1) + c1=a(q) ; + if c1~=0 + ct=[ct c1]; + end + end + ct=str2num(ct); + q2_RC=reverse_code(ct,1); + + %PROCESS THE COMBO + if (isnan(q2_RC)==0) + + [r1,c1, z]=size(QUEST_MAT); + [r2,c2]=size(quest_2) ; + year=[2001 2003 2005 2007 2009 2011 2013]; + quest_1=QUEST_MAT(:,:,1); + year1=quest_1(:,1); + year2=quest_2(:,1); + year_final=double.empty; + quest_1F=double.empty; + quest_2F=double.empty; + counter=1; + s=0; %start for 1 + e=1; %start for 2 + for ii=1:numel(year) + y=year(ii); + indx1=find(year1==y); + indx2=find(year2==y); + if numel(indx1)>0 && numel(indx2)>0 %both in the matrix + year_final(counter,1)=y; + quest_1F(counter,:)=quest_1(indx1,2:c1); + quest_2F(counter,:)=quest_2(indx2,2:c2); + e=ii; + if (counter==1) + s=ii; + end + counter=counter+1; + end + end + [r1,c1]=size(quest_1F); + K=s+2; + for i=1:r1 + %Q1: + index_final{i}=find(race(i,:)== R & sex(i,:)==G ); + index_yes{i}=find(QUEST_MAT(i,2:c1,1)==1 & QUEST_MAT(i,2:c1,2)==1 & QUEST_MAT(i,2:c1,3)==1 & QUEST_MAT(i,2:c1,4)==1 & QUEST_MAT(i,2:c1,5)==1 ); %students who answered yes to Q1 + index_no{i}=find(QUEST_MAT(i,2:c1,1)==0 & QUEST_MAT(i,2:c1,2)==0 & QUEST_MAT(i,2:c1,3)==0 & QUEST_MAT(i,2:c1,4)==0 & QUEST_MAT(i,2:c1,5)==0); %students who answered no to Q1 + index_yes_1{i}=intersect(index_yes{i},index_final{i}); + index_no_1{i}=intersect(index_no{i},index_final{i}); + index_miss{i}=find(QUEST_MAT(i,2:c1,1)==9 | QUEST_MAT(i,2:c1,2)==9 | QUEST_MAT(i,2:c1,3)==9 | QUEST_MAT(i,2:c1,4)==9 | QUEST_MAT(i,2:c1,5)==9 ); %students who didn't answer Q1 + index_miss_1{i}=intersect(index_miss{i}, index_final{i}); + %Q2: + if q2_RC==1 + index_no{i}=find(quest_2F(i,:)==1); + index_yes{i}=find(quest_2F(i,:)==0); + index_yes_2{i}=intersect(index_yes{i},index_final{i}); + index_no_2{i}=intersect(index_no{i},index_final{i}); + index_miss{i}=find(quest_2F(i,:)==9); + index_miss_2{i}=intersect(index_miss{i}, index_final{i}); + else + index_yes{i}=find(quest_2F(i,:)==1); + index_no{i}=find(quest_2F(i,:)==0); + index_yes_2{i}=intersect(index_yes{i},index_final{i}); + index_no_2{i}=intersect(index_no{i},index_final{i}); + index_miss{i}=find(quest_2F(i,:)==9); + index_miss_2{i}=intersect(index_miss{i}, index_final{i}); + end + + index_yes_both{i}=intersect(index_yes_1{i}, index_yes_2{i}); %students who said yes to both Qs + index_no_both{i}=intersect(index_no_1{i}, index_no_2{i}); %student who said no to both Qs + index_yes1_no2{i}=intersect(index_yes_1{i}, index_no_2{i}); %students who said yes to Q1 and no to Q2 + index_no1_yes2{i}=intersect(index_no_1{i}, index_yes_2{i}); %students who said no to Q1 and yes to Q2 + index_miss_both{i}=intersect(index_miss_1{i}, index_miss_2{i}); %students who left out both qs + + + total_yes_both(i)=length(index_yes_both{i}); %total who said yes to both questions + total_no_both(i)=length(index_no_both{i}); %total who said no to both questions + total_yes1_no2(i)=length(index_yes1_no2{i}); %total who said yes to Q1 and no to Q2 + total_no1_yes2(i)=length(index_no1_yes2{i}); %total who said no to Q1 and yes to Q2 + % Q1 + % + % | yes | no + % ______|_______|_________ + % Q2 yes | a | b + % ______|_______|_________ + % no | c | d + + a=total_yes_both(i); + d=total_no_both(i); + c=total_yes1_no2(i); + b=total_no1_yes2(i); + + %formula for odds ratio: + OR= (a*d)/(b*c); + + %find confidence interval for OR + lnOR=log(OR); + seOR=sqrt((1/a)+(1/b)+(1/c)+(1/d)); + CI_upper=lnOR+1.96*seOR; + CI_lower=lnOR-1.96*seOR; + CI_upper=exp(CI_upper); + CI_lower=exp(CI_lower); + %odds_ratio cell matrix: + odds_ratio_cell{k,1}=[filename1]; + odds_ratio_cell{k,2}=[filename2]; + x=num2cell(OR); + odds_ratio_cell(k,K)=x; + OR_CI{k,1}=[filename1]; + OR_CI{k,2}=[filename2]; + upper=sprintf('%0.2f',round(CI_upper*100)/100); + lower=sprintf('%0.2f',round(CI_lower*100)/100); + OR_CI{k,K}=[lower ', ' upper]; + + K=K+1 ; + end + k=k+1; + end +end + +cd OR_results +save('OR_2013_HISPANIC_GIRLS_MentalHealth', 'odds_ratio_cell'); +save('OR_CI_2013_HISPANIC_GIRLS_MentalHealth', 'OR_CI'); +OR_CI=cell.empty; +odds_ratio_cell=cell.empty; +cd .. \ No newline at end of file diff --git a/odds_ratio/create_OR_2013_any_tanning_white_boys_girls.m b/odds_ratio/create_OR_2013_any_tanning_white_boys_girls.m new file mode 100644 index 0000000..c952428 --- /dev/null +++ b/odds_ratio/create_OR_2013_any_tanning_white_boys_girls.m @@ -0,0 +1,194 @@ +%has reverse code information incorporated into this analysis + +% This program goes through all the binary_NaN files and gets the relative risk of every combination +% of variables and displays them in a chart as follows: + +% V1 | V2 | RR2001 | RR 2003 | RR2005 | RR2007 | RR2009 | RR2011 +%____|____|________|_________|________|________|________|________ +% | | | | | | | + +% It then does the same thing for odds ratio + +cd .. +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +cd .. +cd matlab + +k=1; %counter for rows in rel_risk_cell and odds_ratio_cell +CAT=2; %20 or more times +files1=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_053015/cat/', '*.txt')); +load reverse_code_091914 +R=1; %WHITE +P=length(files1); +for G=1:2%gender + for p=1:P %question + cd results_053015 + cd cat + files1(p).name; + quest_1=importdata(files1(p).name, '\t'); + filename1=''; + a1=char(files1(p).name); + b1=strfind(a1,'-'); + for q=1:(b1(1)-1) + c1=a1(q); + filename1=[filename1 c1]; + end + ct=[]; + for q=2:(b1(1)-1) + c1=a1(q) ; + if c1~=0 + ct=[ct c1]; + end + end + ct=str2num(ct); + q1_RC=reverse_code(ct,1); + cd .. + cd .. + if (ct == 78) %tanning + %filename2 + files2=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_053015/NaN/', '*.txt')); + N=length(files2); + cd results_053015 + cd NaN + for n=1:N + quest_2=importdata(files2(n).name, '\t'); + filename2=''; + a2=char(files2(n).name); + b2=strfind(a2,'-'); + for m=1:(b2(1)-1) + c2=a2(m); + filename2=[filename2 c2]; + end + ct=[]; + for m=2:(b2(1)-1) + c2=a2(m); + if c2~=0 + ct=[ct c2] ; + end + end + ct=str2num(ct); + q2_RC=reverse_code(ct,1); + + %PROCESS THE COMBO + if ( isnan(q2_RC)==0 && isnan(q1_RC)==0) + + [r1,c1]=size(quest_1); + [r2,c2]=size(quest_2) ; + year=[2001 2003 2005 2007 2009 2011 2013]; + year1=quest_1(:,1); + year2=quest_2(:,1); + year_final=double.empty; + quest_1F=double.empty; + quest_2F=double.empty; + counter=1; + s=0; %start for 1 + e=1; %start for 2 + for ii=1:numel(year) + y=year(ii); + indx1=find(year1==y); + indx2=find(year2==y); + if numel(indx1)>0 && numel(indx2)>0 %both in the matrix + year_final(counter,1)=y; + quest_1F(counter,:)=quest_1(indx1,2:c1); + quest_2F(counter,:)=quest_2(indx2,2:c2); + e=ii; + if (counter==1) + s=ii; + end + counter=counter+1; + end + end + [r,c]=size(quest_1F); + K=s+2; + for i=1:r + %Q1: + index_final{i}=find(race(i,:)== R & sex(i,:)==G ); + index_yes{i}=find(quest_1F(i,:)>=CAT); %students who answered yes to Q1 + index_yes_1{i}=intersect(index_yes{i},index_final{i}); + index_no{i}=find(quest_1F(i,:)0); %students who answered no to Q1 + index_no_1{i}=intersect(index_no{i},index_final{i}); + index_miss_1{i}=find(quest_1F(i,:)==0); %students who didn't answer Q1 + %Q2: + if q2_RC==1 + index_no{i}=find(quest_2F(i,:)==1); + index_yes{i}=find(quest_2F(i,:)==0); + index_yes_2{i}=intersect(index_yes{i},index_final{i}); + index_no_2{i}=intersect(index_no{i},index_final{i}); + index_miss_2{i}=find(quest_2F(i,:)==9); + else + index_yes{i}=find(quest_2F(i,:)==1); + index_no{i}=find(quest_2F(i,:)==0); + index_yes_2{i}=intersect(index_yes{i},index_final{i}); + index_no_2{i}=intersect(index_no{i},index_final{i}); + index_miss_2{i}=find(quest_2F(i,:)==9); + end + + index_yes_both{i}=intersect(index_yes_1{i}, index_yes_2{i}); %students who said yes to both Qs + index_no_both{i}=intersect(index_no_1{i}, index_no_2{i}); %student who said no to both Qs + index_yes1_no2{i}=intersect(index_yes_1{i}, index_no_2{i}); %students who said yes to Q1 and no to Q2 + index_no1_yes2{i}=intersect(index_no_1{i}, index_yes_2{i}); %students who said no to Q1 and yes to Q2 + index_miss_both{i}=intersect(index_miss_1{i}, index_miss_2{i}); %students who left out both qs + + + total_yes_both(i)=length(index_yes_both{i}); %total who said yes to both questions + total_no_both(i)=length(index_no_both{i}); %total who said no to both questions + total_yes1_no2(i)=length(index_yes1_no2{i}); %total who said yes to Q1 and no to Q2 + total_no1_yes2(i)=length(index_no1_yes2{i}); %total who said no to Q1 and yes to Q2 + % Q1 + % + % | yes | no + % ______|_______|_________ + % Q2 yes | a | b + % ______|_______|_________ + % no | c | d + + a=total_yes_both(i); + d=total_no_both(i); + c=total_yes1_no2(i); + b=total_no1_yes2(i); + + %formula for odds ratio: + OR= (a*d)/(b*c); + + %find confidence interval for OR + lnOR=log(OR); + seOR=sqrt((1/a)+(1/b)+(1/c)+(1/d)); + CI_upper=lnOR+1.96*seOR; + CI_lower=lnOR-1.96*seOR; + CI_upper=exp(CI_upper); + CI_lower=exp(CI_lower); + %odds_ratio cell matrix: + odds_ratio_cell{k,1}=[filename1]; + odds_ratio_cell{k,2}=[filename2]; + x=num2cell(OR); + odds_ratio_cell(k,K)=x; + OR_CI{k,1}=[filename1]; + OR_CI{k,2}=[filename2]; + upper=sprintf('%0.2f',round(CI_upper*100)/100); + lower=sprintf('%0.2f',round(CI_lower*100)/100); + OR_CI{k,K}=[lower ', ' upper]; + + K=K+1 ; + end + k=k+1; + end + end + cd .. + cd .. + end + end + + cd OR_results + if R==1 + if G==1 + save('OR_2013_WHITE_GIRLS_Q78', 'odds_ratio_cell'); + save('OR_CI_2013_WHITE_GIRLS_Q78', 'OR_CI'); + elseif G==2 + save('OR_2013_WHITE_BOYS_Q78', 'odds_ratio_cell'); + save('OR_CI_2013_WHITE_BOYS_Q78', 'OR_CI'); + end + end + cd .. +end \ No newline at end of file diff --git a/odds_ratio/create_OR_2013_heavy_tanning_white_boys_girls.m b/odds_ratio/create_OR_2013_heavy_tanning_white_boys_girls.m new file mode 100644 index 0000000..e265191 --- /dev/null +++ b/odds_ratio/create_OR_2013_heavy_tanning_white_boys_girls.m @@ -0,0 +1,194 @@ +%has reverse code information incorporated into this analysis + +% This program goes through all the binary_NaN files and gets the relative risk of every combination +% of variables and displays them in a chart as follows: + +% V1 | V2 | RR2001 | RR 2003 | RR2005 | RR2007 | RR2009 | RR2011 +%____|____|________|_________|________|________|________|________ +% | | | | | | | + +% It then does the same thing for odds ratio + +cd .. +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +cd .. +cd matlab + +k=1; %counter for rows in rel_risk_cell and odds_ratio_cell +CAT=5; %20 or more times +files1=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_053015/cat/', '*.txt')); +load reverse_code_091914 +R=1; %WHITE +P=length(files1); +for G=1:2%gender + for p=1:P %question + cd results_053015 + cd cat + files1(p).name; + quest_1=importdata(files1(p).name, '\t'); + filename1=''; + a1=char(files1(p).name); + b1=strfind(a1,'-'); + for q=1:(b1(1)-1) + c1=a1(q); + filename1=[filename1 c1]; + end + ct=[]; + for q=2:(b1(1)-1) + c1=a1(q) ; + if c1~=0 + ct=[ct c1]; + end + end + ct=str2num(ct); + q1_RC=reverse_code(ct,1); + cd .. + cd .. + if (ct == 78) %tanning + %filename2 + files2=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_053015/NaN/', '*.txt')); + N=length(files2); + cd results_053015 + cd NaN + for n=1:N + quest_2=importdata(files2(n).name, '\t'); + filename2=''; + a2=char(files2(n).name); + b2=strfind(a2,'-'); + for m=1:(b2(1)-1) + c2=a2(m); + filename2=[filename2 c2]; + end + ct=[]; + for m=2:(b2(1)-1) + c2=a2(m); + if c2~=0 + ct=[ct c2] ; + end + end + ct=str2num(ct); + q2_RC=reverse_code(ct,1); + + %PROCESS THE COMBO + if ( isnan(q2_RC)==0 && isnan(q1_RC)==0) + + [r1,c1]=size(quest_1); + [r2,c2]=size(quest_2) ; + year=[2001 2003 2005 2007 2009 2011 2013]; + year1=quest_1(:,1); + year2=quest_2(:,1); + year_final=double.empty; + quest_1F=double.empty; + quest_2F=double.empty; + counter=1; + s=0; %start for 1 + e=1; %start for 2 + for ii=1:numel(year) + y=year(ii); + indx1=find(year1==y); + indx2=find(year2==y); + if numel(indx1)>0 && numel(indx2)>0 %both in the matrix + year_final(counter,1)=y; + quest_1F(counter,:)=quest_1(indx1,2:c1); + quest_2F(counter,:)=quest_2(indx2,2:c2); + e=ii; + if (counter==1) + s=ii; + end + counter=counter+1; + end + end + [r,c]=size(quest_1F); + K=s+2; + for i=1:r + %Q1: + index_final{i}=find(race(i,:)== R & sex(i,:)==G ); + index_yes{i}=find(quest_1F(i,:)>=CAT); %students who answered yes to Q1 + index_yes_1{i}=intersect(index_yes{i},index_final{i}); + index_no{i}=find(quest_1F(i,:)0); %students who answered no to Q1 + index_no_1{i}=intersect(index_no{i},index_final{i}); + index_miss_1{i}=find(quest_1F(i,:)==0); %students who didn't answer Q1 + %Q2: + if q2_RC==1 + index_no{i}=find(quest_2F(i,:)==1); + index_yes{i}=find(quest_2F(i,:)==0); + index_yes_2{i}=intersect(index_yes{i},index_final{i}); + index_no_2{i}=intersect(index_no{i},index_final{i}); + index_miss_2{i}=find(quest_2F(i,:)==9); + else + index_yes{i}=find(quest_2F(i,:)==1); + index_no{i}=find(quest_2F(i,:)==0); + index_yes_2{i}=intersect(index_yes{i},index_final{i}); + index_no_2{i}=intersect(index_no{i},index_final{i}); + index_miss_2{i}=find(quest_2F(i,:)==9); + end + + index_yes_both{i}=intersect(index_yes_1{i}, index_yes_2{i}); %students who said yes to both Qs + index_no_both{i}=intersect(index_no_1{i}, index_no_2{i}); %student who said no to both Qs + index_yes1_no2{i}=intersect(index_yes_1{i}, index_no_2{i}); %students who said yes to Q1 and no to Q2 + index_no1_yes2{i}=intersect(index_no_1{i}, index_yes_2{i}); %students who said no to Q1 and yes to Q2 + index_miss_both{i}=intersect(index_miss_1{i}, index_miss_2{i}); %students who left out both qs + + + total_yes_both(i)=length(index_yes_both{i}); %total who said yes to both questions + total_no_both(i)=length(index_no_both{i}); %total who said no to both questions + total_yes1_no2(i)=length(index_yes1_no2{i}); %total who said yes to Q1 and no to Q2 + total_no1_yes2(i)=length(index_no1_yes2{i}); %total who said no to Q1 and yes to Q2 + % Q1 + % + % | yes | no + % ______|_______|_________ + % Q2 yes | a | b + % ______|_______|_________ + % no | c | d + + a=total_yes_both(i); + d=total_no_both(i); + c=total_yes1_no2(i); + b=total_no1_yes2(i); + + %formula for odds ratio: + OR= (a*d)/(b*c); + + %find confidence interval for OR + lnOR=log(OR); + seOR=sqrt((1/a)+(1/b)+(1/c)+(1/d)); + CI_upper=lnOR+1.96*seOR; + CI_lower=lnOR-1.96*seOR; + CI_upper=exp(CI_upper); + CI_lower=exp(CI_lower); + %odds_ratio cell matrix: + odds_ratio_cell{k,1}=[filename1]; + odds_ratio_cell{k,2}=[filename2]; + x=num2cell(OR); + odds_ratio_cell(k,K)=x; + OR_CI{k,1}=[filename1]; + OR_CI{k,2}=[filename2]; + upper=sprintf('%0.2f',round(CI_upper*100)/100); + lower=sprintf('%0.2f',round(CI_lower*100)/100); + OR_CI{k,K}=[lower ', ' upper]; + + K=K+1 ; + end + k=k+1; + end + end + cd .. + cd .. + end + end + + cd OR_results + if R==1 + if G==1 + save('OR_2013_WHITE_GIRLS_Q78_CAT5', 'odds_ratio_cell'); + save('OR_CI_2013_WHITE_GIRLS_Q78_CAT5', 'OR_CI'); + elseif G==2 + save('OR_2013_WHITE_BOYS_Q78_CAT5', 'odds_ratio_cell'); + save('OR_CI_2013_WHITE_BOYS_Q78_CAT5', 'OR_CI'); + end + end + cd .. +end \ No newline at end of file diff --git a/odds_ratio/create_RR_OR_2013_CI_race_sex.m b/odds_ratio/create_RR_OR_2013_CI_race_sex.m index 911b2f3..7d223e6 100644 --- a/odds_ratio/create_RR_OR_2013_CI_race_sex.m +++ b/odds_ratio/create_RR_OR_2013_CI_race_sex.m @@ -16,7 +16,6 @@ cd .. cd matlab -k=1; %counter for rows in rel_risk_cell and odds_ratio_cell files1=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_103114/NaN/', '*.txt')); load reverse_code_091914 @@ -24,6 +23,7 @@ P=length(files1); for R=1:3 %race for G=1:2 %gender + k=1; %counter for rows in rel_risk_cell and odds_ratio_cell cd results_103114 cd NaN for p=1:P %question @@ -101,20 +101,23 @@ K=s+2; for i=1:r %Q1: - index_final{i}=find(race(i,:)== R && sex(i,:)==G ); + index_final{i}=find(race(i,:)== R & sex(i,:)==G ); if q1_RC==1 index_no{i}=find(quest_1F(i,:)==1); %students who answered yes to Q1 index_yes{i}=find(quest_1F(i,:)==0); %students who answered no to Q1 index_yes_1{i}=intersect(index_yes{i},index_final{i}); index_no_1{i}=intersect(index_no{i},index_final{i}); - index_miss_1{i}=find(quest_1F(i,:)==9); %students who didn't answer Q1 + index_miss{i}=find(quest_1F(i,:)==9); %students who didn't answer Q1 + index_miss_1{i}=intersect(index_miss{i}, index_final{i}); else index_yes{i}=find(quest_1F(i,:)==1); %students who answered yes to Q1 index_no{i}=find(quest_1F(i,:)==0); %students who answered no to Q1 index_yes_1{i}=intersect(index_yes{i},index_final{i}); index_no_1{i}=intersect(index_no{i},index_final{i}); - index_miss_1{i}=find(quest_1F(i,:)==9); %students who didn't answer Q1 + index_miss{i}=find(quest_1F(i,:)==9); %students who didn't answer Q1 + index_miss_1{i}=intersect(index_miss{i}, index_final{i}); + end %Q2: if q2_RC==1 @@ -122,13 +125,15 @@ index_yes{i}=find(quest_2F(i,:)==0); index_yes_2{i}=intersect(index_yes{i},index_final{i}); index_no_2{i}=intersect(index_no{i},index_final{i}); - index_miss_2{i}=find(quest_2F(i,:)==9); + index_miss{i}=find(quest_2F(i,:)==9); + index_miss_2{i}=intersect(index_miss{i}, index_final{i}); else index_yes{i}=find(quest_2F(i,:)==1); index_no{i}=find(quest_2F(i,:)==0); index_yes_2{i}=intersect(index_yes{i},index_final{i}); index_no_2{i}=intersect(index_no{i},index_final{i}); - index_miss_2{i}=find(quest_2F(i,:)==9); + index_miss{i}=find(quest_2F(i,:)==9); + index_miss_2{i}=intersect(index_miss{i}, index_final{i}); end index_yes_both{i}=intersect(index_yes_1{i}, index_yes_2{i}); %students who said yes to both Qs @@ -185,6 +190,7 @@ cd .. cd .. + cd OR_results if R==1 if G==1 save('OR_2013_WHITE_GIRLS', 'odds_ratio_cell'); @@ -210,5 +216,8 @@ save('OR_CI_2013_HISPANIC_BOYS', 'OR_CI'); end end + OR_CI=cell.empty; + odds_ratio_cell=cell.empty; + cd .. end end \ No newline at end of file diff --git a/odds_ratio/process_cluster_OR_CI_WHITE_boys.m b/odds_ratio/process_cluster_OR_CI_WHITE_boys.m new file mode 100644 index 0000000..a3a06ab --- /dev/null +++ b/odds_ratio/process_cluster_OR_CI_WHITE_boys.m @@ -0,0 +1,41 @@ +cd .. +cd .. +cd matrices +load qlabel_090914 +cd .. +cd programs +cd odds_ratios +cd OR_2014_11_04_2decimals +load OR_2013_WHITE_BOYS +load OR_CI_2013_WHITE_BOYS +cd .. + +ques=input ('Enter in the question number you want to use (ex. Q01): ', 's'); + +indx=find (strcmp(odds_ratio_cell(:,1),ques)==1); +if isempty(indx)==0 + plot_mat=odds_ratio_cell(indx,3:9); + CI_mat=OR_CI(indx,3:9); + [r,c]=size(plot_mat); + table_OR=cell(r+1,c+1); + q2=odds_ratio_cell(indx,2); + for j=1:numel(q2) + indx2=find(strcmp(q2{j},qlabel(:,2))==1); + if numel(indx2)>0 + table_OR{j+1,1}=qlabel{indx2,1}; + end + end + x={'2001' '2003' '2005' '2007' '2009' '2011' '2013'}; + table_OR(1,2:c+1)=x; + for j=1:r + for k=1:c + %check for empty + i=cellfun(@isempty,plot_mat(j,k)); + if i==0 + n_round=plot_mat{j,k}; + n_round=sprintf('%0.2f',round(n_round*100)/100); + table_OR{j+1,k+1}=[n_round ' (' num2str(CI_mat{j,k}) ')']; + end + end + end +end diff --git a/physical_activity/video_games_cat_OR.m b/physical_activity/video_games_cat_OR.m new file mode 100644 index 0000000..aa209be --- /dev/null +++ b/physical_activity/video_games_cat_OR.m @@ -0,0 +1,427 @@ +cd .. +cd .. +cd matrices +load OR_2013_100714 +load OR_CI_2013_100714 +OR_2013=odds_ratio_cell; +OR_2013_CI=OR_CI; +cd OR_categorical +cd PhysicalActivity +load Q72_cat_OR +load Q72_response_2_OR_2013_cat +load Q72_response_2_OR_2013_cat_CI +OR2=odds_ratio_cell; +OR_CI2=OR_CI; +load Q72_response_3_OR_2013_cat +load Q72_response_3_OR_2013_cat_CI +OR3=odds_ratio_cell; +OR_CI3=OR_CI; +load Q72_response_4_OR_2013_cat +load Q72_response_4_OR_2013_cat_CI +OR4=odds_ratio_cell; +OR_CI4=OR_CI; +load Q72_response_5_OR_2013_cat +load Q72_response_5_OR_2013_cat_CI +OR5=odds_ratio_cell; +OR_CI5=OR_CI; +load Q72_response_6_OR_2013_cat +load Q72_response_6_OR_2013_cat_CI +OR6=odds_ratio_cell; +OR_CI6=OR_CI; +load Q72_response_7_OR_2013_cat +load Q72_response_7_OR_2013_cat_CI +OR7=odds_ratio_cell; +OR_CI7=OR_CI; +cd .. +cd .. +load qlabel_090914 +cd .. +cd programs +cd physical_activity + +indx=find(strcmp(OR_2013(:,1),'Q72')==1); +OR_2013_=OR_2013(indx,:); +OR_2013_CI_=OR_2013_CI(indx,:); + +[r,c]=size(OR2); + +final=zeros(r,7); +lower=zeros(r,7); +upper=zeros(r,7); +for i=1:r + + %check for empty + k=cellfun(@isempty,OR2(i,9)); %only 2013 data + if (k==0) + N=OR2{i,9}; + final(i,1)=N; + C=OR_CI2{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,1)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,1)=str2double(up_); + end + + k=cellfun(@isempty,OR3(i,9)); %only 2013 data + if (k==0) + N=OR3{i,9}; + final(i,2)=N; + C=OR_CI3{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,2)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,2)=str2double(up_); + end + + k=cellfun(@isempty,OR4(i,9)); %only 2013 data + if (k==0) + N=OR4{i,9}; + final(i,3)=N; + C=OR_CI4{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,3)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,3)=str2double(up_); + end + + k=cellfun(@isempty,OR5(i,9)); %only 2013 data + if (k==0) + N=OR5{i,9}; + final(i,4)=N; + C=OR_CI5{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,4)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,4)=str2double(up_); + end + + k=cellfun(@isempty,OR6(i,9)); %only 2013 data + if (k==0) + N=OR6{i,9}; + final(i,5)=N; + C=OR_CI6{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,5)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,5)=str2double(up_); + end + + k=cellfun(@isempty,OR7(i,9)); %only 2013 data + if (k==0) + N=OR7{i,9}; + final(i,6)=N; + C=OR_CI7{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,6)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,6)=str2double(up_); + end +end +% +% %significantly positive increases---------------------------- +% signif_pos=cell.empty; +% count=1; +% x=2:7; +% plot_mat=double.empty; +% for i=1:r +% max_=max(final(i,:)); +% min_=min(final(i,:)); +% diff=max_-min_; +% diff2=0; +% diff3=final(i,1)-final(i,6); %slope +% for j=1:6 +% for k=1:6 +% if lower(i,j)>upper(i,k) || upper(i,k)0) && diff2==1 && diff>0.7 && diff3<0 %positive slope +% signif_pos{count,1}=qlabel{i,1}; +% signif_pos{count,2}=diff; +% signif_pos{count,3}=final(i,1); +% signif_pos{count,4}=final(i,6); +% plot_mat(count,:,1)=final(i,:); +% E=(upper(i,:)-lower(i,:))/2; +% plot_mat(count,:,2)=E; +% plot_mat(count,:,3)=lower(i,:); +% plot_mat(count,:,4)=upper(i,:); +% count=count+1; +% %plot(x, final(i,:), '-o'); +% %hold on +% %errorbar(x, final(i,:),E); +% end +% end +% +% [r2,c2,z]=size(plot_mat); +% for i=1:r2 +% h2=subplot(1,r2,i); +% scatter(x, plot_mat(i,:,1), 'fill'); +% hold on +% % for j=1:6 +% % plot(x(j),plot_mat(i,j,3), x(j), plot_mat(i,j,4), 'LineStyle', '-', 'LineWidth', 0.5, 'Color', 'k'); +% % end +% errorbar(x,plot_mat(i,:,1),plot_mat(i,:,2), '.', 'Color', 'k'); +% hold off +% ylim([0 3]); +% set(gca, 'XTick', 2:7); +% set(gca, 'XTickLabel', {'0', '1', '2', '3', '4', '5'}); +% if (i>1) +% set(gca, 'YTick', []); +% linkaxes([h1 h2],'y'); +% pos1=get(h1,'Position'); +% pos2=get(h2,'Position'); +% pos2(4)=pos1(4); +% set(h2,'Position',pos2); +% pos1(2)=pos2(2); +% set(h1, 'Position', pos1); +% pos2(1)=pos1(1)+pos1(3); +% set(h2,'Position', pos2); +% else +% ylabel('Odds Ratio'); +% end +% h1=h2; +% +% end +% saveas (gcf, 'Q72_changingOR_positive.fig'); +% close all +% +% %significantly negative increases---------------------------- +% signif_neg=cell.empty; +% count=1; +% x=2:7; +% plot_mat=double.empty; +% for i=1:r +% max_=max(final(i,:)); +% min_=min(final(i,:)); +% diff=max_-min_; +% diff2=0; +% diff3=final(i,1)-final(i,6); %slope +% for j=1:6 +% for k=1:6 +% if lower(i,j)>upper(i,k) || upper(i,k)0) && diff2==1 && diff>0.5 && diff3>0 %positive slope +% signif_neg{count,1}=qlabel{i,1}; +% signif_neg{count,2}=diff; +% signif_neg{count,3}=final(i,1); +% signif_neg{count,4}=final(i,6); +% plot_mat(count,:,1)=final(i,:); +% E=(upper(i,:)-lower(i,:))/2; +% plot_mat(count,:,2)=E; +% plot_mat(count,:,3)=lower(i,:); +% plot_mat(count,:,4)=upper(i,:); +% count=count+1; +% % plot(x, final(i,:), '-o'); +% % hold on +% % E=(upper(i,:)-lower(i,:))/2; +% % errorbar(x, final(i,:),E); +% end +% end +% +% [r2,c2,z]=size(plot_mat); +% for i=1:r2 +% h2=subplot(1,r2,i); +% scatter(x, plot_mat(i,:,1), 'fill'); +% hold on +% % for j=1:6 +% % plot(x(j),plot_mat(i,j,3), x(j), plot_mat(i,j,4), 'LineStyle', '-', 'LineWidth', 0.5, 'Color', 'k'); +% % end +% errorbar(x,plot_mat(i,:,1),plot_mat(i,:,2), '.', 'Color', 'k'); +% hold off +% ylim([0 3]); +% set(gca, 'XTick', 2:7); +% set(gca, 'XTickLabel', {'0', '1', '2', '3', '4', '5'}); +% if (i>1) +% set(gca, 'YTick', []); +% linkaxes([h1 h2],'y'); +% pos1=get(h1,'Position'); +% pos2=get(h2,'Position'); +% pos2(4)=pos1(4); +% set(h2,'Position',pos2); +% pos1(2)=pos2(2); +% set(h1, 'Position', pos1); +% pos2(1)=pos1(1)+pos1(3); +% set(h2,'Position', pos2); +% else +% ylabel('Odds Ratio'); +% end +% h1=h2; +% +% end +% saveas (gcf, 'Q72_changingOR_negative.fig'); +% close all +% +% +% high_OR=cell.empty; +% count=1; +% x=2:7; +% plot_mat=double.empty; +% for i=1:r +% min_=min(lower(i,:)); +% if sum(final(i,:)>0) && min_>1 +% high_OR{count,1}=qlabel{i,1}; +% high_OR{count,2}=final(i,1); +% high_OR{count,3}=final(i,6); +% plot_mat(count,:,1)=final(i,:); +% E=(upper(i,:)-lower(i,:))/2; +% plot_mat(count,:,2)=E; +% plot_mat(count,:,3)=lower(i,:); +% plot_mat(count,:,4)=upper(i,:); +% count=count+1; +% % plot(x, final(i,:), '-o'); +% % hold on +% % E=(upper(i,:)-lower(i,:))/2; +% % errorbar(x, final(i,:),E); +% end +% end +% [r2,c2,z]=size(plot_mat); +% for i=1:r2 +% h2=subplot(1,r2,i); +% scatter(x, plot_mat(i,:,1), 'fill'); +% hold on +% errorbar(x,plot_mat(i,:,1),plot_mat(i,:,2), '.', 'Color', 'k'); +% hold off +% ylim([0 2.5]); +% set(gca, 'XTick', 2:7); +% set(gca, 'XTickLabel', {'0', '1', '2', '3', '4', '5'}); +% if (i>1) +% set(gca, 'YTick', []); +% linkaxes([h1 h2],'y'); +% pos1=get(h1,'Position'); +% pos2=get(h2,'Position'); +% pos2(4)=pos1(4); +% set(h2,'Position',pos2); +% pos1(2)=pos2(2); +% set(h1, 'Position', pos1); +% pos2(1)=pos1(1)+pos1(3); +% set(h2,'Position', pos2); +% else +% ylabel('Odds Ratio'); +% end +% h1=h2; +% +% end +% saveas (gcf, 'Q72_highOR.fig'); +% close all +% +% low_OR=cell.empty; +% count=1; +% x=2:7; +% plot_mat=double.empty; +% for i=1:r +% max_=max(upper(i,:)); +% if sum(final(i,:)>0) && max_<1 +% low_OR{count,1}=qlabel{i,1}; +% low_OR{count,2}=final(i,1); +% low_OR{count,3}=final(i,6); +% plot_mat(count,:,1)=final(i,:); +% E=(upper(i,:)-lower(i,:))/2; +% plot_mat(count,:,2)=E; +% plot_mat(count,:,3)=lower(i,:); +% plot_mat(count,:,4)=upper(i,:); +% count=count+1; +% % plot(x, final(i,:), '-o'); +% % hold on +% % E=(upper(i,:)-lower(i,:))/2; +% % errorbar(x, final(i,:),E); +% end +% end +% +% [r2,c2,z]=size(plot_mat); +% for i=1:r2 +% h2=subplot(1,r2,i); +% scatter(x, plot_mat(i,:,1), 'fill'); +% hold on +% errorbar(x,plot_mat(i,:,1),plot_mat(i,:,2), '.', 'Color', 'k'); +% hold off +% ylim([0 2.5]); +% set(gca, 'XTick', 2:7); +% set(gca, 'XTickLabel', {'0', '1', '2', '3', '4', '5'}); +% if (i>1) +% set(gca, 'YTick', []); +% linkaxes([h1 h2],'y'); +% pos1=get(h1,'Position'); +% pos2=get(h2,'Position'); +% pos2(4)=pos1(4); +% set(h2,'Position',pos2); +% pos1(2)=pos2(2); +% set(h1, 'Position', pos1); +% pos2(1)=pos1(1)+pos1(3); +% set(h2,'Position', pos2); +% else +% ylabel('Odds Ratio'); +% end +% h1=h2; +% +% end +% saveas (gcf, 'Q72_lowOR.fig'); +% close all +% +% +% diff --git a/physical_activity/video_games_cat_ORv2.m b/physical_activity/video_games_cat_ORv2.m new file mode 100644 index 0000000..c5922b1 --- /dev/null +++ b/physical_activity/video_games_cat_ORv2.m @@ -0,0 +1,446 @@ +cd .. +cd .. +cd matrices +load OR_2013_100714 +load OR_CI_2013_100714 +OR_2013=odds_ratio_cell; +OR_2013_CI=OR_CI; +cd OR_categorical +cd PhysicalActivity +load Q72_cat_OR +load Q72_response_2_OR_2013_cat +load Q72_response_2_OR_2013_cat_CI +OR2=odds_ratio_cell; +OR_CI2=OR_CI; +load Q72_response_3_OR_2013_cat +load Q72_response_3_OR_2013_cat_CI +OR3=odds_ratio_cell; +OR_CI3=OR_CI; +load Q72_response_4_OR_2013_cat +load Q72_response_4_OR_2013_cat_CI +OR4=odds_ratio_cell; +OR_CI4=OR_CI; +load Q72_response_5_OR_2013_cat +load Q72_response_5_OR_2013_cat_CI +OR5=odds_ratio_cell; +OR_CI5=OR_CI; +load Q72_response_6_OR_2013_cat +load Q72_response_6_OR_2013_cat_CI +OR6=odds_ratio_cell; +OR_CI6=OR_CI; +load Q72_response_7_OR_2013_cat +load Q72_response_7_OR_2013_cat_CI +OR7=odds_ratio_cell; +OR_CI7=OR_CI; +cd .. +cd .. +load qlabel_090914 +cd .. +cd programs +cd physical_activity + +indx=find(strcmp(OR_2013(:,1),'Q72')==1); +OR_2013_=OR_2013(indx,:); +OR_2013_CI_=OR_2013_CI(indx,:); + +[r,c]=size(OR2); + +final=zeros(r,7); +lower=zeros(r,7); +upper=zeros(r,7); +for i=1:r + + k=cellfun(@isempty,OR_2013_(i,9)); %only 2013 data + if (k==0) + N=OR_2013_{i,9}; + final(i,1)=N; + C=OR_2013_CI_{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,1)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,1)=str2double(up_); + end + + %check for empty + k=cellfun(@isempty,OR2(i,9)); %only 2013 data + if (k==0) + N=OR2{i,9}; + final(i,2)=N; + C=OR_CI2{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,2)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,2)=str2double(up_); + end + + k=cellfun(@isempty,OR3(i,9)); %only 2013 data + if (k==0) + N=OR3{i,9}; + final(i,3)=N; + C=OR_CI3{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,3)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,3)=str2double(up_); + end + + k=cellfun(@isempty,OR4(i,9)); %only 2013 data + if (k==0) + N=OR4{i,9}; + final(i,4)=N; + C=OR_CI4{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,4)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,4)=str2double(up_); + end + + k=cellfun(@isempty,OR5(i,9)); %only 2013 data + if (k==0) + N=OR5{i,9}; + final(i,5)=N; + C=OR_CI5{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,5)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,5)=str2double(up_); + end + + k=cellfun(@isempty,OR6(i,9)); %only 2013 data + if (k==0) + N=OR6{i,9}; + final(i,6)=N; + C=OR_CI6{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,6)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,6)=str2double(up_); + end + + k=cellfun(@isempty,OR7(i,9)); %only 2013 data + if (k==0) + N=OR7{i,9}; + final(i,7)=N; + C=OR_CI7{i,9}; + a=char(C); + b=strfind(a,', '); + up_=''; + dn_=''; + for p=1:(b(1)-1) + temp=a(p); + dn_=[dn_ temp]; + end + lower(i,7)=str2double(dn_); + for p=(b(1)+1):numel(a) + temp=a(p); + up_=[up_ temp]; + end + upper(i,7)=str2double(up_); + end +end + +% %significantly positive increases---------------------------- +% signif_pos=cell.empty; +% count=1; +% x=1:7; +% plot_mat=double.empty; +% for i=1:r +% max_=max(final(i,:)); +% min_=min(final(i,:)); +% diff=max_-min_; +% diff2=0; +% diff3=final(i,2)-final(i,7); %slope +% for j=2:7 +% for k=2:7 +% if lower(i,j)>upper(i,k) || upper(i,k)0) && diff2==1 && diff>0.7 && diff3<0 %positive slope +% signif_pos{count,1}=qlabel{i,1}; +% signif_pos{count,2}=diff; +% signif_pos{count,3}=final(i,1); +% signif_pos{count,4}=final(i,6); +% plot_mat(count,:,1)=final(i,:); +% E=(upper(i,:)-lower(i,:))/2; +% plot_mat(count,:,2)=E; +% plot_mat(count,:,3)=lower(i,:); +% plot_mat(count,:,4)=upper(i,:); +% count=count+1; +% %plot(x, final(i,:), '-o'); +% %hold on +% %errorbar(x, final(i,:),E); +% end +% end +% +% [r2,c2,z]=size(plot_mat); +% for i=1:r2 +% h2=subplot(1,r2,i); +% scatter(x, plot_mat(i,:,1), 'fill'); +% hold on +% % for j=1:6 +% % plot(x(j),plot_mat(i,j,3), x(j), plot_mat(i,j,4), 'LineStyle', '-', 'LineWidth', 0.5, 'Color', 'k'); +% % end +% errorbar(x,plot_mat(i,:,1),plot_mat(i,:,2), '.', 'Color', 'k'); +% hold off +% ylim([0 3]); +% set(gca, 'XTick', 1:7); +% set(gca, 'XTickLabel', {'0','>0', '>1', '>2', '>3', '>4', '>5'}); +% title(signif_pos{i,1}); +% if (i>1) +% set(gca, 'YTick', []); +% linkaxes([h1 h2],'y'); +% pos1=get(h1,'Position'); +% pos2=get(h2,'Position'); +% pos2(4)=pos1(4); +% set(h2,'Position',pos2); +% pos1(2)=pos2(2); +% set(h1, 'Position', pos1); +% pos2(1)=pos1(1)+pos1(3); +% set(h2,'Position', pos2); +% else +% ylabel('Odds Ratio'); +% end +% h1=h2; +% +% end +% saveas (gcf, 'Q72_changingOR_positivev2.fig'); +% close all +% +% %significantly negative increases---------------------------- +% signif_neg=cell.empty; +% count=1; +% x=1:7; +% plot_mat=double.empty; +% for i=1:r +% max_=max(final(i,:)); +% min_=min(final(i,:)); +% diff=max_-min_; +% diff2=0; +% diff3=final(i,1)-final(i,6); %slope +% for j=2:7 +% for k=2:7 +% if lower(i,j)>upper(i,k) || upper(i,k)0) && diff2==1 && diff>0.5 && diff3>0 %positive slope +% signif_neg{count,1}=qlabel{i,1}; +% signif_neg{count,2}=diff; +% signif_neg{count,3}=final(i,1); +% signif_neg{count,4}=final(i,6); +% plot_mat(count,:,1)=final(i,:); +% E=(upper(i,:)-lower(i,:))/2; +% plot_mat(count,:,2)=E; +% plot_mat(count,:,3)=lower(i,:); +% plot_mat(count,:,4)=upper(i,:); +% count=count+1; +% % plot(x, final(i,:), '-o'); +% % hold on +% % E=(upper(i,:)-lower(i,:))/2; +% % errorbar(x, final(i,:),E); +% end +% end +% +% [r2,c2,z]=size(plot_mat); +% for i=1:r2 +% h2=subplot(1,r2,i); +% scatter(x, plot_mat(i,:,1), 'fill'); +% hold on +% % for j=1:6 +% % plot(x(j),plot_mat(i,j,3), x(j), plot_mat(i,j,4), 'LineStyle', '-', 'LineWidth', 0.5, 'Color', 'k'); +% % end +% errorbar(x,plot_mat(i,:,1),plot_mat(i,:,2), '.', 'Color', 'k'); +% hold off +% ylim([0 3]); +% set(gca, 'XTick', 1:7); +% set(gca, 'XTickLabel',{'0','>0', '>1', '>2', '>3', '>4', '>5'}); +% title(signif_neg{i,1}); +% if (i>1) +% set(gca, 'YTick', []); +% linkaxes([h1 h2],'y'); +% pos1=get(h1,'Position'); +% pos2=get(h2,'Position'); +% pos2(4)=pos1(4); +% set(h2,'Position',pos2); +% pos1(2)=pos2(2); +% set(h1, 'Position', pos1); +% pos2(1)=pos1(1)+pos1(3); +% set(h2,'Position', pos2); +% else +% ylabel('Odds Ratio'); +% end +% h1=h2; +% +% end +% saveas (gcf, 'Q72_changingOR_negative.fig'); +% close all +% +% + +high_OR=cell.empty; +count=1; +x=1:7; +plot_mat=double.empty; +for i=1:r + min_=min(lower(i,2:7)); + if sum(final(i,:)>0) && min_>1 + high_OR{count,1}=qlabel{i,1}; + high_OR{count,2}=final(i,2); + high_OR{count,3}=final(i,7); + plot_mat(count,:,1)=final(i,:); + E=(upper(i,:)-lower(i,:))/2; + plot_mat(count,:,2)=E; + plot_mat(count,:,3)=lower(i,:); + plot_mat(count,:,4)=upper(i,:); + count=count+1; + end +end + +[r2,c2,z]=size(plot_mat); +for i=1:r2 + h2=subplot(1,r2,i); + scatter(x, plot_mat(i,:,1), 'fill'); + hold on + errorbar(x,plot_mat(i,:,1),plot_mat(i,:,2), '.', 'Color', 'k'); + hold off + ylim([0 2.5]); + set(gca, 'XTick', 1:7); + set(gca, 'XTickLabel', {'0', '>0', '>1', '>2', '>3', '>4', '>5'}); + title(high_OR{i,1}); + if (i>1) + set(gca, 'YTick', []); + linkaxes([h1 h2],'y'); + pos1=get(h1,'Position'); + pos2=get(h2,'Position'); + pos2(4)=pos1(4); + set(h2,'Position',pos2); + pos1(2)=pos2(2); + set(h1, 'Position', pos1); + pos2(1)=pos1(1)+pos1(3); + set(h2,'Position', pos2); + else + ylabel('Odds Ratio'); + end + h1=h2; + +end +saveas (gcf, 'Q72_highORv2.fig'); +close all + +low_OR=cell.empty; +count=1; +x=1:7; +plot_mat=double.empty; +for i=1:r + max_=max(upper(i,2:7)); + if sum(final(i,:)>0) && max_<1 + low_OR{count,1}=qlabel{i,1}; + low_OR{count,2}=final(i,1); + low_OR{count,3}=final(i,6); + plot_mat(count,:,1)=final(i,:); + E=(upper(i,:)-lower(i,:))/2; + plot_mat(count,:,2)=E; + plot_mat(count,:,3)=lower(i,:); + plot_mat(count,:,4)=upper(i,:); + count=count+1; + end +end + +[r2,c2,z]=size(plot_mat); +for i=1:r2 + h2=subplot(1,r2,i); + scatter(x, plot_mat(i,:,1), 'fill'); + hold on + errorbar(x,plot_mat(i,:,1),plot_mat(i,:,2), '.', 'Color', 'k'); + hold off + ylim([0 2.5]); + set(gca, 'XTick', 2:7); + set(gca, 'XTickLabel', {'0','>0', '>1', '>2', '>3', '>4', '>5'}); + title(low_OR{i,1}); + if (i>1) + set(gca, 'YTick', []); + linkaxes([h1 h2],'y'); + pos1=get(h1,'Position'); + pos2=get(h2,'Position'); + pos2(4)=pos1(4); + set(h2,'Position',pos2); + pos1(2)=pos2(2); + set(h1, 'Position', pos1); + pos2(1)=pos1(1)+pos1(3); + set(h2,'Position', pos2); + else + ylabel('Odds Ratio'); + end + h1=h2; + +end +saveas (gcf, 'Q72_lowORv2.fig'); +close all + + + diff --git a/physical_activity/videogames_boys_girls_2013_CI.m b/physical_activity/videogames_boys_girls_2013_CI.m new file mode 100644 index 0000000..8ffe6ee --- /dev/null +++ b/physical_activity/videogames_boys_girls_2013_CI.m @@ -0,0 +1,812 @@ +% +% cd .. +% cd .. +% cd data +% cd Controls_061514 +% sex=importdata('sex-NaN.txt', '\t'); +% race=importdata('race-NaN.txt', '\t'); +% weight=importdata('weights-NaN.txt','\t'); +% grade=importdata('grade-NaN.txt','\t'); +% [r,c]=size(sex); +% weight=weight(2:7,2:c); +% race=race(2:7,2:c); +% sex=sex(2:7,2:c); +% grade=grade(2:7,2:c); +% cd .. +% cd results_103114 +% cd cat +% question_mat=importdata('Q72-cat-NaN.txt', '\t'); +% [r,c]=size(question_mat); +% question_mat=question_mat(:,2:c); +% cd .. +% cd .. +% cd .. +% cd programs +% cd physical_activity +% +% conf_mat=cell(59,21); +% conf_mat{1,2}='0 hours'; +% conf_mat{1,3}='0.5-2 hours'; +% conf_mat{1,4}='3-4 hours'; +% conf_mat{1,5}='5+ hours'; +% +% conf_mat{1,6}='0 hours'; +% conf_mat{1,7}='0.5-2 hours'; +% conf_mat{1,8}='3-4 hours'; +% conf_mat{1,9}='5+ hours'; +% +% conf_mat{1,10}='0 hours'; +% conf_mat{1,11}='0.5-2 hours'; +% conf_mat{1,12}='3-4 hours'; +% conf_mat{1,13}='5+ hours'; +% +% conf_mat{1,14}='0 hours'; +% conf_mat{1,15}='0.5-2 hours'; +% conf_mat{1,16}='2-4 hours'; +% conf_mat{1,17}='5+ hours'; +% +% conf_mat{1,18}='0 hours'; +% conf_mat{1,19}='0.5-1 hours'; +% conf_mat{1,20}='3-4 hours'; +% conf_mat{1,21}='5+ hours'; +% +% conf_mat{2,1}='total'; +% conf_mat{3,1}='girls'; +% conf_mat{4,1}='boys'; +% conf_mat{5,1}='Wg'; +% conf_mat{6,1}='Wb'; +% conf_mat{7,1}='Bg'; +% conf_mat{8,1}='Bb'; +% conf_mat{9,1}='Hg'; +% conf_mat{10,1}='Hb'; +% conf_mat{11,1}='Og'; +% conf_mat{12,1}='Ob'; +% conf_mat{13,1}='W'; +% conf_mat{14,1}='B'; +% conf_mat{15,1}='H'; +% conf_mat{16,1}='O'; +% conf_mat{17,1}='9'; +% conf_mat{18,1}='10'; +% conf_mat{19,1}='11'; +% conf_mat{20,1}='12'; +% conf_mat{21,1}='9g'; +% conf_mat{22,1}='9b'; +% conf_mat{23,1}='10g'; +% conf_mat{24,1}='10b'; +% conf_mat{25,1}='11g'; +% conf_mat{26,1}='11b'; +% conf_mat{27,1}='12g'; +% conf_mat{28,1}='12b'; +% +% conf_mat{29,1}='H9g'; +% conf_mat{30,1}='H10g'; +% conf_mat{31,1}='H11g'; +% conf_mat{32,1}='H12g'; +% conf_mat{33,1}='H9b'; +% conf_mat{34,1}='H10b'; +% conf_mat{35,1}='H11b'; +% conf_mat{36,1}='H12b'; +% conf_mat{37,1}='W9g'; +% conf_mat{38,1}='W10g'; +% conf_mat{39,1}='W11g'; +% conf_mat{40,1}='W12g'; +% conf_mat{41,1}='W9b'; +% conf_mat{42,1}='W10b'; +% conf_mat{43,1}='W11b'; +% conf_mat{44,1}='W12b'; +% +% conf_mat{45,1}='B9g'; +% conf_mat{46,1}='B10g'; +% conf_mat{47,1}='B11g'; +% conf_mat{48,1}='B12g'; +% conf_mat{49,1}='B9b'; +% conf_mat{50,1}='B10b'; +% conf_mat{51,1}='B11b'; +% conf_mat{52,1}='B12b'; +% conf_mat{53,1}='O9g'; +% conf_mat{54,1}='O10g'; +% conf_mat{55,1}='O11g'; +% conf_mat{56,1}='O12g'; +% conf_mat{57,1}='O9b'; +% conf_mat{58,1}='O10b'; +% conf_mat{59,1}='O11b'; +% conf_mat{60,1}='O12b'; +% +% n_mat=zeros(59,r); +% x_mat=zeros(59,4,r); +% +% boys=double.empty; +% girls=double.empty; +% total=double.empty; +% c=1; +% +% +% %key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +% %E(5)=3, %F(6)=4, %G(7)=5 +% for i=1:r +% % total(i)=TOTAL(i,1); +% for j=1:7 +% n=zeros(59,1); +% x=zeros(59,4); +% +% index_yes{i}=find(question_mat(i,:)==j); +% index_girls{i}=find(sex(i,:)==1); +% index_boys{i}=find(sex(i,:)==2); +% index_W{i}=find(race(i,:)== 1 ); +% index_B{i}=find(race(i,:)== 2 ); +% index_H{i}=find(race(i,:)== 3 ); +% index_O{i}=find(race(i,:)== 4 ); +% index_9{i}=find(grade(i,:)== 1 ); +% index_10{i}=find(grade(i,:)== 2 ); +% index_11{i}=find(grade(i,:)== 3 ); +% index_12{i}=find(grade(i,:)== 4 ); +% +% index_W9{i}=intersect(index_9{i},index_W{i}); +% index_W10{i}=intersect(index_10{i},index_W{i}); +% index_W11{i}=intersect(index_11{i},index_W{i}); +% index_W12{i}=intersect(index_12{i},index_W{i}); +% +% index_B9{i}=intersect(index_9{i},index_B{i}); +% index_B10{i}=intersect(index_10{i},index_B{i}); +% index_B11{i}=intersect(index_11{i},index_B{i}); +% index_B12{i}=intersect(index_12{i},index_B{i}); +% +% index_H9{i}=intersect(index_9{i},index_H{i}); +% index_H10{i}=intersect(index_10{i},index_H{i}); +% index_H11{i}=intersect(index_11{i},index_H{i}); +% index_H12{i}=intersect(index_12{i},index_H{i}); +% +% index_O9{i}=intersect(index_9{i},index_O{i}); +% index_O10{i}=intersect(index_10{i},index_O{i}); +% index_O11{i}=intersect(index_11{i},index_O{i}); +% index_O12{i}=intersect(index_12{i},index_O{i}); +% +% index_missQ{i}=find(question_mat(i,:)== 0); %students who didn't answer the Q +% index_nomiss{i}=find(question_mat(i,:)>0); %answers that were NOT missing (ie. 0's and 1's / no's and yes's) +% +% index_total_b{i}=intersect(index_nomiss{i},index_boys{i}); %index of all boys who answered +% index_total_g{i}=intersect(index_nomiss{i},index_girls{i}); %index of all girls who answered +% +% w=weight(i,:)'; +% total_ans(i)=nansum(w(index_nomiss{i})); +% total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered +% total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered +% total_W{i}=nansum(w(intersect(index_nomiss{i}, index_W{i}))); %total # of white students who answered +% total_B{i}=nansum(w(intersect(index_nomiss{i}, index_B{i}))); %total # of black students who answered +% total_H{i}=nansum(w(intersect(index_nomiss{i}, index_H{i}))); %total # of hispanic students who answered +% total_O{i}=nansum(w(intersect(index_nomiss{i}, index_O{i}))); %total # of "other" students who answered +% +% total_w(i)=total_W{i}; +% total_b(i)=total_B{i}; +% total_h(i)=total_H{i}; +% total_o(i)=total_O{i}; +% +% total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i}))); +% total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i}))); +% total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i}))); +% total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i}))); +% total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i}))); +% total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i}))); +% total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i}))); +% total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i}))); +% total_9(i)=nansum(w(intersect((index_9{i}),index_nomiss{i}))); +% total_10(i)=nansum(w(intersect((index_10{i}),index_nomiss{i}))); +% total_11(i)=nansum(w(intersect((index_11{i}),index_nomiss{i}))); +% total_12(i)=nansum(w(intersect((index_12{i}),index_nomiss{i}))); +% +% total_9G(i)=nansum(w(intersect(index_9{i},index_total_g{i}))); +% total_10G(i)=nansum(w(intersect(index_10{i},index_total_g{i}))); +% total_11G(i)=nansum(w(intersect(index_11{i},index_total_g{i}))); +% total_12G(i)=nansum(w(intersect(index_12{i},index_total_g{i}))); +% +% total_9B(i)=nansum(w(intersect(index_9{i},index_total_b{i}))); +% total_10B(i)=nansum(w(intersect(index_10{i},index_total_b{i}))); +% total_11B(i)=nansum(w(intersect(index_11{i},index_total_b{i}))); +% total_12B(i)=nansum(w(intersect(index_12{i},index_total_b{i}))); +% +% total_9G_W(i)=nansum(w(intersect(index_W9{i},index_total_g{i}))); +% total_9B_W(i)=nansum(w(intersect(index_W9{i},index_total_b{i}))); +% total_9G_B(i)=nansum(w(intersect(index_B9{i},index_total_g{i}))); +% total_9B_B(i)=nansum(w(intersect(index_B9{i},index_total_b{i}))); +% +% total_9G_H(i)=nansum(w(intersect(index_H9{i},index_total_g{i}))); +% total_9B_H(i)=nansum(w(intersect(index_H9{i},index_total_b{i}))); +% total_9G_O(i)=nansum(w(intersect(index_O9{i},index_total_g{i}))); +% total_9B_O(i)=nansum(w(intersect(index_O9{i},index_total_b{i}))); +% +% total_10G_W(i)=nansum(w(intersect(index_W10{i},index_total_g{i}))); +% total_10B_W(i)=nansum(w(intersect(index_W10{i},index_total_b{i}))); +% total_10G_B(i)=nansum(w(intersect(index_B10{i},index_total_g{i}))); +% total_10B_B(i)=nansum(w(intersect(index_B10{i}, index_total_b{i}))); +% +% total_10G_H(i)=nansum(w(intersect(index_H10{i},index_total_g{i}))); +% total_10B_H(i)=nansum(w(intersect(index_H10{i},index_total_b{i}))); +% total_10G_O(i)=nansum(w(intersect(index_O10{i},index_total_g{i}))); +% total_10B_O(i)=nansum(w(intersect(index_O10{i},index_total_b{i}))); +% +% total_11G_W(i)=nansum(w(intersect(index_W11{i},index_total_g{i}))); +% total_11B_W(i)=nansum(w(intersect(index_W11{i},index_total_b{i}))); +% total_11G_B(i)=nansum(w(intersect(index_B11{i},index_total_g{i}))); +% total_11B_B(i)=nansum(w(intersect(index_B11{i},index_total_b{i}))); +% +% total_11G_H(i)=nansum(w(intersect(index_H11{i},index_total_g{i}))); +% total_11B_H(i)=nansum(w(intersect(index_H11{i},index_total_b{i}))); +% total_11G_O(i)=nansum(w(intersect(index_O11{i},index_total_g{i}))); +% total_11B_O(i)=nansum(w(intersect(index_O11{i},index_total_b{i}))); +% +% total_12G_W(i)=nansum(w(intersect(index_W12{i},index_total_g{i}))); +% total_12B_W(i)=nansum(w(intersect(index_W12{i},index_total_b{i}))); +% total_12G_B(i)=nansum(w(intersect(index_B12{i},index_total_g{i}))); +% total_12B_B(i)=nansum(w(intersect(index_B12{i},index_total_b{i}))); +% +% total_12G_H(i)=nansum(w(intersect(index_H12{i},index_total_g{i}))); +% total_12B_H(i)=nansum(w(intersect(index_H12{i},index_total_b{i}))); +% total_12G_O(i)=nansum(w(intersect(index_O12{i},index_total_g{i}))); +% total_12B_O(i)=nansum(w(intersect(index_O12{i},index_total_b{i}))); +% +% total_9_W(i)=nansum(w(intersect(index_nomiss{i},index_W9{i}))); +% total_9_B(i)=nansum(w(intersect(index_nomiss{i},index_B9{i}))); +% total_9_H(i)=nansum(w(intersect(index_nomiss{i},index_H9{i}))); +% total_9_O(i)=nansum(w(intersect(index_nomiss{i},index_O9{i}))); +% +% total_10_W(i)=nansum(w(intersect(index_nomiss{i},index_W10{i}))); +% total_10_B(i)=nansum(w(intersect(index_nomiss{i},index_B10{i}))); +% total_10_H(i)=nansum(w(intersect(index_nomiss{i},index_H10{i}))); +% total_10_O(i)=nansum(w(intersect(index_nomiss{i},index_O10{i}))); +% +% total_11_W(i)=nansum(w(intersect(index_nomiss{i},index_W11{i}))); +% total_11_B(i)=nansum(w(intersect(index_nomiss{i},index_B11{i}))); +% total_11_H(i)=nansum(w(intersect(index_nomiss{i},index_H11{i}))); +% total_11_O(i)=nansum(w(intersect(index_nomiss{i},index_O11{i}))); +% +% total_12_W(i)=nansum(w(intersect(index_nomiss{i},index_W12{i}))); +% total_12_B(i)=nansum(w(intersect(index_nomiss{i},index_B12{i}))); +% total_12_H(i)=nansum(w(intersect(index_nomiss{i},index_H12{i}))); +% total_12_O(i)=nansum(w(intersect(index_nomiss{i},index_O12{i}))); +% +% w=weight(i,:)'; +% index_yesgirls{i}=intersect(index_yes{i},index_girls{i}); +% index_yesboys{i}=intersect(index_yes{i},index_boys{i}); +% yes_girls(i)=nansum(w(index_yesgirls{i})); +% yes_boys(i)=nansum(w(index_yesboys{i})); +% total_yes(i)=nansum(w(index_yes{i})); +% +% yes_W(i)=nansum(w(intersect(index_yes{i}, index_W{i}))); +% yes_B(i)=nansum(w(intersect(index_yes{i}, index_B{i}))); +% yes_H(i)=nansum(w(intersect(index_yes{i}, index_H{i}))); +% yes_O(i)=nansum(w(intersect(index_yes{i}, index_O{i}))); +% yes_WG(i)=nansum(w(intersect(index_yesgirls{i},index_W{i}))); +% yes_BG(i)=nansum(w(intersect(index_yesgirls{i},index_B{i}))); +% yes_HG(i)=nansum(w(intersect(index_yesgirls{i},index_H{i}))); +% yes_OG(i)=nansum(w(intersect(index_yesgirls{i},index_O{i}))); +% yes_WB(i)=nansum(w(intersect(index_yesboys{i},index_W{i}))); +% yes_BB(i)=nansum(w(intersect(index_yesboys{i},index_B{i}))); +% yes_HB(i)=nansum(w(intersect(index_yesboys{i},index_H{i}))); +% yes_OB(i)=nansum(w(intersect(index_yesboys{i},index_O{i}))); +% yes_9(i)=nansum(w(intersect(index_yes{i},index_9{i}))); +% yes_10(i)=nansum(w(intersect(index_yes{i},index_10{i}))); +% yes_11(i)=nansum(w(intersect(index_yes{i},index_11{i}))); +% yes_12(i)=nansum(w(intersect(index_yes{i},index_12{i}))); +% yes_9b(i)=nansum(w(intersect(index_yesboys{i},index_9{i}))); +% yes_10b(i)=nansum(w(intersect(index_yesboys{i},index_10{i}))); +% yes_11b(i)=nansum(w(intersect(index_yesboys{i},index_11{i}))); +% yes_12b(i)=nansum(w(intersect(index_yesboys{i},index_12{i}))); +% yes_9g(i)=nansum(w(intersect(index_yesgirls{i},index_9{i}))); +% yes_10g(i)=nansum(w(intersect(index_yesgirls{i},index_10{i}))); +% yes_11g(i)=nansum(w(intersect(index_yesgirls{i},index_11{i}))); +% yes_12g(i)=nansum(w(intersect(index_yesgirls{i},index_12{i}))); +% +% +% yes_9WB(i)=nansum(w(intersect(index_yesboys{i},index_W9{i}))); +% yes_10WB(i)=nansum(w(intersect(index_yesboys{i},index_W10{i}))); +% yes_11WB(i)=nansum(w(intersect(index_yesboys{i},index_W11{i}))); +% yes_12WB(i)=nansum(w(intersect(index_yesboys{i},index_W12{i}))); +% yes_9WG(i)=nansum(w(intersect(index_yesgirls{i},index_W9{i}))); +% yes_10WG(i)=nansum(w(intersect(index_yesgirls{i},index_W10{i}))); +% yes_11WG(i)=nansum(w(intersect(index_yesgirls{i},index_W11{i}))); +% yes_12WG(i)=nansum(w(intersect(index_yesgirls{i},index_W12{i}))); +% +% yes_9W(i)=nansum(w(intersect(index_yes{i},index_W9{i}))); +% yes_10W(i)=nansum(w(intersect(index_yes{i},(index_W10{i})))); +% yes_11W(i)=nansum(w(intersect(index_yes{i},(index_W11{i})))); +% yes_12W(i)=nansum(w(intersect(index_yes{i},(index_W12{i})))); +% +% yes_9B(i)=nansum(w(intersect(index_yes{i},(index_B9{i})))); +% yes_10B(i)=nansum(w(intersect(index_yes{i},(index_B10{i})))); +% yes_11B(i)=nansum(w(intersect(index_yes{i},(index_B11{i})))); +% yes_12B(i)=nansum(w(intersect(index_yes{i},(index_B12{i})))); +% +% yes_9H(i)=nansum(w(intersect(index_yes{i},(index_H9{i})))); +% yes_10H(i)=nansum(w(intersect(index_yes{i},(index_H10{i})))); +% yes_11H(i)=nansum(w(intersect(index_yes{i},(index_H11{i})))); +% yes_12H(i)=nansum(w(intersect(index_yes{i},(index_H12{i})))); +% +% yes_9O(i)=nansum(w(intersect(index_yes{i},(index_O9{i})))); +% yes_10O(i)=nansum(w(intersect(index_yes{i},(index_O10{i})))); +% yes_11O(i)=nansum(w(intersect(index_yes{i},(index_O11{i})))); +% yes_12O(i)=nansum(w(intersect(index_yes{i},(index_O12{i})))); +% +% yes_9BB(i)=nansum(w(intersect(index_yesboys{i},index_B9{i}))); +% yes_10BB(i)=nansum(w(intersect(index_yesboys{i},index_B10{i}))); +% yes_11BB(i)=nansum(w(intersect(index_yesboys{i},index_B11{i}))); +% yes_12BB(i)=nansum(w(intersect(index_yesboys{i},index_B12{i}))); +% yes_9BG(i)=nansum(w(intersect(index_yesgirls{i},index_B9{i}))); +% yes_10BG(i)=nansum(w(intersect(index_yesgirls{i},index_B10{i}))); +% yes_11BG(i)=nansum(w(intersect(index_yesgirls{i},index_B11{i}))); +% yes_12BG(i)=nansum(w(intersect(index_yesgirls{i},index_B12{i}))); +% +% yes_9HB(i)=nansum(w(intersect(index_yesboys{i},index_H9{i}))); +% yes_10HB(i)=nansum(w(intersect(index_yesboys{i},index_H10{i}))); +% yes_11HB(i)=nansum(w(intersect(index_yesboys{i},index_H11{i}))); +% yes_12HB(i)=nansum(w(intersect(index_yesboys{i},index_H12{i}))); +% yes_9HG(i)=nansum(w(intersect(index_yesgirls{i},index_H9{i}))); +% yes_10HG(i)=nansum(w(intersect(index_yesgirls{i},index_H10{i}))); +% yes_11HG(i)=nansum(w(intersect(index_yesgirls{i},index_H11{i}))); +% yes_12HG(i)=nansum(w(intersect(index_yesgirls{i},index_H12{i}))); +% +% yes_9OB(i)=nansum(w(intersect(index_yesboys{i},index_O9{i}))); +% yes_10OB(i)=nansum(w(intersect(index_yesboys{i},index_O10{i}))); +% yes_11OB(i)=nansum(w(intersect(index_yesboys{i},index_O11{i}))); +% yes_12OB(i)=nansum(w(intersect(index_yesboys{i},index_O12{i}))); +% yes_9OG(i)=nansum(w(intersect(index_yesgirls{i},index_O9{i}))); +% yes_10OG(i)=nansum(w(intersect(index_yesgirls{i},index_O10{i}))); +% yes_11OG(i)=nansum(w(intersect(index_yesgirls{i},index_O11{i}))); +% yes_12OG(i)=nansum(w(intersect(index_yesgirls{i},index_O12{i}))); +% +% girls(1, c)=yes_girls(i)/total_girls(i)*100; %girls +% girls(2, c)=yes_WG(i)/total_Wg(i)*100; %WG +% girls(3, c)=yes_BG(i)/total_Bg(i)*100; %BG +% girls(4, c)=yes_HG(i)/total_Hg(i)*100; %HG +% girls(5, c)=yes_OG(i)/total_Og(i)*100; %OG +% girls(6, c)=yes_9g(i)/total_9G(i)*100; +% girls(7, c)=yes_10g(i)/total_10G(i)*100; +% girls(8, c)=yes_11g(i)/total_11G(i)*100; +% girls(9, c)=yes_12g(i)/total_12G(i)*100; +% girls(10, c)=yes_9WG(i)/total_9G_W(i)*100; +% girls(11, c)=yes_10WG(i)/total_10G_W(i)*100; +% girls(12, c)=yes_11WG(i)/total_11G_W(i)*100; +% girls(13, c)=yes_12WG(i)/total_12G_W(i)*100; +% girls(14, c)=yes_9BG(i)/total_9G_B(i)*100; +% girls(15, c)=yes_10BG(i)/total_10G_B(i)*100; +% girls(16, c)=yes_11BG(i)/total_11G_B(i)*100; +% girls(17, c)=yes_12BG(i)/total_12G_B(i)*100; +% girls(18, c)=yes_9HG(i)/total_9G_H(i)*100; +% girls(19, c)=yes_10HG(i)/total_10G_H(i)*100; +% girls(20, c)=yes_11HG(i)/total_11G_H(i)*100; +% girls(21, c)=yes_12HG(i)/total_12G_H(i)*100; +% girls(22, c)=yes_9OG(i)/total_9G_O(i)*100; +% girls(23, c)=yes_10OG(i)/total_10G_O(i)*100; +% girls(24, c)=yes_11OG(i)/total_11G_O(i)*100; +% girls(25, c)=yes_12OG(i)/total_12G_O(i)*100; +% +% boys(1, c)=yes_boys(i)/total_boys(i)*100; %boys +% boys(2, c)=yes_WB(i)/total_Wb(i)*100; %WB +% boys (3, c)=yes_BB(i)/total_Bb(i)*100; %BB +% boys(4, c)=yes_HB(i)/total_Hb(i)*100; %HB +% boys(5, c)=yes_OB(i)/total_Ob(i)*100; %OB +% boys(6, c)=yes_9b(i)/total_9B(i)*100; +% boys(7, c)=yes_10b(i)/total_10B(i)*100; +% boys(8, c)=yes_11b(i)/total_11B(i)*100; +% boys(9, c)=yes_12b(i)/total_12B(i)*100; +% boys(10, c)=yes_9WB(i)/total_9B_W(i)*100; +% boys(11, c)=yes_10WB(i)/total_10B_W(i)*100; +% boys(12, c)=yes_11WB(i)/total_11B_W(i)*100; +% boys(13, c)=yes_12WB(i)/total_12B_W(i)*100; +% boys(14, c)=yes_9BB(i)/total_9B_B(i)*100; +% boys(15, c)=yes_10BB(i)/total_10B_B(i)*100; +% boys(16, c)=yes_11BB(i)/total_11B_B(i)*100; +% boys(17, c)=yes_12BB(i)/total_12B_B(i)*100; +% boys(18, c)=yes_9HB(i)/total_9B_H(i)*100; +% boys(19, c)=yes_10HB(i)/total_10B_H(i)*100; +% boys(20, c)=yes_11HB(i)/total_11B_H(i)*100; +% boys(21, c)=yes_12HB(i)/total_12B_H(i)*100; +% boys(22, c)=yes_9OB(i)/total_9B_O(i)*100; +% boys(23, c)=yes_10OB(i)/total_10B_O(i)*100; +% boys(24, c)=yes_11OB(i)/total_11B_O(i)*100; +% boys(25, c)=yes_12OB(i)/total_12B_O(i)*100; +% +% total(1,c)=total_yes(i)/total_ans(i)*100; %total +% total(2,c)=yes_boys(i)/total_boys(i)*100; %boys +% total(3,c)=yes_girls(i)/total_girls(i)*100; %girls +% total(4,c)=yes_W(i)/total_w(i)*100; %whites +% total(5,c)=yes_B(i)/total_b(i)*100; %blacks +% total(6,c)=yes_H(i)/total_h(i)*100; %hispanics +% total(7,c)=yes_O(i)/total_o(i)*100; %other +% total(8,c)=yes_9(i)/total_9(i)*100; +% total(9,c)=yes_10(i)/total_10(i)*100; +% total(10,c)=yes_11(i)/total_11(i)*100; +% total(11,c)=yes_12(i)/total_12(i)*100; +% total(12, c)=yes_9W(i)/total_9_W(i)*100; +% total(13, c)=yes_10W(i)/total_10_W(i)*100; +% total(14, c)=yes_11W(i)/total_11_W(i)*100; +% total(15, c)=yes_12W(i)/total_12_W(i)*100; +% total(16, c)=yes_9B(i)/total_9_B(i)*100; +% total(17, c)=yes_10B(i)/total_10_B(i)*100; +% total(18, c)=yes_11B(i)/total_11_B(i)*100; +% total(19, c)=yes_12B(i)/total_12_B(i)*100; +% total(20, c)=yes_9H(i)/total_9_H(i)*100; +% total(21, c)=yes_10H(i)/total_10_H(i)*100; +% total(22, c)=yes_11H(i)/total_11_H(i)*100; +% total(23, c)=yes_12H(i)/total_12_H(i)*100; +% total(24, c)=yes_9O(i)/total_9_O(i)*100; +% total(25, c)=yes_10O(i)/total_10_O(i)*100; +% total(26, c)=yes_11O(i)/total_11_O(i)*100; +% total(27, c)=yes_12O(i)/total_12_O(i)*100; +% +% c=c+1; +% +% %for stats +% n_mat (1,i)= total_ans(i); +% n_mat (2,i)=total_girls(i); +% n_mat (3,i)=total_boys(i); +% n_mat (4,i)=total_Wg(i); +% n_mat (5,i)=total_Wb(i); +% n_mat (6,i)=total_Bg(i); +% n_mat (7,i)=total_Bb(i); +% n_mat (8,i)=total_Hg(i); +% n_mat (9,i)=total_Hb(i); +% n_mat (10,i)=total_Og(i); +% n_mat (11,i)=total_Ob(i); +% n_mat (12,i)=total_w(i); +% n_mat (13,i)=total_b(i); +% n_mat (14,i)=total_h(i); +% n_mat (15,i)=total_o(i); +% n_mat (16,i)=total_9(i); +% n_mat (17,i)=total_10(i); +% n_mat (18,i)=total_11(i); +% n_mat (19,i)=total_12(i); +% n_mat (20,i)=total_9G(i); +% n_mat (21,i)=total_9B(i); +% n_mat (22,i)=total_10G(i); +% n_mat (23,i)=total_10B(i); +% n_mat (24,i)=total_11G(i); +% n_mat (25,i)=total_11B(i); +% n_mat (26,i)=total_12G(i); +% n_mat (27,i)=total_12B(i); +% +% n_mat (28,i)=total_9G_H(i); +% n_mat (29,i)=total_10G_H(i); +% n_mat (30,i)=total_11G_H(i); +% n_mat (31,i)=total_12G_H(i); +% n_mat (32,i)=total_9B_H(i); +% n_mat (33,i)=total_10B_H(i); +% n_mat (34,i)=total_11B_H(i); +% n_mat (35,i)=total_12B_H(i); +% +% n_mat (36,i)=total_9G_W(i); +% n_mat (37,i)=total_10G_W(i); +% n_mat (38,i)=total_11G_W(i); +% n_mat (39,i)=total_12G_W(i); +% n_mat (40,i)=total_9B_W(i); +% n_mat (41,i)=total_10B_W(i); +% n_mat (42,i)=total_11B_W(i); +% n_mat (43,i)=total_12B_W(i); +% +% n_mat (44,i)=total_9G_B(i); +% n_mat (45,i)=total_10G_B(i); +% n_mat (46,i)=total_11G_B(i); +% n_mat (47,i)=total_12G_B(i); +% n_mat (48,i)=total_9B_B(i); +% n_mat (49,i)=total_10B_B(i); +% n_mat (50,i)=total_11B_B(i); +% n_mat (51,i)=total_12B_B(i); +% +% n_mat (52,i)=total_9G_O(i); +% n_mat (53,i)=total_10G_O(i); +% n_mat (54,i)=total_11G_O(i); +% n_mat (55,i)=total_12G_O(i); +% n_mat (56,i)=total_9B_O(i); +% n_mat (57,i)=total_10B_O(i); +% n_mat (58,i)=total_11B_O(i); +% n_mat (59,i)=total_12B_O(i); +% +% if (j>1 && j<5)%0.5-1 hour hours or less +% x(1,2)=total_yes(i); +% x(2,2)=yes_girls(i); +% x(3,2)=yes_boys(i); +% x(4,2)=yes_WG(i); +% x(5,2)=yes_WB(i); +% x(6,2)=yes_BG(i); +% x(7,2)=yes_BB(i); +% x(8,2)=yes_HG(i); +% x(9,2)=yes_HB(i); +% x(10,2)=yes_OG(i); +% x(11,2)=yes_OB(i); +% x(12,2)=yes_W(i); +% x(13,2)=yes_B(i); +% x(14,2)=yes_H(i); +% x(15,2)=yes_O(i); +% x(16,2)=yes_9(i); +% x(17,2)=yes_10(i); +% x(18,2)=yes_11(i); +% x(19,2)=yes_12(i); +% x(20,2)=yes_9g(i); +% x(21,2)=yes_9b(i); +% x(22,2)=yes_10g(i); +% x(23,2)=yes_10b(i); +% x(24,2)=yes_11g(i); +% x(25,2)=yes_11b(i); +% x(26,2)=yes_12g(i); +% x(27,2)=yes_12b(i); +% +% x(28,2)=yes_9HG(i); +% x(29, 2)=yes_10HG(i); +% x(30, 2)=yes_11HG(i); +% x(31, 2)=yes_12HG(i); +% x(32, 2)=yes_9HB(i); +% x(33, 2)=yes_10HB(i); +% x(34, 2)=yes_11HB(i); +% x(35, 2)=yes_12HB(i); +% +% x(36, 2)=yes_9WG(i); +% x(37, 2)=yes_10WG(i); +% x(38, 2)=yes_11WG(i); +% x(39, 2)=yes_12WG(i); +% x(40, 2)=yes_9WB(i); +% x(41, 2)=yes_10WB(i); +% x(42, 2)=yes_11WB(i); +% x(43, 2)=yes_12WB(i); +% +% x(44, 2)=yes_9BG(i); +% x(45, 2)=yes_10BG(i); +% x(46, 2)=yes_11BG(i); +% x(47, 2)=yes_12BG(i); +% x(48, 2)=yes_9BB(i); +% x(49, 2)=yes_10BB(i); +% x(50, 2)=yes_11BB(i); +% x(51, 2)=yes_12BB(i); +% +% x(52, 2)=yes_9OG(i); +% x(53, 2)=yes_10OG(i); +% x(54, 2)=yes_11OG(i); +% x(55, 2)=yes_12OG(i); +% x(56, 2)=yes_9OB(i); +% x(57, 2)=yes_10OB(i); +% x(58, 2)=yes_11OB(i); +% x(59, 2)=yes_12OB(i); +% +% x_mat(:,2, i)=x_mat(:, 2, i)+x(:,2); +% elseif (j>4 && j<7) %3-4 hours hours or more +% x(1,3)=total_yes(i); +% x(2,3)=yes_girls(i); +% x(3,3)=yes_boys(i); +% x(4,3)=yes_WG(i); +% x(5,3)=yes_WB(i); +% x(6,3)=yes_BG(i); +% x(7,3)=yes_BB(i); +% x(8,3)=yes_HG(i); +% x(9,3)=yes_HB(i); +% x(10,3)=yes_OG(i); +% x(11,3)=yes_OB(i); +% x(12,3)=yes_W(i); +% x(13,3)=yes_B(i); +% x(14,3)=yes_H(i); +% x(15,3)=yes_O(i); +% x(16,3)=yes_9(i); +% x(17,3)=yes_10(i); +% x(18,3)=yes_11(i); +% x(19,3)=yes_12(i); +% x(20,3)=yes_9g(i); +% x(21,3)=yes_9b(i); +% x(22,3)=yes_10g(i); +% x(23,3)=yes_10b(i); +% x(24,3)=yes_11g(i); +% x(25,3)=yes_11b(i); +% x(26,3)=yes_12g(i); +% x(27,3)=yes_12b(i); +% +% x(28,3)=yes_9HG(i); +% x(29, 3)=yes_10HG(i); +% x(30, 3)=yes_11HG(i); +% x(31, 3)=yes_12HG(i); +% x(32, 3)=yes_9HB(i); +% x(33, 3)=yes_10HB(i); +% x(34, 3)=yes_11HB(i); +% x(35, 3)=yes_12HB(i); +% +% x(36, 3)=yes_9WG(i); +% x(37, 3)=yes_10WG(i); +% x(38, 3)=yes_11WG(i); +% x(39, 3)=yes_12WG(i); +% x(40, 3)=yes_9WB(i); +% x(41, 3)=yes_10WB(i); +% x(42, 3)=yes_11WB(i); +% x(43, 3)=yes_12WB(i); +% +% x(44, 3)=yes_9BG(i); +% x(45, 3)=yes_10BG(i); +% x(46, 3)=yes_11BG(i); +% x(47, 3)=yes_12BG(i); +% x(48, 3)=yes_9BB(i); +% x(49, 3)=yes_10BB(i); +% x(50, 3)=yes_11BB(i); +% x(51, 3)=yes_12BB(i); +% +% x(52, 3)=yes_9OG(i); +% x(53, 3)=yes_10OG(i); +% x(54, 3)=yes_11OG(i); +% x(55, 3)=yes_12OG(i); +% x(56, 3)=yes_9OB(i); +% x(57, 3)=yes_10OB(i); +% x(58, 3)=yes_11OB(i); +% x(59, 3)=yes_12OB(i); +% +% x_mat(:,3, i)=x_mat(:,3, i)+x(:,3); +% else +% if j==1 +% F=1; +% elseif (j==7) +% F=4; +% end +% x_mat(1,F, i)=total_yes(i); +% x_mat(2,F, i)=yes_girls(i); +% x_mat(3,F, i)=yes_boys(i); +% x_mat(4,F, i)=yes_WG(i); +% x_mat(5,F, i)=yes_WB(i); +% x_mat(6,F, i)=yes_BG(i); +% x_mat(7,F, i)=yes_BB(i); +% x_mat(8,F, i)=yes_HG(i); +% x_mat(9,F, i)=yes_HB(i); +% x_mat(10,F, i)=yes_OG(i); +% x_mat(11,F, i)=yes_OB(i); +% x_mat(12,F, i)=yes_W(i); +% x_mat(13,F, i)=yes_B(i); +% x_mat(14,F, i)=yes_H(i); +% x_mat(15,F, i)=yes_O(i); +% x_mat(16,F, i)=yes_9(i); +% x_mat(17,F, i)=yes_10(i); +% x_mat(18,F, i)=yes_11(i); +% x_mat(19,F, i)=yes_12(i); +% x_mat(20,F, i)=yes_9g(i); +% x_mat(21,F, i)=yes_9b(i); +% x_mat(22,F, i)=yes_10g(i); +% x_mat(23,F, i)=yes_10b(i); +% x_mat(24,F, i)=yes_11g(i); +% x_mat(25,F, i)=yes_11b(i); +% x_mat(26,F, i)=yes_12g(i); +% x_mat(27,F, i)=yes_12b(i); +% +% x_mat(28,F, i)=yes_9HG(i); +% x_mat(29, F, i)=yes_10HG(i); +% x_mat(30, F, i)=yes_11HG(i); +% x_mat(31, F, i)=yes_12HG(i); +% x_mat(32, F, i)=yes_9HB(i); +% x_mat(33, F, i)=yes_10HB(i); +% x_mat(34, F, i)=yes_11HB(i); +% x_mat(35, F, i)=yes_12HB(i); +% +% x_mat(36, F, i)=yes_9WG(i); +% x_mat(37, F, i)=yes_10WG(i); +% x_mat(38, F, i)=yes_11WG(i); +% x_mat(39, F, i)=yes_12WG(i); +% x_mat(40, F, i)=yes_9WB(i); +% x_mat(41, F, i)=yes_10WB(i); +% x_mat(42, F, i)=yes_11WB(i); +% x_mat(43, F, i)=yes_12WB(i); +% +% x_mat(44, F, i)=yes_9BG(i); +% x_mat(45, F, i)=yes_10BG(i); +% x_mat(46, F, i)=yes_11BG(i); +% x_mat(47, F, i)=yes_12BG(i); +% x_mat(48, F, i)=yes_9BB(i); +% x_mat(49, F, i)=yes_10BB(i); +% x_mat(50, F, i)=yes_11BB(i); +% x_mat(51, F, i)=yes_12BB(i); +% +% x_mat(52, F, i)=yes_9OG(i); +% x_mat(53, F, i)=yes_10OG(i); +% x_mat(54, F, i)=yes_11OG(i); +% x_mat(55, F, i)=yes_12OG(i); +% x_mat(56, F, i)=yes_9OB(i); +% x_mat(57, F, i)=yes_10OB(i); +% x_mat(58, F, i)=yes_11OB(i); +% x_mat(59, F, i)=yes_12OB(i); +% +% end +% +% +% end +% end +% +% c=1; +% c2=1; +% %key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +% %E(5)=3, %F(6)=4, %G(7)=5 +% final_boys=double.empty; +% +% for i=1:5 +% final_boys(:,c)=boys(:,c2); +% final_boys(:,c+1)=boys(:,c2+1)+boys(:,c2+2)+boys(:,c2+3); +% final_boys(:,c+2)=boys(:,c+4)+boys(c2+5); +% final_boys(:,c+3)=boys(:,c2+6); +% c2=c2+7; +% c=c+4; +% end +% +% c=1; +% c2=1; +% %key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +% %E(5)=3, %F(6)=4, %G(7)=5 +% final_girls=double.empty; +% for i=1:5 +% final_girls(:,c)=girls(:,c2); +% final_girls(:,c+1)=girls(:,c2+1)+girls(:,c2+2)+girls(:,c2+3); +% final_girls(:,c+2)=girls(:,c2+4)+girls(:,c2+5); +% final_girls(:,c+3)=girls(:,c2+6); +% c2=c2+7; +% c=c+4; +% end +% +% c=1; +% c2=1; +% %key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +% %E(5)=3, %F(6)=4, %G(7)=5 +% final_total=double.empty; +% +% for i=1:5 +% final_total(:,c)=total(:,c2); +% final_total(:,c+1)=total(:,c2+1)+total(:,c2+2)+total(:,c2+3); +% final_total(:,c+2)=total(:,c2+4)+total(:,c2+5); +% final_total(:,c+3)=total(:,c2+6); +% c2=c2+7; +% c=c+4; +% end +% +% %%confidence interval +% +% lower_mat=double.empty; +% upper_mat=double.empty; +% plot_mat=double.empty; +% z=1.96; +% for i=1:59 +% count=2; +% for j=1:r +% n=n_mat(i,j); +% for k=1:4 +% x=x_mat(i,k, j); %x_mat=zeros(59,5,r); +% p=x/n; %x is the number of subjects saying "yes", n is the total subjects +% upper=((p+z*sqrt(p*(1-p)/n))*100); +% lower=((p-z*sqrt(p*(1-p)/n))*100); +% lower_mat(i,count-1)=lower; +% upper_mat(i,count-1)=upper; +% upper=sprintf('%0.1f',round(upper*10)/10); +% lower=sprintf('%0.1f',round(lower*10)/10); +% p=p*100; +% plot_mat(i,count-1)=p; +% p_num=sprintf('%0.1f', round(p*10)/10); +% conf_mat{i+1,count}=[p_num ' [' lower ', ' upper ']']; +% count=count+1; +% end +% end +% end + +%make plots! +x=2005:2:2013; +lookat=[1, 2, 3]; %, 12, 13, 14, 15, 16, 17, 18, 19]; +cmap=jet(numel(lookat)); +title_mat={'0 hours', 'less than 3 hours', '3-4 hours', '5 or more hours'}; +for j=1:4 + f2=subplot(1,4,j); + for i=1:numel(lookat); + I=lookat(i); + y=[plot_mat(I,j), plot_mat(I, j+4), plot_mat(I,j+8), plot_mat(I,j+12), plot_mat(I,j+16)]; + plot(x, y,'-', 'MarkerFaceColor', cmap(i,:)); + hold on; + end + ylim([0 75]); + xlim([2004 2014]); + hold off; + title(title_mat{j}); + set(gca, 'xtick', x); + if j>1 + set(gca, 'yticklabel', []); + %linkaxes([f1 f2],'y'); %make y axis the same + pos1=get(f1,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(1)=pos1(1) + pos2(3); %move the second so it touches the first + set (f2,'Position',pos2); + end + f1=f2; +end + + diff --git a/physical_activity/videogames_boys_girls_2013_CI_test.m b/physical_activity/videogames_boys_girls_2013_CI_test.m new file mode 100644 index 0000000..49b5b77 --- /dev/null +++ b/physical_activity/videogames_boys_girls_2013_CI_test.m @@ -0,0 +1,1061 @@ + +cd .. +cd .. +cd data +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +weight=importdata('weights-NaN.txt','\t'); +grade=importdata('grade-NaN.txt','\t'); +[r,c]=size(sex); +weight=weight(3:7,2:c); +race=race(3:7,2:c); +sex=sex(3:7,2:c); +grade=grade(3:7,2:c); +cd .. +cd results_103114 +cd cat +question_mat=importdata('Q72-cat-NaN.txt', '\t'); +[r,c]=size(question_mat); +question_mat=question_mat(:,2:c); +cd .. +cd .. +cd .. +cd programs +cd physical_activity + +conf_mat=cell(59,16); +conf_mat{1,2}='0 hours'; +conf_mat{1,3}='0.5-2 hours'; +conf_mat{1,4}='3+ hours'; + +conf_mat{1,5}='0 hours'; +conf_mat{1,6}='0.5-2 hours'; +conf_mat{1,7}='3+ hours'; + +conf_mat{1,8}='0 hours'; +conf_mat{1,9}='0.5-2 hours'; +conf_mat{1,10}='3+ hours'; + +conf_mat{1,11}='0 hours'; +conf_mat{1,12}='0.5-2 hours'; +conf_mat{1,13}='3+ hours'; + +conf_mat{1,14}='0 hours'; +conf_mat{1,15}='0.5-1 hours'; +conf_mat{1,16}='3+ hours'; + +conf_mat{2,1}='total'; +conf_mat{3,1}='girls'; +conf_mat{4,1}='boys'; +conf_mat{5,1}='Wg'; +conf_mat{6,1}='Wb'; +conf_mat{7,1}='Bg'; +conf_mat{8,1}='Bb'; +conf_mat{9,1}='Hg'; +conf_mat{10,1}='Hb'; +conf_mat{11,1}='Og'; +conf_mat{12,1}='Ob'; +conf_mat{13,1}='W'; +conf_mat{14,1}='B'; +conf_mat{15,1}='H'; +conf_mat{16,1}='O'; +conf_mat{17,1}='9'; +conf_mat{18,1}='10'; +conf_mat{19,1}='11'; +conf_mat{20,1}='12'; +conf_mat{21,1}='9g'; +conf_mat{22,1}='9b'; +conf_mat{23,1}='10g'; +conf_mat{24,1}='10b'; +conf_mat{25,1}='11g'; +conf_mat{26,1}='11b'; +conf_mat{27,1}='12g'; +conf_mat{28,1}='12b'; + +conf_mat{29,1}='H9g'; +conf_mat{30,1}='H10g'; +conf_mat{31,1}='H11g'; +conf_mat{32,1}='H12g'; +conf_mat{33,1}='H9b'; +conf_mat{34,1}='H10b'; +conf_mat{35,1}='H11b'; +conf_mat{36,1}='H12b'; +conf_mat{37,1}='W9g'; +conf_mat{38,1}='W10g'; +conf_mat{39,1}='W11g'; +conf_mat{40,1}='W12g'; +conf_mat{41,1}='W9b'; +conf_mat{42,1}='W10b'; +conf_mat{43,1}='W11b'; +conf_mat{44,1}='W12b'; + +conf_mat{45,1}='B9g'; +conf_mat{46,1}='B10g'; +conf_mat{47,1}='B11g'; +conf_mat{48,1}='B12g'; +conf_mat{49,1}='B9b'; +conf_mat{50,1}='B10b'; +conf_mat{51,1}='B11b'; +conf_mat{52,1}='B12b'; +conf_mat{53,1}='O9g'; +conf_mat{54,1}='O10g'; +conf_mat{55,1}='O11g'; +conf_mat{56,1}='O12g'; +conf_mat{57,1}='O9b'; +conf_mat{58,1}='O10b'; +conf_mat{59,1}='O11b'; +conf_mat{60,1}='O12b'; + +n_mat=zeros(59,r); +n_mat_=zeros(59,r); +x_mat=zeros(59,4,r); + +boys=double.empty; +girls=double.empty; +total=double.empty; +c=1; + + +%key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +%E(5)=3, %F(6)=4, %G(7)=5 +for i=1:r +% total(i)=TOTAL(i,1); + for j=1:7 + n=zeros(59,1); + x=zeros(59,3); + + index_yes{i}=find(question_mat(i,:)==j); + index_girls{i}=find(sex(i,:)==1); + index_boys{i}=find(sex(i,:)==2); + index_W{i}=find(race(i,:)== 1 ); + index_B{i}=find(race(i,:)== 2 ); + index_H{i}=find(race(i,:)== 3 ); + index_O{i}=find(race(i,:)== 4 ); + index_9{i}=find(grade(i,:)== 1 ); + index_10{i}=find(grade(i,:)== 2 ); + index_11{i}=find(grade(i,:)== 3 ); + index_12{i}=find(grade(i,:)== 4 ); + + index_W9{i}=intersect(index_9{i},index_W{i}); + index_W10{i}=intersect(index_10{i},index_W{i}); + index_W11{i}=intersect(index_11{i},index_W{i}); + index_W12{i}=intersect(index_12{i},index_W{i}); + + index_B9{i}=intersect(index_9{i},index_B{i}); + index_B10{i}=intersect(index_10{i},index_B{i}); + index_B11{i}=intersect(index_11{i},index_B{i}); + index_B12{i}=intersect(index_12{i},index_B{i}); + + index_H9{i}=intersect(index_9{i},index_H{i}); + index_H10{i}=intersect(index_10{i},index_H{i}); + index_H11{i}=intersect(index_11{i},index_H{i}); + index_H12{i}=intersect(index_12{i},index_H{i}); + + index_O9{i}=intersect(index_9{i},index_O{i}); + index_O10{i}=intersect(index_10{i},index_O{i}); + index_O11{i}=intersect(index_11{i},index_O{i}); + index_O12{i}=intersect(index_12{i},index_O{i}); + + index_missQ{i}=find(question_mat(i,:)== 0); %students who didn't answer the Q + index_nomiss{i}=find(question_mat(i,:)>0); %answers that were NOT missing (ie. 0's and 1's / no's and yes's) + index_noNaN{i}=find(isnan(question_mat(i,:))==0); + + index_total_b{i}=intersect(index_nomiss{i},index_boys{i}); %index of all boys who answered + index_total_g{i}=intersect(index_nomiss{i},index_girls{i}); %index of all girls who answered + + index_total_b_{i}=intersect(index_noNaN{i},index_boys{i}); %index of all boys who answered + index_total_g_{i}=intersect(index_noNaN{i},index_girls{i}); %index of all girls who answered + + w=weight(i,:)'; + %TOTAL FOR PREVALENCE + total_ans(i)=nansum(w(index_nomiss{i})); + total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered + total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered + total_W{i}=nansum(w(intersect(index_nomiss{i}, index_W{i}))); %total # of white students who answered + total_B{i}=nansum(w(intersect(index_nomiss{i}, index_B{i}))); %total # of black students who answered + total_H{i}=nansum(w(intersect(index_nomiss{i}, index_H{i}))); %total # of hispanic students who answered + total_O{i}=nansum(w(intersect(index_nomiss{i}, index_O{i}))); %total # of "other" students who answered + + total_w(i)=total_W{i}; + total_b(i)=total_B{i}; + total_h(i)=total_H{i}; + total_o(i)=total_O{i}; + + total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i}))); + total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i}))); + total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i}))); + total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i}))); + total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i}))); + total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i}))); + total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i}))); + total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i}))); + total_9(i)=nansum(w(intersect((index_9{i}),index_nomiss{i}))); + total_10(i)=nansum(w(intersect((index_10{i}),index_nomiss{i}))); + total_11(i)=nansum(w(intersect((index_11{i}),index_nomiss{i}))); + total_12(i)=nansum(w(intersect((index_12{i}),index_nomiss{i}))); + + total_9G(i)=nansum(w(intersect(index_9{i},index_total_g{i}))); + total_10G(i)=nansum(w(intersect(index_10{i},index_total_g{i}))); + total_11G(i)=nansum(w(intersect(index_11{i},index_total_g{i}))); + total_12G(i)=nansum(w(intersect(index_12{i},index_total_g{i}))); + + total_9B(i)=nansum(w(intersect(index_9{i},index_total_b{i}))); + total_10B(i)=nansum(w(intersect(index_10{i},index_total_b{i}))); + total_11B(i)=nansum(w(intersect(index_11{i},index_total_b{i}))); + total_12B(i)=nansum(w(intersect(index_12{i},index_total_b{i}))); + + total_9G_W(i)=nansum(w(intersect(index_W9{i},index_total_g{i}))); + total_9B_W(i)=nansum(w(intersect(index_W9{i},index_total_b{i}))); + total_9G_B(i)=nansum(w(intersect(index_B9{i},index_total_g{i}))); + total_9B_B(i)=nansum(w(intersect(index_B9{i},index_total_b{i}))); + + total_9G_H(i)=nansum(w(intersect(index_H9{i},index_total_g{i}))); + total_9B_H(i)=nansum(w(intersect(index_H9{i},index_total_b{i}))); + total_9G_O(i)=nansum(w(intersect(index_O9{i},index_total_g{i}))); + total_9B_O(i)=nansum(w(intersect(index_O9{i},index_total_b{i}))); + + total_10G_W(i)=nansum(w(intersect(index_W10{i},index_total_g{i}))); + total_10B_W(i)=nansum(w(intersect(index_W10{i},index_total_b{i}))); + total_10G_B(i)=nansum(w(intersect(index_B10{i},index_total_g{i}))); + total_10B_B(i)=nansum(w(intersect(index_B10{i}, index_total_b{i}))); + + total_10G_H(i)=nansum(w(intersect(index_H10{i},index_total_g{i}))); + total_10B_H(i)=nansum(w(intersect(index_H10{i},index_total_b{i}))); + total_10G_O(i)=nansum(w(intersect(index_O10{i},index_total_g{i}))); + total_10B_O(i)=nansum(w(intersect(index_O10{i},index_total_b{i}))); + + total_11G_W(i)=nansum(w(intersect(index_W11{i},index_total_g{i}))); + total_11B_W(i)=nansum(w(intersect(index_W11{i},index_total_b{i}))); + total_11G_B(i)=nansum(w(intersect(index_B11{i},index_total_g{i}))); + total_11B_B(i)=nansum(w(intersect(index_B11{i},index_total_b{i}))); + + total_11G_H(i)=nansum(w(intersect(index_H11{i},index_total_g{i}))); + total_11B_H(i)=nansum(w(intersect(index_H11{i},index_total_b{i}))); + total_11G_O(i)=nansum(w(intersect(index_O11{i},index_total_g{i}))); + total_11B_O(i)=nansum(w(intersect(index_O11{i},index_total_b{i}))); + + total_12G_W(i)=nansum(w(intersect(index_W12{i},index_total_g{i}))); + total_12B_W(i)=nansum(w(intersect(index_W12{i},index_total_b{i}))); + total_12G_B(i)=nansum(w(intersect(index_B12{i},index_total_g{i}))); + total_12B_B(i)=nansum(w(intersect(index_B12{i},index_total_b{i}))); + + total_12G_H(i)=nansum(w(intersect(index_H12{i},index_total_g{i}))); + total_12B_H(i)=nansum(w(intersect(index_H12{i},index_total_b{i}))); + total_12G_O(i)=nansum(w(intersect(index_O12{i},index_total_g{i}))); + total_12B_O(i)=nansum(w(intersect(index_O12{i},index_total_b{i}))); + + total_9_W(i)=nansum(w(intersect(index_nomiss{i},index_W9{i}))); + total_9_B(i)=nansum(w(intersect(index_nomiss{i},index_B9{i}))); + total_9_H(i)=nansum(w(intersect(index_nomiss{i},index_H9{i}))); + total_9_O(i)=nansum(w(intersect(index_nomiss{i},index_O9{i}))); + + total_10_W(i)=nansum(w(intersect(index_nomiss{i},index_W10{i}))); + total_10_B(i)=nansum(w(intersect(index_nomiss{i},index_B10{i}))); + total_10_H(i)=nansum(w(intersect(index_nomiss{i},index_H10{i}))); + total_10_O(i)=nansum(w(intersect(index_nomiss{i},index_O10{i}))); + + total_11_W(i)=nansum(w(intersect(index_nomiss{i},index_W11{i}))); + total_11_B(i)=nansum(w(intersect(index_nomiss{i},index_B11{i}))); + total_11_H(i)=nansum(w(intersect(index_nomiss{i},index_H11{i}))); + total_11_O(i)=nansum(w(intersect(index_nomiss{i},index_O11{i}))); + + total_12_W(i)=nansum(w(intersect(index_nomiss{i},index_W12{i}))); + total_12_B(i)=nansum(w(intersect(index_nomiss{i},index_B12{i}))); + total_12_H(i)=nansum(w(intersect(index_nomiss{i},index_H12{i}))); + total_12_O(i)=nansum(w(intersect(index_nomiss{i},index_O12{i}))); + + %TOTAL FOR CI + total_ans_(i)=nansum(w(index_noNaN{i})); + total_girls_(i)=nansum(w(index_total_g_{i})); %total # of girls who answered + total_boys_(i)=nansum(w(index_total_b_{i})); %total number of boys who answered + total_W{i}=nansum(w(intersect(index_noNaN{i}, index_W{i}))); %total # of white students who answered + total_B{i}=nansum(w(intersect(index_noNaN{i}, index_B{i}))); %total # of black students who answered + total_H{i}=nansum(w(intersect(index_noNaN{i}, index_H{i}))); %total # of hispanic students who answered + total_O{i}=nansum(w(intersect(index_noNaN{i}, index_O{i}))); %total # of "other" students who answered + + total_w_(i)=total_W{i}; + total_b_(i)=total_B{i}; + total_h_(i)=total_H{i}; + total_o_(i)=total_O{i}; + + total_Wb_(i)=nansum(w(intersect(index_total_b_{i},index_W{i}))); + total_Wg_(i)=nansum(w(intersect(index_total_g_{i},index_W{i}))); + total_Bb_(i)=nansum(w(intersect(index_total_b_{i},index_B{i}))); + total_Bg_(i)=nansum(w(intersect(index_total_g_{i},index_B{i}))); + total_Hb_(i)=nansum(w(intersect(index_total_b_{i},index_H{i}))); + total_Hg_(i)=nansum(w(intersect(index_total_g_{i},index_H{i}))); + total_Ob_(i)=nansum(w(intersect(index_total_b_{i},index_O{i}))); + total_Og_(i)=nansum(w(intersect(index_total_g_{i},index_O{i}))); + total_9_(i)=nansum(w(intersect((index_9{i}),index_noNaN{i}))); + total_10_(i)=nansum(w(intersect((index_10{i}),index_noNaN{i}))); + total_11_(i)=nansum(w(intersect((index_11{i}),index_noNaN{i}))); + total_12_(i)=nansum(w(intersect((index_12{i}),index_noNaN{i}))); + + total_9G_(i)=nansum(w(intersect(index_9{i},index_total_g_{i}))); + total_10G_(i)=nansum(w(intersect(index_10{i},index_total_g_{i}))); + total_11G_(i)=nansum(w(intersect(index_11{i},index_total_g_{i}))); + total_12G_(i)=nansum(w(intersect(index_12{i},index_total_g_{i}))); + + total_9B_(i)=nansum(w(intersect(index_9{i},index_total_b_{i}))); + total_10B_(i)=nansum(w(intersect(index_10{i},index_total_b_{i}))); + total_11B_(i)=nansum(w(intersect(index_11{i},index_total_b_{i}))); + total_12B_(i)=nansum(w(intersect(index_12{i},index_total_b_{i}))); + + total_9G_W_(i)=nansum(w(intersect(index_W9{i},index_total_g_{i}))); + total_9B_W_(i)=nansum(w(intersect(index_W9{i},index_total_b_{i}))); + total_9G_B_(i)=nansum(w(intersect(index_B9{i},index_total_g_{i}))); + total_9B_B_(i)=nansum(w(intersect(index_B9{i},index_total_b_{i}))); + + total_9G_H_(i)=nansum(w(intersect(index_H9{i},index_total_g_{i}))); + total_9B_H_(i)=nansum(w(intersect(index_H9{i},index_total_b_{i}))); + total_9G_O_(i)=nansum(w(intersect(index_O9{i},index_total_g_{i}))); + total_9B_O_(i)=nansum(w(intersect(index_O9{i},index_total_b_{i}))); + + total_10G_W_(i)=nansum(w(intersect(index_W10{i},index_total_g_{i}))); + total_10B_W_(i)=nansum(w(intersect(index_W10{i},index_total_b_{i}))); + total_10G_B_(i)=nansum(w(intersect(index_B10{i},index_total_g_{i}))); + total_10B_B_(i)=nansum(w(intersect(index_B10{i}, index_total_b_{i}))); + + total_10G_H_(i)=nansum(w(intersect(index_H10{i},index_total_g_{i}))); + total_10B_H_(i)=nansum(w(intersect(index_H10{i},index_total_b_{i}))); + total_10G_O_(i)=nansum(w(intersect(index_O10{i},index_total_g_{i}))); + total_10B_O_(i)=nansum(w(intersect(index_O10{i},index_total_b_{i}))); + + total_11G_W_(i)=nansum(w(intersect(index_W11{i},index_total_g_{i}))); + total_11B_W_(i)=nansum(w(intersect(index_W11{i},index_total_b_{i}))); + total_11G_B_(i)=nansum(w(intersect(index_B11{i},index_total_g_{i}))); + total_11B_B_(i)=nansum(w(intersect(index_B11{i},index_total_b_{i}))); + + total_11G_H_(i)=nansum(w(intersect(index_H11{i},index_total_g_{i}))); + total_11B_H_(i)=nansum(w(intersect(index_H11{i},index_total_b_{i}))); + total_11G_O_(i)=nansum(w(intersect(index_O11{i},index_total_g_{i}))); + total_11B_O_(i)=nansum(w(intersect(index_O11{i},index_total_b_{i}))); + + total_12G_W_(i)=nansum(w(intersect(index_W12{i},index_total_g_{i}))); + total_12B_W_(i)=nansum(w(intersect(index_W12{i},index_total_b_{i}))); + total_12G_B_(i)=nansum(w(intersect(index_B12{i},index_total_g_{i}))); + total_12B_B_(i)=nansum(w(intersect(index_B12{i},index_total_b_{i}))); + + total_12G_H_(i)=nansum(w(intersect(index_H12{i},index_total_g_{i}))); + total_12B_H_(i)=nansum(w(intersect(index_H12{i},index_total_b_{i}))); + total_12G_O_(i)=nansum(w(intersect(index_O12{i},index_total_g_{i}))); + total_12B_O_(i)=nansum(w(intersect(index_O12{i},index_total_b_{i}))); + + total_9_W_(i)=nansum(w(intersect(index_noNaN{i},index_W9{i}))); + total_9_B_(i)=nansum(w(intersect(index_noNaN{i},index_B9{i}))); + total_9_H_(i)=nansum(w(intersect(index_noNaN{i},index_H9{i}))); + total_9_O_(i)=nansum(w(intersect(index_noNaN{i},index_O9{i}))); + + total_10_W_(i)=nansum(w(intersect(index_noNaN{i},index_W10{i}))); + total_10_B_(i)=nansum(w(intersect(index_noNaN{i},index_B10{i}))); + total_10_H_(i)=nansum(w(intersect(index_noNaN{i},index_H10{i}))); + total_10_O_(i)=nansum(w(intersect(index_noNaN{i},index_O10{i}))); + + total_11_W_(i)=nansum(w(intersect(index_noNaN{i},index_W11{i}))); + total_11_B_(i)=nansum(w(intersect(index_noNaN{i},index_B11{i}))); + total_11_H_(i)=nansum(w(intersect(index_noNaN{i},index_H11{i}))); + total_11_O_(i)=nansum(w(intersect(index_noNaN{i},index_O11{i}))); + + total_12_W_(i)=nansum(w(intersect(index_noNaN{i},index_W12{i}))); + total_12_B_(i)=nansum(w(intersect(index_noNaN{i},index_B12{i}))); + total_12_H_(i)=nansum(w(intersect(index_noNaN{i},index_H12{i}))); + total_12_O_(i)=nansum(w(intersect(index_noNaN{i},index_O12{i}))); + + w=weight(i,:)'; + index_yesgirls{i}=intersect(index_yes{i},index_girls{i}); + index_yesboys{i}=intersect(index_yes{i},index_boys{i}); + yes_girls(i)=nansum(w(index_yesgirls{i})); + yes_boys(i)=nansum(w(index_yesboys{i})); + total_yes(i)=nansum(w(index_yes{i})); + + yes_W(i)=nansum(w(intersect(index_yes{i}, index_W{i}))); + yes_B(i)=nansum(w(intersect(index_yes{i}, index_B{i}))); + yes_H(i)=nansum(w(intersect(index_yes{i}, index_H{i}))); + yes_O(i)=nansum(w(intersect(index_yes{i}, index_O{i}))); + yes_WG(i)=nansum(w(intersect(index_yesgirls{i},index_W{i}))); + yes_BG(i)=nansum(w(intersect(index_yesgirls{i},index_B{i}))); + yes_HG(i)=nansum(w(intersect(index_yesgirls{i},index_H{i}))); + yes_OG(i)=nansum(w(intersect(index_yesgirls{i},index_O{i}))); + yes_WB(i)=nansum(w(intersect(index_yesboys{i},index_W{i}))); + yes_BB(i)=nansum(w(intersect(index_yesboys{i},index_B{i}))); + yes_HB(i)=nansum(w(intersect(index_yesboys{i},index_H{i}))); + yes_OB(i)=nansum(w(intersect(index_yesboys{i},index_O{i}))); + yes_9(i)=nansum(w(intersect(index_yes{i},index_9{i}))); + yes_10(i)=nansum(w(intersect(index_yes{i},index_10{i}))); + yes_11(i)=nansum(w(intersect(index_yes{i},index_11{i}))); + yes_12(i)=nansum(w(intersect(index_yes{i},index_12{i}))); + yes_9b(i)=nansum(w(intersect(index_yesboys{i},index_9{i}))); + yes_10b(i)=nansum(w(intersect(index_yesboys{i},index_10{i}))); + yes_11b(i)=nansum(w(intersect(index_yesboys{i},index_11{i}))); + yes_12b(i)=nansum(w(intersect(index_yesboys{i},index_12{i}))); + yes_9g(i)=nansum(w(intersect(index_yesgirls{i},index_9{i}))); + yes_10g(i)=nansum(w(intersect(index_yesgirls{i},index_10{i}))); + yes_11g(i)=nansum(w(intersect(index_yesgirls{i},index_11{i}))); + yes_12g(i)=nansum(w(intersect(index_yesgirls{i},index_12{i}))); + + + yes_9WB(i)=nansum(w(intersect(index_yesboys{i},index_W9{i}))); + yes_10WB(i)=nansum(w(intersect(index_yesboys{i},index_W10{i}))); + yes_11WB(i)=nansum(w(intersect(index_yesboys{i},index_W11{i}))); + yes_12WB(i)=nansum(w(intersect(index_yesboys{i},index_W12{i}))); + yes_9WG(i)=nansum(w(intersect(index_yesgirls{i},index_W9{i}))); + yes_10WG(i)=nansum(w(intersect(index_yesgirls{i},index_W10{i}))); + yes_11WG(i)=nansum(w(intersect(index_yesgirls{i},index_W11{i}))); + yes_12WG(i)=nansum(w(intersect(index_yesgirls{i},index_W12{i}))); + + yes_9W(i)=nansum(w(intersect(index_yes{i},index_W9{i}))); + yes_10W(i)=nansum(w(intersect(index_yes{i},(index_W10{i})))); + yes_11W(i)=nansum(w(intersect(index_yes{i},(index_W11{i})))); + yes_12W(i)=nansum(w(intersect(index_yes{i},(index_W12{i})))); + + yes_9B(i)=nansum(w(intersect(index_yes{i},(index_B9{i})))); + yes_10B(i)=nansum(w(intersect(index_yes{i},(index_B10{i})))); + yes_11B(i)=nansum(w(intersect(index_yes{i},(index_B11{i})))); + yes_12B(i)=nansum(w(intersect(index_yes{i},(index_B12{i})))); + + yes_9H(i)=nansum(w(intersect(index_yes{i},(index_H9{i})))); + yes_10H(i)=nansum(w(intersect(index_yes{i},(index_H10{i})))); + yes_11H(i)=nansum(w(intersect(index_yes{i},(index_H11{i})))); + yes_12H(i)=nansum(w(intersect(index_yes{i},(index_H12{i})))); + + yes_9O(i)=nansum(w(intersect(index_yes{i},(index_O9{i})))); + yes_10O(i)=nansum(w(intersect(index_yes{i},(index_O10{i})))); + yes_11O(i)=nansum(w(intersect(index_yes{i},(index_O11{i})))); + yes_12O(i)=nansum(w(intersect(index_yes{i},(index_O12{i})))); + + yes_9BB(i)=nansum(w(intersect(index_yesboys{i},index_B9{i}))); + yes_10BB(i)=nansum(w(intersect(index_yesboys{i},index_B10{i}))); + yes_11BB(i)=nansum(w(intersect(index_yesboys{i},index_B11{i}))); + yes_12BB(i)=nansum(w(intersect(index_yesboys{i},index_B12{i}))); + yes_9BG(i)=nansum(w(intersect(index_yesgirls{i},index_B9{i}))); + yes_10BG(i)=nansum(w(intersect(index_yesgirls{i},index_B10{i}))); + yes_11BG(i)=nansum(w(intersect(index_yesgirls{i},index_B11{i}))); + yes_12BG(i)=nansum(w(intersect(index_yesgirls{i},index_B12{i}))); + + yes_9HB(i)=nansum(w(intersect(index_yesboys{i},index_H9{i}))); + yes_10HB(i)=nansum(w(intersect(index_yesboys{i},index_H10{i}))); + yes_11HB(i)=nansum(w(intersect(index_yesboys{i},index_H11{i}))); + yes_12HB(i)=nansum(w(intersect(index_yesboys{i},index_H12{i}))); + yes_9HG(i)=nansum(w(intersect(index_yesgirls{i},index_H9{i}))); + yes_10HG(i)=nansum(w(intersect(index_yesgirls{i},index_H10{i}))); + yes_11HG(i)=nansum(w(intersect(index_yesgirls{i},index_H11{i}))); + yes_12HG(i)=nansum(w(intersect(index_yesgirls{i},index_H12{i}))); + + yes_9OB(i)=nansum(w(intersect(index_yesboys{i},index_O9{i}))); + yes_10OB(i)=nansum(w(intersect(index_yesboys{i},index_O10{i}))); + yes_11OB(i)=nansum(w(intersect(index_yesboys{i},index_O11{i}))); + yes_12OB(i)=nansum(w(intersect(index_yesboys{i},index_O12{i}))); + yes_9OG(i)=nansum(w(intersect(index_yesgirls{i},index_O9{i}))); + yes_10OG(i)=nansum(w(intersect(index_yesgirls{i},index_O10{i}))); + yes_11OG(i)=nansum(w(intersect(index_yesgirls{i},index_O11{i}))); + yes_12OG(i)=nansum(w(intersect(index_yesgirls{i},index_O12{i}))); + + girls(1, c)=yes_girls(i)/total_girls(i)*100; %girls + girls(2, c)=yes_WG(i)/total_Wg(i)*100; %WG + girls(3, c)=yes_BG(i)/total_Bg(i)*100; %BG + girls(4, c)=yes_HG(i)/total_Hg(i)*100; %HG + girls(5, c)=yes_OG(i)/total_Og(i)*100; %OG + girls(6, c)=yes_9g(i)/total_9G(i)*100; + girls(7, c)=yes_10g(i)/total_10G(i)*100; + girls(8, c)=yes_11g(i)/total_11G(i)*100; + girls(9, c)=yes_12g(i)/total_12G(i)*100; + girls(10, c)=yes_9WG(i)/total_9G_W(i)*100; + girls(11, c)=yes_10WG(i)/total_10G_W(i)*100; + girls(12, c)=yes_11WG(i)/total_11G_W(i)*100; + girls(13, c)=yes_12WG(i)/total_12G_W(i)*100; + girls(14, c)=yes_9BG(i)/total_9G_B(i)*100; + girls(15, c)=yes_10BG(i)/total_10G_B(i)*100; + girls(16, c)=yes_11BG(i)/total_11G_B(i)*100; + girls(17, c)=yes_12BG(i)/total_12G_B(i)*100; + girls(18, c)=yes_9HG(i)/total_9G_H(i)*100; + girls(19, c)=yes_10HG(i)/total_10G_H(i)*100; + girls(20, c)=yes_11HG(i)/total_11G_H(i)*100; + girls(21, c)=yes_12HG(i)/total_12G_H(i)*100; + girls(22, c)=yes_9OG(i)/total_9G_O(i)*100; + girls(23, c)=yes_10OG(i)/total_10G_O(i)*100; + girls(24, c)=yes_11OG(i)/total_11G_O(i)*100; + girls(25, c)=yes_12OG(i)/total_12G_O(i)*100; + + boys(1, c)=yes_boys(i)/total_boys(i)*100; %boys + boys(2, c)=yes_WB(i)/total_Wb(i)*100; %WB + boys (3, c)=yes_BB(i)/total_Bb(i)*100; %BB + boys(4, c)=yes_HB(i)/total_Hb(i)*100; %HB + boys(5, c)=yes_OB(i)/total_Ob(i)*100; %OB + boys(6, c)=yes_9b(i)/total_9B(i)*100; + boys(7, c)=yes_10b(i)/total_10B(i)*100; + boys(8, c)=yes_11b(i)/total_11B(i)*100; + boys(9, c)=yes_12b(i)/total_12B(i)*100; + boys(10, c)=yes_9WB(i)/total_9B_W(i)*100; + boys(11, c)=yes_10WB(i)/total_10B_W(i)*100; + boys(12, c)=yes_11WB(i)/total_11B_W(i)*100; + boys(13, c)=yes_12WB(i)/total_12B_W(i)*100; + boys(14, c)=yes_9BB(i)/total_9B_B(i)*100; + boys(15, c)=yes_10BB(i)/total_10B_B(i)*100; + boys(16, c)=yes_11BB(i)/total_11B_B(i)*100; + boys(17, c)=yes_12BB(i)/total_12B_B(i)*100; + boys(18, c)=yes_9HB(i)/total_9B_H(i)*100; + boys(19, c)=yes_10HB(i)/total_10B_H(i)*100; + boys(20, c)=yes_11HB(i)/total_11B_H(i)*100; + boys(21, c)=yes_12HB(i)/total_12B_H(i)*100; + boys(22, c)=yes_9OB(i)/total_9B_O(i)*100; + boys(23, c)=yes_10OB(i)/total_10B_O(i)*100; + boys(24, c)=yes_11OB(i)/total_11B_O(i)*100; + boys(25, c)=yes_12OB(i)/total_12B_O(i)*100; + + total(1,c)=total_yes(i)/total_ans(i)*100; %total + total(2,c)=yes_boys(i)/total_boys(i)*100; %boys + total(3,c)=yes_girls(i)/total_girls(i)*100; %girls + total(4,c)=yes_W(i)/total_w(i)*100; %whites + total(5,c)=yes_B(i)/total_b(i)*100; %blacks + total(6,c)=yes_H(i)/total_h(i)*100; %hispanics + total(7,c)=yes_O(i)/total_o(i)*100; %other + total(8,c)=yes_9(i)/total_9(i)*100; + total(9,c)=yes_10(i)/total_10(i)*100; + total(10,c)=yes_11(i)/total_11(i)*100; + total(11,c)=yes_12(i)/total_12(i)*100; + total(12, c)=yes_9W(i)/total_9_W(i)*100; + total(13, c)=yes_10W(i)/total_10_W(i)*100; + total(14, c)=yes_11W(i)/total_11_W(i)*100; + total(15, c)=yes_12W(i)/total_12_W(i)*100; + total(16, c)=yes_9B(i)/total_9_B(i)*100; + total(17, c)=yes_10B(i)/total_10_B(i)*100; + total(18, c)=yes_11B(i)/total_11_B(i)*100; + total(19, c)=yes_12B(i)/total_12_B(i)*100; + total(20, c)=yes_9H(i)/total_9_H(i)*100; + total(21, c)=yes_10H(i)/total_10_H(i)*100; + total(22, c)=yes_11H(i)/total_11_H(i)*100; + total(23, c)=yes_12H(i)/total_12_H(i)*100; + total(24, c)=yes_9O(i)/total_9_O(i)*100; + total(25, c)=yes_10O(i)/total_10_O(i)*100; + total(26, c)=yes_11O(i)/total_11_O(i)*100; + total(27, c)=yes_12O(i)/total_12_O(i)*100; + + c=c+1; + + %for stats + n_mat_ (1,i)= total_ans_(i); + n_mat_ (2,i)=total_girls_(i); + n_mat_ (3,i)=total_boys_(i); + n_mat_ (4,i)=total_Wg_(i); + n_mat_ (5,i)=total_Wb_(i); + n_mat_ (6,i)=total_Bg_(i); + n_mat_ (7,i)=total_Bb_(i); + n_mat_ (8,i)=total_Hg_(i); + n_mat_ (9,i)=total_Hb_(i); + n_mat_ (10,i)=total_Og_(i); + n_mat_ (11,i)=total_Ob_(i); + n_mat_ (12,i)=total_w_(i); + n_mat_ (13,i)=total_b_(i); + n_mat_ (14,i)=total_h_(i); + n_mat_ (15,i)=total_o_(i); + n_mat_ (16,i)=total_9_(i); + n_mat_ (17,i)=total_10_(i); + n_mat_ (18,i)=total_11_(i); + n_mat_ (19,i)=total_12_(i); + n_mat_ (20,i)=total_9G_(i); + n_mat_ (21,i)=total_9B_(i); + n_mat_ (22,i)=total_10G_(i); + n_mat_ (23,i)=total_10B_(i); + n_mat_ (24,i)=total_11G_(i); + n_mat_ (25,i)=total_11B_(i); + n_mat_ (26,i)=total_12G_(i); + n_mat_ (27,i)=total_12B_(i); + + n_mat_ (28,i)=total_9G_H_(i); + n_mat_ (29,i)=total_10G_H_(i); + n_mat_ (30,i)=total_11G_H_(i); + n_mat_ (31,i)=total_12G_H_(i); + n_mat_ (32,i)=total_9B_H_(i); + n_mat_ (33,i)=total_10B_H_(i); + n_mat_ (34,i)=total_11B_H_(i); + n_mat_ (35,i)=total_12B_H_(i); + + n_mat_ (36,i)=total_9G_W_(i); + n_mat_ (37,i)=total_10G_W_(i); + n_mat_ (38,i)=total_11G_W_(i); + n_mat_ (39,i)=total_12G_W_(i); + n_mat_ (40,i)=total_9B_W_(i); + n_mat_ (41,i)=total_10B_W_(i); + n_mat_ (42,i)=total_11B_W_(i); + n_mat_ (43,i)=total_12B_W_(i); + + n_mat_ (44,i)=total_9G_B_(i); + n_mat_ (45,i)=total_10G_B_(i); + n_mat_ (46,i)=total_11G_B_(i); + n_mat_ (47,i)=total_12G_B_(i); + n_mat_ (48,i)=total_9B_B_(i); + n_mat_ (49,i)=total_10B_B_(i); + n_mat_ (50,i)=total_11B_B_(i); + n_mat_ (51,i)=total_12B_B_(i); + + n_mat_ (52,i)=total_9G_O_(i); + n_mat_ (53,i)=total_10G_O_(i); + n_mat_ (54,i)=total_11G_O_(i); + n_mat_ (55,i)=total_12G_O_(i); + n_mat_ (56,i)=total_9B_O_(i); + n_mat_ (57,i)=total_10B_O_(i); + n_mat_ (58,i)=total_11B_O_(i); + n_mat_ (59,i)=total_12B_O_(i); + + %for stats + n_mat (1,i)= total_ans(i); + n_mat (2,i)=total_girls(i); + n_mat (3,i)=total_boys(i); + n_mat (4,i)=total_Wg(i); + n_mat (5,i)=total_Wb(i); + n_mat (6,i)=total_Bg(i); + n_mat (7,i)=total_Bb(i); + n_mat (8,i)=total_Hg(i); + n_mat (9,i)=total_Hb(i); + n_mat (10,i)=total_Og(i); + n_mat (11,i)=total_Ob(i); + n_mat (12,i)=total_w(i); + n_mat (13,i)=total_b(i); + n_mat (14,i)=total_h(i); + n_mat (15,i)=total_o(i); + n_mat (16,i)=total_9(i); + n_mat (17,i)=total_10(i); + n_mat (18,i)=total_11(i); + n_mat (19,i)=total_12(i); + n_mat (20,i)=total_9G(i); + n_mat (21,i)=total_9B(i); + n_mat (22,i)=total_10G(i); + n_mat (23,i)=total_10B(i); + n_mat (24,i)=total_11G(i); + n_mat (25,i)=total_11B(i); + n_mat (26,i)=total_12G(i); + n_mat (27,i)=total_12B(i); + + n_mat (28,i)=total_9G_H(i); + n_mat (29,i)=total_10G_H(i); + n_mat (30,i)=total_11G_H(i); + n_mat (31,i)=total_12G_H(i); + n_mat (32,i)=total_9B_H(i); + n_mat (33,i)=total_10B_H(i); + n_mat (34,i)=total_11B_H(i); + n_mat (35,i)=total_12B_H(i); + + n_mat (36,i)=total_9G_W(i); + n_mat (37,i)=total_10G_W(i); + n_mat (38,i)=total_11G_W(i); + n_mat (39,i)=total_12G_W(i); + n_mat (40,i)=total_9B_W(i); + n_mat (41,i)=total_10B_W(i); + n_mat (42,i)=total_11B_W(i); + n_mat (43,i)=total_12B_W(i); + + n_mat (44,i)=total_9G_B(i); + n_mat (45,i)=total_10G_B(i); + n_mat (46,i)=total_11G_B(i); + n_mat (47,i)=total_12G_B(i); + n_mat (48,i)=total_9B_B(i); + n_mat (49,i)=total_10B_B(i); + n_mat (50,i)=total_11B_B(i); + n_mat (51,i)=total_12B_B(i); + + n_mat (52,i)=total_9G_O(i); + n_mat (53,i)=total_10G_O(i); + n_mat (54,i)=total_11G_O(i); + n_mat (55,i)=total_12G_O(i); + n_mat (56,i)=total_9B_O(i); + n_mat (57,i)=total_10B_O(i); + n_mat (58,i)=total_11B_O(i); + n_mat (59,i)=total_12B_O(i); + + if (j>1 && j<5)%0.5-1 hour hours or less + x(1,2)=total_yes(i); + x(2,2)=yes_girls(i); + x(3,2)=yes_boys(i); + x(4,2)=yes_WG(i); + x(5,2)=yes_WB(i); + x(6,2)=yes_BG(i); + x(7,2)=yes_BB(i); + x(8,2)=yes_HG(i); + x(9,2)=yes_HB(i); + x(10,2)=yes_OG(i); + x(11,2)=yes_OB(i); + x(12,2)=yes_W(i); + x(13,2)=yes_B(i); + x(14,2)=yes_H(i); + x(15,2)=yes_O(i); + x(16,2)=yes_9(i); + x(17,2)=yes_10(i); + x(18,2)=yes_11(i); + x(19,2)=yes_12(i); + x(20,2)=yes_9g(i); + x(21,2)=yes_9b(i); + x(22,2)=yes_10g(i); + x(23,2)=yes_10b(i); + x(24,2)=yes_11g(i); + x(25,2)=yes_11b(i); + x(26,2)=yes_12g(i); + x(27,2)=yes_12b(i); + + x(28,2)=yes_9HG(i); + x(29, 2)=yes_10HG(i); + x(30, 2)=yes_11HG(i); + x(31, 2)=yes_12HG(i); + x(32, 2)=yes_9HB(i); + x(33, 2)=yes_10HB(i); + x(34, 2)=yes_11HB(i); + x(35, 2)=yes_12HB(i); + + x(36, 2)=yes_9WG(i); + x(37, 2)=yes_10WG(i); + x(38, 2)=yes_11WG(i); + x(39, 2)=yes_12WG(i); + x(40, 2)=yes_9WB(i); + x(41, 2)=yes_10WB(i); + x(42, 2)=yes_11WB(i); + x(43, 2)=yes_12WB(i); + + x(44, 2)=yes_9BG(i); + x(45, 2)=yes_10BG(i); + x(46, 2)=yes_11BG(i); + x(47, 2)=yes_12BG(i); + x(48, 2)=yes_9BB(i); + x(49, 2)=yes_10BB(i); + x(50, 2)=yes_11BB(i); + x(51, 2)=yes_12BB(i); + + x(52, 2)=yes_9OG(i); + x(53, 2)=yes_10OG(i); + x(54, 2)=yes_11OG(i); + x(55, 2)=yes_12OG(i); + x(56, 2)=yes_9OB(i); + x(57, 2)=yes_10OB(i); + x(58, 2)=yes_11OB(i); + x(59, 2)=yes_12OB(i); + + x_mat(:,2, i)=x_mat(:, 2, i)+x(:,2); + elseif j>4 %3-4 hours hours or more + x(1,3)=total_yes(i); + x(2,3)=yes_girls(i); + x(3,3)=yes_boys(i); + x(4,3)=yes_WG(i); + x(5,3)=yes_WB(i); + x(6,3)=yes_BG(i); + x(7,3)=yes_BB(i); + x(8,3)=yes_HG(i); + x(9,3)=yes_HB(i); + x(10,3)=yes_OG(i); + x(11,3)=yes_OB(i); + x(12,3)=yes_W(i); + x(13,3)=yes_B(i); + x(14,3)=yes_H(i); + x(15,3)=yes_O(i); + x(16,3)=yes_9(i); + x(17,3)=yes_10(i); + x(18,3)=yes_11(i); + x(19,3)=yes_12(i); + x(20,3)=yes_9g(i); + x(21,3)=yes_9b(i); + x(22,3)=yes_10g(i); + x(23,3)=yes_10b(i); + x(24,3)=yes_11g(i); + x(25,3)=yes_11b(i); + x(26,3)=yes_12g(i); + x(27,3)=yes_12b(i); + + x(28,3)=yes_9HG(i); + x(29, 3)=yes_10HG(i); + x(30, 3)=yes_11HG(i); + x(31, 3)=yes_12HG(i); + x(32, 3)=yes_9HB(i); + x(33, 3)=yes_10HB(i); + x(34, 3)=yes_11HB(i); + x(35, 3)=yes_12HB(i); + + x(36, 3)=yes_9WG(i); + x(37, 3)=yes_10WG(i); + x(38, 3)=yes_11WG(i); + x(39, 3)=yes_12WG(i); + x(40, 3)=yes_9WB(i); + x(41, 3)=yes_10WB(i); + x(42, 3)=yes_11WB(i); + x(43, 3)=yes_12WB(i); + + x(44, 3)=yes_9BG(i); + x(45, 3)=yes_10BG(i); + x(46, 3)=yes_11BG(i); + x(47, 3)=yes_12BG(i); + x(48, 3)=yes_9BB(i); + x(49, 3)=yes_10BB(i); + x(50, 3)=yes_11BB(i); + x(51, 3)=yes_12BB(i); + + x(52, 3)=yes_9OG(i); + x(53, 3)=yes_10OG(i); + x(54, 3)=yes_11OG(i); + x(55, 3)=yes_12OG(i); + x(56, 3)=yes_9OB(i); + x(57, 3)=yes_10OB(i); + x(58, 3)=yes_11OB(i); + x(59, 3)=yes_12OB(i); + + x_mat(:,3, i)=x_mat(:,3, i)+x(:,3); + else + if j==1 + F=1; + end + x_mat(1,F, i)=total_yes(i); + x_mat(2,F, i)=yes_girls(i); + x_mat(3,F, i)=yes_boys(i); + x_mat(4,F, i)=yes_WG(i); + x_mat(5,F, i)=yes_WB(i); + x_mat(6,F, i)=yes_BG(i); + x_mat(7,F, i)=yes_BB(i); + x_mat(8,F, i)=yes_HG(i); + x_mat(9,F, i)=yes_HB(i); + x_mat(10,F, i)=yes_OG(i); + x_mat(11,F, i)=yes_OB(i); + x_mat(12,F, i)=yes_W(i); + x_mat(13,F, i)=yes_B(i); + x_mat(14,F, i)=yes_H(i); + x_mat(15,F, i)=yes_O(i); + x_mat(16,F, i)=yes_9(i); + x_mat(17,F, i)=yes_10(i); + x_mat(18,F, i)=yes_11(i); + x_mat(19,F, i)=yes_12(i); + x_mat(20,F, i)=yes_9g(i); + x_mat(21,F, i)=yes_9b(i); + x_mat(22,F, i)=yes_10g(i); + x_mat(23,F, i)=yes_10b(i); + x_mat(24,F, i)=yes_11g(i); + x_mat(25,F, i)=yes_11b(i); + x_mat(26,F, i)=yes_12g(i); + x_mat(27,F, i)=yes_12b(i); + + x_mat(28,F, i)=yes_9HG(i); + x_mat(29, F, i)=yes_10HG(i); + x_mat(30, F, i)=yes_11HG(i); + x_mat(31, F, i)=yes_12HG(i); + x_mat(32, F, i)=yes_9HB(i); + x_mat(33, F, i)=yes_10HB(i); + x_mat(34, F, i)=yes_11HB(i); + x_mat(35, F, i)=yes_12HB(i); + + x_mat(36, F, i)=yes_9WG(i); + x_mat(37, F, i)=yes_10WG(i); + x_mat(38, F, i)=yes_11WG(i); + x_mat(39, F, i)=yes_12WG(i); + x_mat(40, F, i)=yes_9WB(i); + x_mat(41, F, i)=yes_10WB(i); + x_mat(42, F, i)=yes_11WB(i); + x_mat(43, F, i)=yes_12WB(i); + + x_mat(44, F, i)=yes_9BG(i); + x_mat(45, F, i)=yes_10BG(i); + x_mat(46, F, i)=yes_11BG(i); + x_mat(47, F, i)=yes_12BG(i); + x_mat(48, F, i)=yes_9BB(i); + x_mat(49, F, i)=yes_10BB(i); + x_mat(50, F, i)=yes_11BB(i); + x_mat(51, F, i)=yes_12BB(i); + + x_mat(52, F, i)=yes_9OG(i); + x_mat(53, F, i)=yes_10OG(i); + x_mat(54, F, i)=yes_11OG(i); + x_mat(55, F, i)=yes_12OG(i); + x_mat(56, F, i)=yes_9OB(i); + x_mat(57, F, i)=yes_10OB(i); + x_mat(58, F, i)=yes_11OB(i); + x_mat(59, F, i)=yes_12OB(i); + + end + + + end +end + +c=1; +c2=1; +%key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +%E(5)=3, %F(6)=4, %G(7)=5 +final_boys=double.empty; + +for i=1:5 + final_boys(:,c)=boys(:,c2); + final_boys(:,c+1)=boys(:,c2+1)+boys(:,c2+2)+boys(:,c2+3); + final_boys(:,c+2)=boys(:,c+4)+boys(c2+5); + final_boys(:,c+3)=boys(:,c2+6); + c2=c2+7; + c=c+3; +end + +c=1; +c2=1; +%key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +%E(5)=3, %F(6)=4, %G(7)=5 +final_girls=double.empty; +for i=1:5 + final_girls(:,c)=girls(:,c2); + final_girls(:,c+1)=girls(:,c2+1)+girls(:,c2+2)+girls(:,c2+3); + final_girls(:,c+2)=girls(:,c2+4)+girls(:,c2+5); + final_girls(:,c+3)=girls(:,c2+6); + c2=c2+7; + c=c+3; +end + +c=1; +c2=1; +%key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +%E(5)=3, %F(6)=4, %G(7)=5 +final_total=double.empty; + +for i=1:5 + final_total(:,c)=total(:,c2); + final_total(:,c+1)=total(:,c2+1)+total(:,c2+2)+total(:,c2+3); + final_total(:,c+2)=total(:,c2+4)+total(:,c2+5); + final_total(:,c+3)=total(:,c2+6); + c2=c2+7; + c=c+3; +end + +%%confidence interval + +lower_mat=double.empty; +upper_mat=double.empty; +plot_mat=double.empty; +z=1.96; +for i=1:59 + count=2; + for j=1:r + n=n_mat_(i,j); + N=n_mat(i,j); %total without missing + for k=1:3 + x=x_mat(i,k, j); %x_mat=zeros(59,5,r); + p=x/n; %x is the number of subjects saying "yes", n is the total subjects + P=x/N; + upper=((P+z*sqrt(p*(1-p)/n))*100); + lower=((P-z*sqrt(p*(1-p)/n))*100); + lower_mat(i,count-1)=lower; + upper_mat(i,count-1)=upper; + upper=sprintf('%0.1f',round(upper*10)/10); + lower=sprintf('%0.1f',round(lower*10)/10); + p=p*100; + P=P*100; + plot_mat(i,count-1)=P; + p_num=sprintf('%0.1f', round(P*10)/10); + conf_mat{i+1,count}=[p_num ' [' lower ', ' upper ']']; + count=count+1; + end + end +end + +%make plots! +x=2005:2:2013; +lookat=[1, 2, 3; 14, 6, 7; 15, 8, 9; 16, 10, 11]; %, 12, 13, 14, 15, 16, 17, 18, 19]; +[r,c]=size(lookat); +cmap=jet(c); +title_mat={'0 hours', '3 hours or less', '3+ hours'}; +ylabel_mat={'Total', 'White', 'Black', 'Hispanic'}; +count=1; +for k=1:4 %by race + for j=1:3 + f2=subplot(4,3,count); + for i=1:c + I=lookat(k,i); + %CI + x2=[2005 2005]; + y2=[lower_mat(I,j) upper_mat(I,j)]; + plot(x2, y2, '-k'); + hold on + x2=[2007 2007]; + y2=[lower_mat(I,j+3) upper_mat(I,j+3)]; + plot(x2, y2, '-k'); + hold on + x2=[2009 2009]; + y2=[lower_mat(I,j+6) upper_mat(I,j+6)]; + plot(x2, y2, '-k'); + hold on + x2=[2011 2011]; + y2=[lower_mat(I,j+9) upper_mat(I,j+9)]; + plot(x2, y2, '-k'); + hold on + x2=[2013 2013]; + y2=[lower_mat(I,j+12) upper_mat(I,j+12)]; + plot(x2, y2, '-k'); + hold on + y=[plot_mat(I,j), plot_mat(I, j+3), plot_mat(I,j+6), plot_mat(I,j+9),plot_mat(I,j+12)]; + plot(x, y,'-', 'Color', cmap(i,:)); %, 'MarkerFaceColor', cmap(i,:)'MarkerEdgeColor', cmap(i,:), 'MarkerSize', 3, + end + ylim([0 65]); + xlim([2004 2014]); + hold off; + set(gca, 'xtick', x); + set(gca, 'xticklabel', []); + + if count<4 + title(title_mat{j}); + if count==1 + pos_1=get(f2, 'Position'); + F1=f2; + elseif count==2 + set(gca, 'yticklabel', []); + linkaxes([f1 f2],'y'); %make y axis the same + pos1=get(f1,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(1)=pos1(1) + pos2(3); %move the second so it touches the first + set (f2,'Position',pos2); + pos_2=get(f2, 'Position'); + F2=f2; + else + set(gca, 'yticklabel', []); + linkaxes([f1 f2],'y'); %make y axis the same + pos1=get(f1,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(1)=pos1(1) + pos2(3); %move the second so it touches the first + set (f2,'Position',pos2); + pos_3=get(f2, 'Position'); + F3=f2; + end + else + if (count==4 || count==7 || count==10) %not the first in the row + ylabel( ylabel_mat{k}); + linkaxes([F1 f2],'x'); %make y axis the same + pos1=get(F1,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(2)=pos1(2) - pos2(4); %move the second so it touches the first + set (f2,'Position',pos2); + F1=f2; + elseif(count==5 || count==8 || count==11) + set(gca, 'yticklabel', []); + linkaxes([F2 f2],'x'); %make y axis the same + pos1=get(F2,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(2)=pos1(2) - pos2(4); %move the second so it touches the first + set (f2,'Position',pos2); + + linkaxes([f1 f2],'y'); %make y axis the same + pos1=get(f1,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(1)=pos1(1) + pos2(3); %move the second so it touches the first + set (f2,'Position',pos2); + F2=f2; + elseif (count==6 || count==9 || count==12) + set(gca, 'yticklabel', []); + linkaxes([F3 f2],'x'); %make y axis the same + pos1=get(F3,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(2)=pos1(2) - pos2(4); %move the second so it touches the first + set (f2,'Position',pos2); + + linkaxes([f1 f2],'y'); %make y axis the same + pos1=get(f1,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(1)=pos1(1) + pos2(3); %move the second so it touches the first + set (f2,'Position',pos2); + pos_2=get(f2, 'Position'); + set (f2,'Position',pos2); + F3=f2; + end + end + f1=f2; + count=count+1; + end +end +%legend ('All', 'Girls', 'Boys', 'Location', 'SouthOutside'); +%all yellow, girls red, boys blue + diff --git a/physical_activity/videogames_boys_girls_2013_CIv2.m b/physical_activity/videogames_boys_girls_2013_CIv2.m new file mode 100644 index 0000000..189381d --- /dev/null +++ b/physical_activity/videogames_boys_girls_2013_CIv2.m @@ -0,0 +1,989 @@ + +cd .. +cd .. +cd data +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +weight=importdata('weights-NaN.txt','\t'); +grade=importdata('grade-NaN.txt','\t'); +[r,c]=size(sex); +weight=weight(3:7,2:c); +race=race(3:7,2:c); +sex=sex(3:7,2:c); +grade=grade(3:7,2:c); +cd .. +cd results_103114 +cd cat +question_mat=importdata('Q72-cat-NaN.txt', '\t'); +[r,c]=size(question_mat); +question_mat=question_mat(:,2:c); +cd .. +cd .. +cd .. +cd programs +cd physical_activity + +conf_mat=cell(59,16); +conf_mat{1,2}='0 hours'; +conf_mat{1,3}='0.5-2 hours'; +conf_mat{1,4}='3+ hours'; + +conf_mat{1,5}='0 hours'; +conf_mat{1,6}='0.5-2 hours'; +conf_mat{1,7}='3+ hours'; + +conf_mat{1,8}='0 hours'; +conf_mat{1,9}='0.5-2 hours'; +conf_mat{1,10}='3+ hours'; + +conf_mat{1,11}='0 hours'; +conf_mat{1,12}='0.5-2 hours'; +conf_mat{1,13}='3+ hours'; + +conf_mat{1,14}='0 hours'; +conf_mat{1,15}='0.5-1 hours'; +conf_mat{1,16}='3+ hours'; + +conf_mat{2,1}='total'; +conf_mat{3,1}='girls'; +conf_mat{4,1}='boys'; +conf_mat{5,1}='Wg'; +conf_mat{6,1}='Wb'; +conf_mat{7,1}='Bg'; +conf_mat{8,1}='Bb'; +conf_mat{9,1}='Hg'; +conf_mat{10,1}='Hb'; +conf_mat{11,1}='Og'; +conf_mat{12,1}='Ob'; +conf_mat{13,1}='W'; +conf_mat{14,1}='B'; +conf_mat{15,1}='H'; +conf_mat{16,1}='O'; +conf_mat{17,1}='9'; +conf_mat{18,1}='10'; +conf_mat{19,1}='11'; +conf_mat{20,1}='12'; +conf_mat{21,1}='9g'; +conf_mat{22,1}='9b'; +conf_mat{23,1}='10g'; +conf_mat{24,1}='10b'; +conf_mat{25,1}='11g'; +conf_mat{26,1}='11b'; +conf_mat{27,1}='12g'; +conf_mat{28,1}='12b'; + +conf_mat{29,1}='H9g'; +conf_mat{30,1}='H10g'; +conf_mat{31,1}='H11g'; +conf_mat{32,1}='H12g'; +conf_mat{33,1}='H9b'; +conf_mat{34,1}='H10b'; +conf_mat{35,1}='H11b'; +conf_mat{36,1}='H12b'; +conf_mat{37,1}='W9g'; +conf_mat{38,1}='W10g'; +conf_mat{39,1}='W11g'; +conf_mat{40,1}='W12g'; +conf_mat{41,1}='W9b'; +conf_mat{42,1}='W10b'; +conf_mat{43,1}='W11b'; +conf_mat{44,1}='W12b'; + +conf_mat{45,1}='B9g'; +conf_mat{46,1}='B10g'; +conf_mat{47,1}='B11g'; +conf_mat{48,1}='B12g'; +conf_mat{49,1}='B9b'; +conf_mat{50,1}='B10b'; +conf_mat{51,1}='B11b'; +conf_mat{52,1}='B12b'; +conf_mat{53,1}='O9g'; +conf_mat{54,1}='O10g'; +conf_mat{55,1}='O11g'; +conf_mat{56,1}='O12g'; +conf_mat{57,1}='O9b'; +conf_mat{58,1}='O10b'; +conf_mat{59,1}='O11b'; +conf_mat{60,1}='O12b'; + +n_mat=zeros(59,r); +x_mat=zeros(59,4,r); + +boys=double.empty; +girls=double.empty; +total=double.empty; +c=1; + + +%key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +%E(5)=3, %F(6)=4, %G(7)=5 +for i=1:r +% total(i)=TOTAL(i,1); + for j=1:7 + n=zeros(59,1); + x=zeros(59,3); + + index_yes{i}=find(question_mat(i,:)==j); + index_girls{i}=find(sex(i,:)==1); + index_boys{i}=find(sex(i,:)==2); + index_W{i}=find(race(i,:)== 1 ); + index_B{i}=find(race(i,:)== 2 ); + index_H{i}=find(race(i,:)== 3 ); + index_O{i}=find(race(i,:)== 4 ); + index_9{i}=find(grade(i,:)== 1 ); + index_10{i}=find(grade(i,:)== 2 ); + index_11{i}=find(grade(i,:)== 3 ); + index_12{i}=find(grade(i,:)== 4 ); + + index_W9{i}=intersect(index_9{i},index_W{i}); + index_W10{i}=intersect(index_10{i},index_W{i}); + index_W11{i}=intersect(index_11{i},index_W{i}); + index_W12{i}=intersect(index_12{i},index_W{i}); + + index_B9{i}=intersect(index_9{i},index_B{i}); + index_B10{i}=intersect(index_10{i},index_B{i}); + index_B11{i}=intersect(index_11{i},index_B{i}); + index_B12{i}=intersect(index_12{i},index_B{i}); + + index_H9{i}=intersect(index_9{i},index_H{i}); + index_H10{i}=intersect(index_10{i},index_H{i}); + index_H11{i}=intersect(index_11{i},index_H{i}); + index_H12{i}=intersect(index_12{i},index_H{i}); + + index_O9{i}=intersect(index_9{i},index_O{i}); + index_O10{i}=intersect(index_10{i},index_O{i}); + index_O11{i}=intersect(index_11{i},index_O{i}); + index_O12{i}=intersect(index_12{i},index_O{i}); + + index_missQ{i}=find(question_mat(i,:)== 0); %students who didn't answer the Q + index_nomiss{i}=find(question_mat(i,:)>0); %answers that were NOT missing (ie. 0's and 1's / no's and yes's) + index_noNaN{i}=find(isnan(question_mat(i,:))==0); + + index_total_b{i}=intersect(index_nomiss{i},index_boys{i}); %index of all boys who answered + index_total_g{i}=intersect(index_nomiss{i},index_girls{i}); %index of all girls who answered + + w=weight(i,:)'; + %TOTAL FOR PREVALENCE + total_ans(i)=nansum(w(index_nomiss{i})); + total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered + total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered + total_W{i}=nansum(w(intersect(index_nomiss{i}, index_W{i}))); %total # of white students who answered + total_B{i}=nansum(w(intersect(index_nomiss{i}, index_B{i}))); %total # of black students who answered + total_H{i}=nansum(w(intersect(index_nomiss{i}, index_H{i}))); %total # of hispanic students who answered + total_O{i}=nansum(w(intersect(index_nomiss{i}, index_O{i}))); %total # of "other" students who answered + + total_w(i)=total_W{i}; + total_b(i)=total_B{i}; + total_h(i)=total_H{i}; + total_o(i)=total_O{i}; + + total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i}))); + total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i}))); + total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i}))); + total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i}))); + total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i}))); + total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i}))); + total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i}))); + total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i}))); + total_9(i)=nansum(w(intersect((index_9{i}),index_nomiss{i}))); + total_10(i)=nansum(w(intersect((index_10{i}),index_nomiss{i}))); + total_11(i)=nansum(w(intersect((index_11{i}),index_nomiss{i}))); + total_12(i)=nansum(w(intersect((index_12{i}),index_nomiss{i}))); + + total_9G(i)=nansum(w(intersect(index_9{i},index_total_g{i}))); + total_10G(i)=nansum(w(intersect(index_10{i},index_total_g{i}))); + total_11G(i)=nansum(w(intersect(index_11{i},index_total_g{i}))); + total_12G(i)=nansum(w(intersect(index_12{i},index_total_g{i}))); + + total_9B(i)=nansum(w(intersect(index_9{i},index_total_b{i}))); + total_10B(i)=nansum(w(intersect(index_10{i},index_total_b{i}))); + total_11B(i)=nansum(w(intersect(index_11{i},index_total_b{i}))); + total_12B(i)=nansum(w(intersect(index_12{i},index_total_b{i}))); + + total_9G_W(i)=nansum(w(intersect(index_W9{i},index_total_g{i}))); + total_9B_W(i)=nansum(w(intersect(index_W9{i},index_total_b{i}))); + total_9G_B(i)=nansum(w(intersect(index_B9{i},index_total_g{i}))); + total_9B_B(i)=nansum(w(intersect(index_B9{i},index_total_b{i}))); + + total_9G_H(i)=nansum(w(intersect(index_H9{i},index_total_g{i}))); + total_9B_H(i)=nansum(w(intersect(index_H9{i},index_total_b{i}))); + total_9G_O(i)=nansum(w(intersect(index_O9{i},index_total_g{i}))); + total_9B_O(i)=nansum(w(intersect(index_O9{i},index_total_b{i}))); + + total_10G_W(i)=nansum(w(intersect(index_W10{i},index_total_g{i}))); + total_10B_W(i)=nansum(w(intersect(index_W10{i},index_total_b{i}))); + total_10G_B(i)=nansum(w(intersect(index_B10{i},index_total_g{i}))); + total_10B_B(i)=nansum(w(intersect(index_B10{i}, index_total_b{i}))); + + total_10G_H(i)=nansum(w(intersect(index_H10{i},index_total_g{i}))); + total_10B_H(i)=nansum(w(intersect(index_H10{i},index_total_b{i}))); + total_10G_O(i)=nansum(w(intersect(index_O10{i},index_total_g{i}))); + total_10B_O(i)=nansum(w(intersect(index_O10{i},index_total_b{i}))); + + total_11G_W(i)=nansum(w(intersect(index_W11{i},index_total_g{i}))); + total_11B_W(i)=nansum(w(intersect(index_W11{i},index_total_b{i}))); + total_11G_B(i)=nansum(w(intersect(index_B11{i},index_total_g{i}))); + total_11B_B(i)=nansum(w(intersect(index_B11{i},index_total_b{i}))); + + total_11G_H(i)=nansum(w(intersect(index_H11{i},index_total_g{i}))); + total_11B_H(i)=nansum(w(intersect(index_H11{i},index_total_b{i}))); + total_11G_O(i)=nansum(w(intersect(index_O11{i},index_total_g{i}))); + total_11B_O(i)=nansum(w(intersect(index_O11{i},index_total_b{i}))); + + total_12G_W(i)=nansum(w(intersect(index_W12{i},index_total_g{i}))); + total_12B_W(i)=nansum(w(intersect(index_W12{i},index_total_b{i}))); + total_12G_B(i)=nansum(w(intersect(index_B12{i},index_total_g{i}))); + total_12B_B(i)=nansum(w(intersect(index_B12{i},index_total_b{i}))); + + total_12G_H(i)=nansum(w(intersect(index_H12{i},index_total_g{i}))); + total_12B_H(i)=nansum(w(intersect(index_H12{i},index_total_b{i}))); + total_12G_O(i)=nansum(w(intersect(index_O12{i},index_total_g{i}))); + total_12B_O(i)=nansum(w(intersect(index_O12{i},index_total_b{i}))); + + total_9_W(i)=nansum(w(intersect(index_nomiss{i},index_W9{i}))); + total_9_B(i)=nansum(w(intersect(index_nomiss{i},index_B9{i}))); + total_9_H(i)=nansum(w(intersect(index_nomiss{i},index_H9{i}))); + total_9_O(i)=nansum(w(intersect(index_nomiss{i},index_O9{i}))); + + total_10_W(i)=nansum(w(intersect(index_nomiss{i},index_W10{i}))); + total_10_B(i)=nansum(w(intersect(index_nomiss{i},index_B10{i}))); + total_10_H(i)=nansum(w(intersect(index_nomiss{i},index_H10{i}))); + total_10_O(i)=nansum(w(intersect(index_nomiss{i},index_O10{i}))); + + total_11_W(i)=nansum(w(intersect(index_nomiss{i},index_W11{i}))); + total_11_B(i)=nansum(w(intersect(index_nomiss{i},index_B11{i}))); + total_11_H(i)=nansum(w(intersect(index_nomiss{i},index_H11{i}))); + total_11_O(i)=nansum(w(intersect(index_nomiss{i},index_O11{i}))); + + total_12_W(i)=nansum(w(intersect(index_nomiss{i},index_W12{i}))); + total_12_B(i)=nansum(w(intersect(index_nomiss{i},index_B12{i}))); + total_12_H(i)=nansum(w(intersect(index_nomiss{i},index_H12{i}))); + total_12_O(i)=nansum(w(intersect(index_nomiss{i},index_O12{i}))); + + %TOTAL FOR CI + total_ans_(i)=nansum(w(index_nomiss{i})); + total_girls_(i)=nansum(w(index_total_g{i})); %total # of girls who answered + total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered + total_W{i}=nansum(w(intersect(index_nomiss{i}, index_W{i}))); %total # of white students who answered + total_B{i}=nansum(w(intersect(index_nomiss{i}, index_B{i}))); %total # of black students who answered + total_H{i}=nansum(w(intersect(index_nomiss{i}, index_H{i}))); %total # of hispanic students who answered + total_O{i}=nansum(w(intersect(index_nomiss{i}, index_O{i}))); %total # of "other" students who answered + + total_w(i)=total_W{i}; + total_b(i)=total_B{i}; + total_h(i)=total_H{i}; + total_o(i)=total_O{i}; + + total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i}))); + total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i}))); + total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i}))); + total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i}))); + total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i}))); + total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i}))); + total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i}))); + total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i}))); + total_9(i)=nansum(w(intersect((index_9{i}),index_nomiss{i}))); + total_10(i)=nansum(w(intersect((index_10{i}),index_nomiss{i}))); + total_11(i)=nansum(w(intersect((index_11{i}),index_nomiss{i}))); + total_12(i)=nansum(w(intersect((index_12{i}),index_nomiss{i}))); + + total_9G(i)=nansum(w(intersect(index_9{i},index_total_g{i}))); + total_10G(i)=nansum(w(intersect(index_10{i},index_total_g{i}))); + total_11G(i)=nansum(w(intersect(index_11{i},index_total_g{i}))); + total_12G(i)=nansum(w(intersect(index_12{i},index_total_g{i}))); + + total_9B(i)=nansum(w(intersect(index_9{i},index_total_b{i}))); + total_10B(i)=nansum(w(intersect(index_10{i},index_total_b{i}))); + total_11B(i)=nansum(w(intersect(index_11{i},index_total_b{i}))); + total_12B(i)=nansum(w(intersect(index_12{i},index_total_b{i}))); + + total_9G_W(i)=nansum(w(intersect(index_W9{i},index_total_g{i}))); + total_9B_W(i)=nansum(w(intersect(index_W9{i},index_total_b{i}))); + total_9G_B(i)=nansum(w(intersect(index_B9{i},index_total_g{i}))); + total_9B_B(i)=nansum(w(intersect(index_B9{i},index_total_b{i}))); + + total_9G_H(i)=nansum(w(intersect(index_H9{i},index_total_g{i}))); + total_9B_H(i)=nansum(w(intersect(index_H9{i},index_total_b{i}))); + total_9G_O(i)=nansum(w(intersect(index_O9{i},index_total_g{i}))); + total_9B_O(i)=nansum(w(intersect(index_O9{i},index_total_b{i}))); + + total_10G_W(i)=nansum(w(intersect(index_W10{i},index_total_g{i}))); + total_10B_W(i)=nansum(w(intersect(index_W10{i},index_total_b{i}))); + total_10G_B(i)=nansum(w(intersect(index_B10{i},index_total_g{i}))); + total_10B_B(i)=nansum(w(intersect(index_B10{i}, index_total_b{i}))); + + total_10G_H(i)=nansum(w(intersect(index_H10{i},index_total_g{i}))); + total_10B_H(i)=nansum(w(intersect(index_H10{i},index_total_b{i}))); + total_10G_O(i)=nansum(w(intersect(index_O10{i},index_total_g{i}))); + total_10B_O(i)=nansum(w(intersect(index_O10{i},index_total_b{i}))); + + total_11G_W(i)=nansum(w(intersect(index_W11{i},index_total_g{i}))); + total_11B_W(i)=nansum(w(intersect(index_W11{i},index_total_b{i}))); + total_11G_B(i)=nansum(w(intersect(index_B11{i},index_total_g{i}))); + total_11B_B(i)=nansum(w(intersect(index_B11{i},index_total_b{i}))); + + total_11G_H(i)=nansum(w(intersect(index_H11{i},index_total_g{i}))); + total_11B_H(i)=nansum(w(intersect(index_H11{i},index_total_b{i}))); + total_11G_O(i)=nansum(w(intersect(index_O11{i},index_total_g{i}))); + total_11B_O(i)=nansum(w(intersect(index_O11{i},index_total_b{i}))); + + total_12G_W(i)=nansum(w(intersect(index_W12{i},index_total_g{i}))); + total_12B_W(i)=nansum(w(intersect(index_W12{i},index_total_b{i}))); + total_12G_B(i)=nansum(w(intersect(index_B12{i},index_total_g{i}))); + total_12B_B(i)=nansum(w(intersect(index_B12{i},index_total_b{i}))); + + total_12G_H(i)=nansum(w(intersect(index_H12{i},index_total_g{i}))); + total_12B_H(i)=nansum(w(intersect(index_H12{i},index_total_b{i}))); + total_12G_O(i)=nansum(w(intersect(index_O12{i},index_total_g{i}))); + total_12B_O(i)=nansum(w(intersect(index_O12{i},index_total_b{i}))); + + total_9_W(i)=nansum(w(intersect(index_nomiss{i},index_W9{i}))); + total_9_B(i)=nansum(w(intersect(index_nomiss{i},index_B9{i}))); + total_9_H(i)=nansum(w(intersect(index_nomiss{i},index_H9{i}))); + total_9_O(i)=nansum(w(intersect(index_nomiss{i},index_O9{i}))); + + total_10_W(i)=nansum(w(intersect(index_nomiss{i},index_W10{i}))); + total_10_B(i)=nansum(w(intersect(index_nomiss{i},index_B10{i}))); + total_10_H(i)=nansum(w(intersect(index_nomiss{i},index_H10{i}))); + total_10_O(i)=nansum(w(intersect(index_nomiss{i},index_O10{i}))); + + total_11_W(i)=nansum(w(intersect(index_nomiss{i},index_W11{i}))); + total_11_B(i)=nansum(w(intersect(index_nomiss{i},index_B11{i}))); + total_11_H(i)=nansum(w(intersect(index_nomiss{i},index_H11{i}))); + total_11_O(i)=nansum(w(intersect(index_nomiss{i},index_O11{i}))); + + total_12_W(i)=nansum(w(intersect(index_nomiss{i},index_W12{i}))); + total_12_B(i)=nansum(w(intersect(index_nomiss{i},index_B12{i}))); + total_12_H(i)=nansum(w(intersect(index_nomiss{i},index_H12{i}))); + total_12_O(i)=nansum(w(intersect(index_nomiss{i},index_O12{i}))); + + w=weight(i,:)'; + index_yesgirls{i}=intersect(index_yes{i},index_girls{i}); + index_yesboys{i}=intersect(index_yes{i},index_boys{i}); + yes_girls(i)=nansum(w(index_yesgirls{i})); + yes_boys(i)=nansum(w(index_yesboys{i})); + total_yes(i)=nansum(w(index_yes{i})); + + yes_W(i)=nansum(w(intersect(index_yes{i}, index_W{i}))); + yes_B(i)=nansum(w(intersect(index_yes{i}, index_B{i}))); + yes_H(i)=nansum(w(intersect(index_yes{i}, index_H{i}))); + yes_O(i)=nansum(w(intersect(index_yes{i}, index_O{i}))); + yes_WG(i)=nansum(w(intersect(index_yesgirls{i},index_W{i}))); + yes_BG(i)=nansum(w(intersect(index_yesgirls{i},index_B{i}))); + yes_HG(i)=nansum(w(intersect(index_yesgirls{i},index_H{i}))); + yes_OG(i)=nansum(w(intersect(index_yesgirls{i},index_O{i}))); + yes_WB(i)=nansum(w(intersect(index_yesboys{i},index_W{i}))); + yes_BB(i)=nansum(w(intersect(index_yesboys{i},index_B{i}))); + yes_HB(i)=nansum(w(intersect(index_yesboys{i},index_H{i}))); + yes_OB(i)=nansum(w(intersect(index_yesboys{i},index_O{i}))); + yes_9(i)=nansum(w(intersect(index_yes{i},index_9{i}))); + yes_10(i)=nansum(w(intersect(index_yes{i},index_10{i}))); + yes_11(i)=nansum(w(intersect(index_yes{i},index_11{i}))); + yes_12(i)=nansum(w(intersect(index_yes{i},index_12{i}))); + yes_9b(i)=nansum(w(intersect(index_yesboys{i},index_9{i}))); + yes_10b(i)=nansum(w(intersect(index_yesboys{i},index_10{i}))); + yes_11b(i)=nansum(w(intersect(index_yesboys{i},index_11{i}))); + yes_12b(i)=nansum(w(intersect(index_yesboys{i},index_12{i}))); + yes_9g(i)=nansum(w(intersect(index_yesgirls{i},index_9{i}))); + yes_10g(i)=nansum(w(intersect(index_yesgirls{i},index_10{i}))); + yes_11g(i)=nansum(w(intersect(index_yesgirls{i},index_11{i}))); + yes_12g(i)=nansum(w(intersect(index_yesgirls{i},index_12{i}))); + + + yes_9WB(i)=nansum(w(intersect(index_yesboys{i},index_W9{i}))); + yes_10WB(i)=nansum(w(intersect(index_yesboys{i},index_W10{i}))); + yes_11WB(i)=nansum(w(intersect(index_yesboys{i},index_W11{i}))); + yes_12WB(i)=nansum(w(intersect(index_yesboys{i},index_W12{i}))); + yes_9WG(i)=nansum(w(intersect(index_yesgirls{i},index_W9{i}))); + yes_10WG(i)=nansum(w(intersect(index_yesgirls{i},index_W10{i}))); + yes_11WG(i)=nansum(w(intersect(index_yesgirls{i},index_W11{i}))); + yes_12WG(i)=nansum(w(intersect(index_yesgirls{i},index_W12{i}))); + + yes_9W(i)=nansum(w(intersect(index_yes{i},index_W9{i}))); + yes_10W(i)=nansum(w(intersect(index_yes{i},(index_W10{i})))); + yes_11W(i)=nansum(w(intersect(index_yes{i},(index_W11{i})))); + yes_12W(i)=nansum(w(intersect(index_yes{i},(index_W12{i})))); + + yes_9B(i)=nansum(w(intersect(index_yes{i},(index_B9{i})))); + yes_10B(i)=nansum(w(intersect(index_yes{i},(index_B10{i})))); + yes_11B(i)=nansum(w(intersect(index_yes{i},(index_B11{i})))); + yes_12B(i)=nansum(w(intersect(index_yes{i},(index_B12{i})))); + + yes_9H(i)=nansum(w(intersect(index_yes{i},(index_H9{i})))); + yes_10H(i)=nansum(w(intersect(index_yes{i},(index_H10{i})))); + yes_11H(i)=nansum(w(intersect(index_yes{i},(index_H11{i})))); + yes_12H(i)=nansum(w(intersect(index_yes{i},(index_H12{i})))); + + yes_9O(i)=nansum(w(intersect(index_yes{i},(index_O9{i})))); + yes_10O(i)=nansum(w(intersect(index_yes{i},(index_O10{i})))); + yes_11O(i)=nansum(w(intersect(index_yes{i},(index_O11{i})))); + yes_12O(i)=nansum(w(intersect(index_yes{i},(index_O12{i})))); + + yes_9BB(i)=nansum(w(intersect(index_yesboys{i},index_B9{i}))); + yes_10BB(i)=nansum(w(intersect(index_yesboys{i},index_B10{i}))); + yes_11BB(i)=nansum(w(intersect(index_yesboys{i},index_B11{i}))); + yes_12BB(i)=nansum(w(intersect(index_yesboys{i},index_B12{i}))); + yes_9BG(i)=nansum(w(intersect(index_yesgirls{i},index_B9{i}))); + yes_10BG(i)=nansum(w(intersect(index_yesgirls{i},index_B10{i}))); + yes_11BG(i)=nansum(w(intersect(index_yesgirls{i},index_B11{i}))); + yes_12BG(i)=nansum(w(intersect(index_yesgirls{i},index_B12{i}))); + + yes_9HB(i)=nansum(w(intersect(index_yesboys{i},index_H9{i}))); + yes_10HB(i)=nansum(w(intersect(index_yesboys{i},index_H10{i}))); + yes_11HB(i)=nansum(w(intersect(index_yesboys{i},index_H11{i}))); + yes_12HB(i)=nansum(w(intersect(index_yesboys{i},index_H12{i}))); + yes_9HG(i)=nansum(w(intersect(index_yesgirls{i},index_H9{i}))); + yes_10HG(i)=nansum(w(intersect(index_yesgirls{i},index_H10{i}))); + yes_11HG(i)=nansum(w(intersect(index_yesgirls{i},index_H11{i}))); + yes_12HG(i)=nansum(w(intersect(index_yesgirls{i},index_H12{i}))); + + yes_9OB(i)=nansum(w(intersect(index_yesboys{i},index_O9{i}))); + yes_10OB(i)=nansum(w(intersect(index_yesboys{i},index_O10{i}))); + yes_11OB(i)=nansum(w(intersect(index_yesboys{i},index_O11{i}))); + yes_12OB(i)=nansum(w(intersect(index_yesboys{i},index_O12{i}))); + yes_9OG(i)=nansum(w(intersect(index_yesgirls{i},index_O9{i}))); + yes_10OG(i)=nansum(w(intersect(index_yesgirls{i},index_O10{i}))); + yes_11OG(i)=nansum(w(intersect(index_yesgirls{i},index_O11{i}))); + yes_12OG(i)=nansum(w(intersect(index_yesgirls{i},index_O12{i}))); + + girls(1, c)=yes_girls(i)/total_girls(i)*100; %girls + girls(2, c)=yes_WG(i)/total_Wg(i)*100; %WG + girls(3, c)=yes_BG(i)/total_Bg(i)*100; %BG + girls(4, c)=yes_HG(i)/total_Hg(i)*100; %HG + girls(5, c)=yes_OG(i)/total_Og(i)*100; %OG + girls(6, c)=yes_9g(i)/total_9G(i)*100; + girls(7, c)=yes_10g(i)/total_10G(i)*100; + girls(8, c)=yes_11g(i)/total_11G(i)*100; + girls(9, c)=yes_12g(i)/total_12G(i)*100; + girls(10, c)=yes_9WG(i)/total_9G_W(i)*100; + girls(11, c)=yes_10WG(i)/total_10G_W(i)*100; + girls(12, c)=yes_11WG(i)/total_11G_W(i)*100; + girls(13, c)=yes_12WG(i)/total_12G_W(i)*100; + girls(14, c)=yes_9BG(i)/total_9G_B(i)*100; + girls(15, c)=yes_10BG(i)/total_10G_B(i)*100; + girls(16, c)=yes_11BG(i)/total_11G_B(i)*100; + girls(17, c)=yes_12BG(i)/total_12G_B(i)*100; + girls(18, c)=yes_9HG(i)/total_9G_H(i)*100; + girls(19, c)=yes_10HG(i)/total_10G_H(i)*100; + girls(20, c)=yes_11HG(i)/total_11G_H(i)*100; + girls(21, c)=yes_12HG(i)/total_12G_H(i)*100; + girls(22, c)=yes_9OG(i)/total_9G_O(i)*100; + girls(23, c)=yes_10OG(i)/total_10G_O(i)*100; + girls(24, c)=yes_11OG(i)/total_11G_O(i)*100; + girls(25, c)=yes_12OG(i)/total_12G_O(i)*100; + + boys(1, c)=yes_boys(i)/total_boys(i)*100; %boys + boys(2, c)=yes_WB(i)/total_Wb(i)*100; %WB + boys (3, c)=yes_BB(i)/total_Bb(i)*100; %BB + boys(4, c)=yes_HB(i)/total_Hb(i)*100; %HB + boys(5, c)=yes_OB(i)/total_Ob(i)*100; %OB + boys(6, c)=yes_9b(i)/total_9B(i)*100; + boys(7, c)=yes_10b(i)/total_10B(i)*100; + boys(8, c)=yes_11b(i)/total_11B(i)*100; + boys(9, c)=yes_12b(i)/total_12B(i)*100; + boys(10, c)=yes_9WB(i)/total_9B_W(i)*100; + boys(11, c)=yes_10WB(i)/total_10B_W(i)*100; + boys(12, c)=yes_11WB(i)/total_11B_W(i)*100; + boys(13, c)=yes_12WB(i)/total_12B_W(i)*100; + boys(14, c)=yes_9BB(i)/total_9B_B(i)*100; + boys(15, c)=yes_10BB(i)/total_10B_B(i)*100; + boys(16, c)=yes_11BB(i)/total_11B_B(i)*100; + boys(17, c)=yes_12BB(i)/total_12B_B(i)*100; + boys(18, c)=yes_9HB(i)/total_9B_H(i)*100; + boys(19, c)=yes_10HB(i)/total_10B_H(i)*100; + boys(20, c)=yes_11HB(i)/total_11B_H(i)*100; + boys(21, c)=yes_12HB(i)/total_12B_H(i)*100; + boys(22, c)=yes_9OB(i)/total_9B_O(i)*100; + boys(23, c)=yes_10OB(i)/total_10B_O(i)*100; + boys(24, c)=yes_11OB(i)/total_11B_O(i)*100; + boys(25, c)=yes_12OB(i)/total_12B_O(i)*100; + + total(1,c)=total_yes(i)/total_ans(i)*100; %total + total(2,c)=yes_boys(i)/total_boys(i)*100; %boys + total(3,c)=yes_girls(i)/total_girls(i)*100; %girls + total(4,c)=yes_W(i)/total_w(i)*100; %whites + total(5,c)=yes_B(i)/total_b(i)*100; %blacks + total(6,c)=yes_H(i)/total_h(i)*100; %hispanics + total(7,c)=yes_O(i)/total_o(i)*100; %other + total(8,c)=yes_9(i)/total_9(i)*100; + total(9,c)=yes_10(i)/total_10(i)*100; + total(10,c)=yes_11(i)/total_11(i)*100; + total(11,c)=yes_12(i)/total_12(i)*100; + total(12, c)=yes_9W(i)/total_9_W(i)*100; + total(13, c)=yes_10W(i)/total_10_W(i)*100; + total(14, c)=yes_11W(i)/total_11_W(i)*100; + total(15, c)=yes_12W(i)/total_12_W(i)*100; + total(16, c)=yes_9B(i)/total_9_B(i)*100; + total(17, c)=yes_10B(i)/total_10_B(i)*100; + total(18, c)=yes_11B(i)/total_11_B(i)*100; + total(19, c)=yes_12B(i)/total_12_B(i)*100; + total(20, c)=yes_9H(i)/total_9_H(i)*100; + total(21, c)=yes_10H(i)/total_10_H(i)*100; + total(22, c)=yes_11H(i)/total_11_H(i)*100; + total(23, c)=yes_12H(i)/total_12_H(i)*100; + total(24, c)=yes_9O(i)/total_9_O(i)*100; + total(25, c)=yes_10O(i)/total_10_O(i)*100; + total(26, c)=yes_11O(i)/total_11_O(i)*100; + total(27, c)=yes_12O(i)/total_12_O(i)*100; + + c=c+1; + + %for stats + n_mat (1,i)= total_ans(i); + n_mat (2,i)=total_girls(i); + n_mat (3,i)=total_boys(i); + n_mat (4,i)=total_Wg(i); + n_mat (5,i)=total_Wb(i); + n_mat (6,i)=total_Bg(i); + n_mat (7,i)=total_Bb(i); + n_mat (8,i)=total_Hg(i); + n_mat (9,i)=total_Hb(i); + n_mat (10,i)=total_Og(i); + n_mat (11,i)=total_Ob(i); + n_mat (12,i)=total_w(i); + n_mat (13,i)=total_b(i); + n_mat (14,i)=total_h(i); + n_mat (15,i)=total_o(i); + n_mat (16,i)=total_9(i); + n_mat (17,i)=total_10(i); + n_mat (18,i)=total_11(i); + n_mat (19,i)=total_12(i); + n_mat (20,i)=total_9G(i); + n_mat (21,i)=total_9B(i); + n_mat (22,i)=total_10G(i); + n_mat (23,i)=total_10B(i); + n_mat (24,i)=total_11G(i); + n_mat (25,i)=total_11B(i); + n_mat (26,i)=total_12G(i); + n_mat (27,i)=total_12B(i); + + n_mat (28,i)=total_9G_H(i); + n_mat (29,i)=total_10G_H(i); + n_mat (30,i)=total_11G_H(i); + n_mat (31,i)=total_12G_H(i); + n_mat (32,i)=total_9B_H(i); + n_mat (33,i)=total_10B_H(i); + n_mat (34,i)=total_11B_H(i); + n_mat (35,i)=total_12B_H(i); + + n_mat (36,i)=total_9G_W(i); + n_mat (37,i)=total_10G_W(i); + n_mat (38,i)=total_11G_W(i); + n_mat (39,i)=total_12G_W(i); + n_mat (40,i)=total_9B_W(i); + n_mat (41,i)=total_10B_W(i); + n_mat (42,i)=total_11B_W(i); + n_mat (43,i)=total_12B_W(i); + + n_mat (44,i)=total_9G_B(i); + n_mat (45,i)=total_10G_B(i); + n_mat (46,i)=total_11G_B(i); + n_mat (47,i)=total_12G_B(i); + n_mat (48,i)=total_9B_B(i); + n_mat (49,i)=total_10B_B(i); + n_mat (50,i)=total_11B_B(i); + n_mat (51,i)=total_12B_B(i); + + n_mat (52,i)=total_9G_O(i); + n_mat (53,i)=total_10G_O(i); + n_mat (54,i)=total_11G_O(i); + n_mat (55,i)=total_12G_O(i); + n_mat (56,i)=total_9B_O(i); + n_mat (57,i)=total_10B_O(i); + n_mat (58,i)=total_11B_O(i); + n_mat (59,i)=total_12B_O(i); + + if (j>1 && j<5)%0.5-1 hour hours or less + x(1,2)=total_yes(i); + x(2,2)=yes_girls(i); + x(3,2)=yes_boys(i); + x(4,2)=yes_WG(i); + x(5,2)=yes_WB(i); + x(6,2)=yes_BG(i); + x(7,2)=yes_BB(i); + x(8,2)=yes_HG(i); + x(9,2)=yes_HB(i); + x(10,2)=yes_OG(i); + x(11,2)=yes_OB(i); + x(12,2)=yes_W(i); + x(13,2)=yes_B(i); + x(14,2)=yes_H(i); + x(15,2)=yes_O(i); + x(16,2)=yes_9(i); + x(17,2)=yes_10(i); + x(18,2)=yes_11(i); + x(19,2)=yes_12(i); + x(20,2)=yes_9g(i); + x(21,2)=yes_9b(i); + x(22,2)=yes_10g(i); + x(23,2)=yes_10b(i); + x(24,2)=yes_11g(i); + x(25,2)=yes_11b(i); + x(26,2)=yes_12g(i); + x(27,2)=yes_12b(i); + + x(28,2)=yes_9HG(i); + x(29, 2)=yes_10HG(i); + x(30, 2)=yes_11HG(i); + x(31, 2)=yes_12HG(i); + x(32, 2)=yes_9HB(i); + x(33, 2)=yes_10HB(i); + x(34, 2)=yes_11HB(i); + x(35, 2)=yes_12HB(i); + + x(36, 2)=yes_9WG(i); + x(37, 2)=yes_10WG(i); + x(38, 2)=yes_11WG(i); + x(39, 2)=yes_12WG(i); + x(40, 2)=yes_9WB(i); + x(41, 2)=yes_10WB(i); + x(42, 2)=yes_11WB(i); + x(43, 2)=yes_12WB(i); + + x(44, 2)=yes_9BG(i); + x(45, 2)=yes_10BG(i); + x(46, 2)=yes_11BG(i); + x(47, 2)=yes_12BG(i); + x(48, 2)=yes_9BB(i); + x(49, 2)=yes_10BB(i); + x(50, 2)=yes_11BB(i); + x(51, 2)=yes_12BB(i); + + x(52, 2)=yes_9OG(i); + x(53, 2)=yes_10OG(i); + x(54, 2)=yes_11OG(i); + x(55, 2)=yes_12OG(i); + x(56, 2)=yes_9OB(i); + x(57, 2)=yes_10OB(i); + x(58, 2)=yes_11OB(i); + x(59, 2)=yes_12OB(i); + + x_mat(:,2, i)=x_mat(:, 2, i)+x(:,2); + elseif j>4 %3-4 hours hours or more + x(1,3)=total_yes(i); + x(2,3)=yes_girls(i); + x(3,3)=yes_boys(i); + x(4,3)=yes_WG(i); + x(5,3)=yes_WB(i); + x(6,3)=yes_BG(i); + x(7,3)=yes_BB(i); + x(8,3)=yes_HG(i); + x(9,3)=yes_HB(i); + x(10,3)=yes_OG(i); + x(11,3)=yes_OB(i); + x(12,3)=yes_W(i); + x(13,3)=yes_B(i); + x(14,3)=yes_H(i); + x(15,3)=yes_O(i); + x(16,3)=yes_9(i); + x(17,3)=yes_10(i); + x(18,3)=yes_11(i); + x(19,3)=yes_12(i); + x(20,3)=yes_9g(i); + x(21,3)=yes_9b(i); + x(22,3)=yes_10g(i); + x(23,3)=yes_10b(i); + x(24,3)=yes_11g(i); + x(25,3)=yes_11b(i); + x(26,3)=yes_12g(i); + x(27,3)=yes_12b(i); + + x(28,3)=yes_9HG(i); + x(29, 3)=yes_10HG(i); + x(30, 3)=yes_11HG(i); + x(31, 3)=yes_12HG(i); + x(32, 3)=yes_9HB(i); + x(33, 3)=yes_10HB(i); + x(34, 3)=yes_11HB(i); + x(35, 3)=yes_12HB(i); + + x(36, 3)=yes_9WG(i); + x(37, 3)=yes_10WG(i); + x(38, 3)=yes_11WG(i); + x(39, 3)=yes_12WG(i); + x(40, 3)=yes_9WB(i); + x(41, 3)=yes_10WB(i); + x(42, 3)=yes_11WB(i); + x(43, 3)=yes_12WB(i); + + x(44, 3)=yes_9BG(i); + x(45, 3)=yes_10BG(i); + x(46, 3)=yes_11BG(i); + x(47, 3)=yes_12BG(i); + x(48, 3)=yes_9BB(i); + x(49, 3)=yes_10BB(i); + x(50, 3)=yes_11BB(i); + x(51, 3)=yes_12BB(i); + + x(52, 3)=yes_9OG(i); + x(53, 3)=yes_10OG(i); + x(54, 3)=yes_11OG(i); + x(55, 3)=yes_12OG(i); + x(56, 3)=yes_9OB(i); + x(57, 3)=yes_10OB(i); + x(58, 3)=yes_11OB(i); + x(59, 3)=yes_12OB(i); + + x_mat(:,3, i)=x_mat(:,3, i)+x(:,3); + else + if j==1 + F=1; + end + x_mat(1,F, i)=total_yes(i); + x_mat(2,F, i)=yes_girls(i); + x_mat(3,F, i)=yes_boys(i); + x_mat(4,F, i)=yes_WG(i); + x_mat(5,F, i)=yes_WB(i); + x_mat(6,F, i)=yes_BG(i); + x_mat(7,F, i)=yes_BB(i); + x_mat(8,F, i)=yes_HG(i); + x_mat(9,F, i)=yes_HB(i); + x_mat(10,F, i)=yes_OG(i); + x_mat(11,F, i)=yes_OB(i); + x_mat(12,F, i)=yes_W(i); + x_mat(13,F, i)=yes_B(i); + x_mat(14,F, i)=yes_H(i); + x_mat(15,F, i)=yes_O(i); + x_mat(16,F, i)=yes_9(i); + x_mat(17,F, i)=yes_10(i); + x_mat(18,F, i)=yes_11(i); + x_mat(19,F, i)=yes_12(i); + x_mat(20,F, i)=yes_9g(i); + x_mat(21,F, i)=yes_9b(i); + x_mat(22,F, i)=yes_10g(i); + x_mat(23,F, i)=yes_10b(i); + x_mat(24,F, i)=yes_11g(i); + x_mat(25,F, i)=yes_11b(i); + x_mat(26,F, i)=yes_12g(i); + x_mat(27,F, i)=yes_12b(i); + + x_mat(28,F, i)=yes_9HG(i); + x_mat(29, F, i)=yes_10HG(i); + x_mat(30, F, i)=yes_11HG(i); + x_mat(31, F, i)=yes_12HG(i); + x_mat(32, F, i)=yes_9HB(i); + x_mat(33, F, i)=yes_10HB(i); + x_mat(34, F, i)=yes_11HB(i); + x_mat(35, F, i)=yes_12HB(i); + + x_mat(36, F, i)=yes_9WG(i); + x_mat(37, F, i)=yes_10WG(i); + x_mat(38, F, i)=yes_11WG(i); + x_mat(39, F, i)=yes_12WG(i); + x_mat(40, F, i)=yes_9WB(i); + x_mat(41, F, i)=yes_10WB(i); + x_mat(42, F, i)=yes_11WB(i); + x_mat(43, F, i)=yes_12WB(i); + + x_mat(44, F, i)=yes_9BG(i); + x_mat(45, F, i)=yes_10BG(i); + x_mat(46, F, i)=yes_11BG(i); + x_mat(47, F, i)=yes_12BG(i); + x_mat(48, F, i)=yes_9BB(i); + x_mat(49, F, i)=yes_10BB(i); + x_mat(50, F, i)=yes_11BB(i); + x_mat(51, F, i)=yes_12BB(i); + + x_mat(52, F, i)=yes_9OG(i); + x_mat(53, F, i)=yes_10OG(i); + x_mat(54, F, i)=yes_11OG(i); + x_mat(55, F, i)=yes_12OG(i); + x_mat(56, F, i)=yes_9OB(i); + x_mat(57, F, i)=yes_10OB(i); + x_mat(58, F, i)=yes_11OB(i); + x_mat(59, F, i)=yes_12OB(i); + + end + + + end +end + +c=1; +c2=1; +%key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +%E(5)=3, %F(6)=4, %G(7)=5 +final_boys=double.empty; + +for i=1:5 + final_boys(:,c)=boys(:,c2); + final_boys(:,c+1)=boys(:,c2+1)+boys(:,c2+2)+boys(:,c2+3); + final_boys(:,c+2)=boys(:,c+4)+boys(c2+5); + final_boys(:,c+3)=boys(:,c2+6); + c2=c2+7; + c=c+3; +end + +c=1; +c2=1; +%key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +%E(5)=3, %F(6)=4, %G(7)=5 +final_girls=double.empty; +for i=1:5 + final_girls(:,c)=girls(:,c2); + final_girls(:,c+1)=girls(:,c2+1)+girls(:,c2+2)+girls(:,c2+3); + final_girls(:,c+2)=girls(:,c2+4)+girls(:,c2+5); + final_girls(:,c+3)=girls(:,c2+6); + c2=c2+7; + c=c+3; +end + +c=1; +c2=1; +%key A(1)=0, B(2)=<1, C(3)=1 D(4)=2 +%E(5)=3, %F(6)=4, %G(7)=5 +final_total=double.empty; + +for i=1:5 + final_total(:,c)=total(:,c2); + final_total(:,c+1)=total(:,c2+1)+total(:,c2+2)+total(:,c2+3); + final_total(:,c+2)=total(:,c2+4)+total(:,c2+5); + final_total(:,c+3)=total(:,c2+6); + c2=c2+7; + c=c+3; +end + +%%confidence interval + +lower_mat=double.empty; +upper_mat=double.empty; +plot_mat=double.empty; +z=1.96; +for i=1:59 + count=2; + for j=1:r + n=n_mat(i,j); + for k=1:3 + x=x_mat(i,k, j); %x_mat=zeros(59,5,r); + p=x/n; %x is the number of subjects saying "yes", n is the total subjects + upper=((p+z*sqrt(p*(1-p)/n))*100); + lower=((p-z*sqrt(p*(1-p)/n))*100); + lower_mat(i,count-1)=lower; + upper_mat(i,count-1)=upper; + upper=sprintf('%0.1f',round(upper*10)/10); + lower=sprintf('%0.1f',round(lower*10)/10); + p=p*100; + plot_mat(i,count-1)=p; + p_num=sprintf('%0.1f', round(p*10)/10); + conf_mat{i+1,count}=[p_num ' [' lower ', ' upper ']']; + count=count+1; + end + end +end + +%make plots! +x=2005:2:2013; +lookat=[1, 2, 3; 14, 6, 7; 15, 8, 9; 16, 10, 11]; %, 12, 13, 14, 15, 16, 17, 18, 19]; +[r,c]=size(lookat); +cmap=jet(c); +title_mat={'0 hours', '3 hours or less', '3+ hours'}; +ylabel_mat={'Total', 'White', 'Black', 'Hispanic'}; +count=1; +for k=1:4 %by race + for j=1:3 + f2=subplot(4,3,count); + for i=1:c + I=lookat(k,i); + %CI + x2=[2005 2005]; + y2=[lower_mat(I,j) upper_mat(I,j)]; + plot(x2, y2, '-k'); + hold on + x2=[2007 2007]; + y2=[lower_mat(I,j+3) upper_mat(I,j+3)]; + plot(x2, y2, '-k'); + hold on + x2=[2009 2009]; + y2=[lower_mat(I,j+6) upper_mat(I,j+6)]; + plot(x2, y2, '-k'); + hold on + x2=[2011 2011]; + y2=[lower_mat(I,j+9) upper_mat(I,j+9)]; + plot(x2, y2, '-k'); + hold on + x2=[2013 2013]; + y2=[lower_mat(I,j+12) upper_mat(I,j+12)]; + plot(x2, y2, '-k'); + hold on + y=[plot_mat(I,j), plot_mat(I, j+3), plot_mat(I,j+6), plot_mat(I,j+9),plot_mat(I,j+12)]; + plot(x, y,'-', 'Color', cmap(i,:)); %, 'MarkerFaceColor', cmap(i,:)'MarkerEdgeColor', cmap(i,:), 'MarkerSize', 3, + end + ylim([0 65]); + xlim([2004 2014]); + hold off; + set(gca, 'xtick', x); + set(gca, 'xticklabel', []); + + if count<4 + title(title_mat{j}); + if count==1 + pos_1=get(f2, 'Position'); + F1=f2; + elseif count==2 + set(gca, 'yticklabel', []); + linkaxes([f1 f2],'y'); %make y axis the same + pos1=get(f1,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(1)=pos1(1) + pos2(3); %move the second so it touches the first + set (f2,'Position',pos2); + pos_2=get(f2, 'Position'); + F2=f2; + else + set(gca, 'yticklabel', []); + linkaxes([f1 f2],'y'); %make y axis the same + pos1=get(f1,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(1)=pos1(1) + pos2(3); %move the second so it touches the first + set (f2,'Position',pos2); + pos_3=get(f2, 'Position'); + F3=f2; + end + else + if (count==4 || count==7 || count==10) %not the first in the row + ylabel( ylabel_mat{k}); + linkaxes([F1 f2],'x'); %make y axis the same + pos1=get(F1,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(2)=pos1(2) - pos2(4); %move the second so it touches the first + set (f2,'Position',pos2); + F1=f2; + elseif(count==5 || count==8 || count==11) + set(gca, 'yticklabel', []); + linkaxes([F2 f2],'x'); %make y axis the same + pos1=get(F2,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(2)=pos1(2) - pos2(4); %move the second so it touches the first + set (f2,'Position',pos2); + + linkaxes([f1 f2],'y'); %make y axis the same + pos1=get(f1,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(1)=pos1(1) + pos2(3); %move the second so it touches the first + set (f2,'Position',pos2); + F2=f2; + elseif (count==6 || count==9 || count==12) + set(gca, 'yticklabel', []); + linkaxes([F3 f2],'x'); %make y axis the same + pos1=get(F3,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(2)=pos1(2) - pos2(4); %move the second so it touches the first + set (f2,'Position',pos2); + + linkaxes([f1 f2],'y'); %make y axis the same + pos1=get(f1,'Position'); %find the current position [x,y,width,height] + pos2=get(f2,'Position'); %find the current position [x,y,width,height] + pos2(1)=pos1(1) + pos2(3); %move the second so it touches the first + set (f2,'Position',pos2); + pos_2=get(f2, 'Position'); + set (f2,'Position',pos2); + F3=f2; + end + end + f1=f2; + count=count+1; + end +end +%legend ('All', 'Girls', 'Boys', 'Location', 'SouthOutside'); +%all yellow, girls red, boys blue + diff --git a/physical_activity/videogames_boys_girls_2013_CIv3.m b/physical_activity/videogames_boys_girls_2013_CIv3.m new file mode 100644 index 0000000..5fd1437 --- /dev/null +++ b/physical_activity/videogames_boys_girls_2013_CIv3.m @@ -0,0 +1,690 @@ + +clear +cd .. +cd .. +cd data +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +weight=importdata('weights-NaN.txt','\t'); +grade=importdata('grade-NaN.txt','\t'); +[r,c]=size(sex); +weight=weight(3:7,2:c); +race=race(3:7,2:c); +sex=sex(3:7,2:c); +grade=grade(3:7,2:c); +cd .. +cd results_103114 +cd cat +question_mat=importdata('Q72-cat-NaN.txt', '\t'); +[r,c]=size(question_mat); +question_mat=question_mat(:,2:c); +cd .. +cd .. +cd .. +cd programs +cd physical_activity + +conf_mat=cell(59,16); +conf_mat{1,2}='0 hours'; +conf_mat{1,3}='0.5-2 hours'; +conf_mat{1,4}='2+ hours'; + +conf_mat{1,5}='0 hours'; +conf_mat{1,6}='0.5-2 hours'; +conf_mat{1,7}='2+ hours'; + +conf_mat{1,8}='0 hours'; +conf_mat{1,9}='0.5-2 hours'; +conf_mat{1,10}='2+ hours'; + +conf_mat{1,11}='0 hours'; +conf_mat{1,12}='0.5-2 hours'; +conf_mat{1,13}='2+ hours'; + +conf_mat{1,14}='0 hours'; +conf_mat{1,15}='0.5-1 hours'; +conf_mat{1,16}='2+ hours'; + +conf_mat{2,1}='total'; +conf_mat{3,1}='girls'; +conf_mat{4,1}='boys'; +conf_mat{5,1}='Wg'; +conf_mat{6,1}='Wb'; +conf_mat{7,1}='Bg'; +conf_mat{8,1}='Bb'; +conf_mat{9,1}='Hg'; +conf_mat{10,1}='Hb'; +conf_mat{11,1}='Og'; +conf_mat{12,1}='Ob'; +conf_mat{13,1}='W'; +conf_mat{14,1}='B'; +conf_mat{15,1}='H'; +conf_mat{16,1}='O'; +conf_mat{17,1}='9'; +conf_mat{18,1}='10'; +conf_mat{19,1}='11'; +conf_mat{20,1}='12'; +conf_mat{21,1}='9g'; +conf_mat{22,1}='9b'; +conf_mat{23,1}='10g'; +conf_mat{24,1}='10b'; +conf_mat{25,1}='11g'; +conf_mat{26,1}='11b'; +conf_mat{27,1}='12g'; +conf_mat{28,1}='12b'; + +conf_mat{29,1}='H9g'; +conf_mat{30,1}='H10g'; +conf_mat{31,1}='H11g'; +conf_mat{32,1}='H12g'; +conf_mat{33,1}='H9b'; +conf_mat{34,1}='H10b'; +conf_mat{35,1}='H11b'; +conf_mat{36,1}='H12b'; +conf_mat{37,1}='W9g'; +conf_mat{38,1}='W10g'; +conf_mat{39,1}='W11g'; +conf_mat{40,1}='W12g'; +conf_mat{41,1}='W9b'; +conf_mat{42,1}='W10b'; +conf_mat{43,1}='W11b'; +conf_mat{44,1}='W12b'; + +conf_mat{45,1}='B9g'; +conf_mat{46,1}='B10g'; +conf_mat{47,1}='B11g'; +conf_mat{48,1}='B12g'; +conf_mat{49,1}='B9b'; +conf_mat{50,1}='B10b'; +conf_mat{51,1}='B11b'; +conf_mat{52,1}='B12b'; +conf_mat{53,1}='O9g'; +conf_mat{54,1}='O10g'; +conf_mat{55,1}='O11g'; +conf_mat{56,1}='O12g'; +conf_mat{57,1}='O9b'; +conf_mat{58,1}='O10b'; +conf_mat{59,1}='O11b'; +conf_mat{60,1}='O12b'; + +N_MAT=conf_mat; + +n_mat=zeros(59,r); +x_mat=zeros(59,3,r); +n_CI_mat=zeros(59,r); +c=1; + + +for i=1:r +% total(i)=TOTAL(i,1); + for j=1:7 + n=zeros(59,1); + x=zeros(59,1); + + index_yes{i}=find(question_mat(i,:)==j); + index_girls{i}=find(sex(i,:)==1); + index_boys{i}=find(sex(i,:)==2); + index_W{i}=find(race(i,:)== 1 ); + index_B{i}=find(race(i,:)== 2 ); + index_H{i}=find(race(i,:)== 3 ); + index_O{i}=find(race(i,:)== 4 ); + index_9{i}=find(grade(i,:)== 1 ); + index_10{i}=find(grade(i,:)== 2 ); + index_11{i}=find(grade(i,:)== 3 ); + index_12{i}=find(grade(i,:)== 4 ); + + index_W9{i}=intersect(index_9{i},index_W{i}); + index_W10{i}=intersect(index_10{i},index_W{i}); + index_W11{i}=intersect(index_11{i},index_W{i}); + index_W12{i}=intersect(index_12{i},index_W{i}); + + index_B9{i}=intersect(index_9{i},index_B{i}); + index_B10{i}=intersect(index_10{i},index_B{i}); + index_B11{i}=intersect(index_11{i},index_B{i}); + index_B12{i}=intersect(index_12{i},index_B{i}); + + index_H9{i}=intersect(index_9{i},index_H{i}); + index_H10{i}=intersect(index_10{i},index_H{i}); + index_H11{i}=intersect(index_11{i},index_H{i}); + index_H12{i}=intersect(index_12{i},index_H{i}); + + index_O9{i}=intersect(index_9{i},index_O{i}); + index_O10{i}=intersect(index_10{i},index_O{i}); + index_O11{i}=intersect(index_11{i},index_O{i}); + index_O12{i}=intersect(index_12{i},index_O{i}); + + index_missQ{i}=find(question_mat(i,:)== 0); %students who didn't answer the Q + index_nomiss{i}=find(question_mat(i,:)>0); %answers that were NOT missing (ie. 0's and 1's / no's and yes's) + index_noNaN{i}=find(isnan(question_mat(i,:))==0); + + w=weight(i,:)'; + %TOTAL FOR CI + if (j==1) + index_total_b{i}=intersect(index_noNaN{i},index_boys{i}); %index of all boys who answered + index_total_g{i}=intersect(index_noNaN{i},index_girls{i}); %index of all girls who answered + total_ans(i)=nansum(w(index_noNaN{i})); + total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered + total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered + total_W{i}=nansum(w(intersect(index_noNaN{i}, index_W{i}))); %total # of white students who answered + total_B{i}=nansum(w(intersect(index_noNaN{i}, index_B{i}))); %total # of black students who answered + total_H{i}=nansum(w(intersect(index_noNaN{i}, index_H{i}))); %total # of hispanic students who answered + total_O{i}=nansum(w(intersect(index_noNaN{i}, index_O{i}))); %total # of "other" students who answered + + total_w(i)=total_W{i}; + total_b(i)=total_B{i}; + total_h(i)=total_H{i}; + total_o(i)=total_O{i}; + + total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i}))); + total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i}))); + total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i}))); + total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i}))); + total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i}))); + total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i}))); + total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i}))); + total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i}))); + total_9(i)=nansum(w(intersect((index_9{i}),index_noNaN{i}))); + total_10(i)=nansum(w(intersect((index_10{i}),index_noNaN{i}))); + total_11(i)=nansum(w(intersect((index_11{i}),index_noNaN{i}))); + total_12(i)=nansum(w(intersect((index_12{i}),index_noNaN{i}))); + + total_9G(i)=nansum(w(intersect(index_9{i},index_total_g{i}))); + total_10G(i)=nansum(w(intersect(index_10{i},index_total_g{i}))); + total_11G(i)=nansum(w(intersect(index_11{i},index_total_g{i}))); + total_12G(i)=nansum(w(intersect(index_12{i},index_total_g{i}))); + + total_9B(i)=nansum(w(intersect(index_9{i},index_total_b{i}))); + total_10B(i)=nansum(w(intersect(index_10{i},index_total_b{i}))); + total_11B(i)=nansum(w(intersect(index_11{i},index_total_b{i}))); + total_12B(i)=nansum(w(intersect(index_12{i},index_total_b{i}))); + + total_9G_W(i)=nansum(w(intersect(index_W9{i},index_total_g{i}))); + total_9B_W(i)=nansum(w(intersect(index_W9{i},index_total_b{i}))); + total_9G_B(i)=nansum(w(intersect(index_B9{i},index_total_g{i}))); + total_9B_B(i)=nansum(w(intersect(index_B9{i},index_total_b{i}))); + + total_9G_H(i)=nansum(w(intersect(index_H9{i},index_total_g{i}))); + total_9B_H(i)=nansum(w(intersect(index_H9{i},index_total_b{i}))); + total_9G_O(i)=nansum(w(intersect(index_O9{i},index_total_g{i}))); + total_9B_O(i)=nansum(w(intersect(index_O9{i},index_total_b{i}))); + + total_10G_W(i)=nansum(w(intersect(index_W10{i},index_total_g{i}))); + total_10B_W(i)=nansum(w(intersect(index_W10{i},index_total_b{i}))); + total_10G_B(i)=nansum(w(intersect(index_B10{i},index_total_g{i}))); + total_10B_B(i)=nansum(w(intersect(index_B10{i}, index_total_b{i}))); + + total_10G_H(i)=nansum(w(intersect(index_H10{i},index_total_g{i}))); + total_10B_H(i)=nansum(w(intersect(index_H10{i},index_total_b{i}))); + total_10G_O(i)=nansum(w(intersect(index_O10{i},index_total_g{i}))); + total_10B_O(i)=nansum(w(intersect(index_O10{i},index_total_b{i}))); + + total_11G_W(i)=nansum(w(intersect(index_W11{i},index_total_g{i}))); + total_11B_W(i)=nansum(w(intersect(index_W11{i},index_total_b{i}))); + total_11G_B(i)=nansum(w(intersect(index_B11{i},index_total_g{i}))); + total_11B_B(i)=nansum(w(intersect(index_B11{i},index_total_b{i}))); + + total_11G_H(i)=nansum(w(intersect(index_H11{i},index_total_g{i}))); + total_11B_H(i)=nansum(w(intersect(index_H11{i},index_total_b{i}))); + total_11G_O(i)=nansum(w(intersect(index_O11{i},index_total_g{i}))); + total_11B_O(i)=nansum(w(intersect(index_O11{i},index_total_b{i}))); + + total_12G_W(i)=nansum(w(intersect(index_W12{i},index_total_g{i}))); + total_12B_W(i)=nansum(w(intersect(index_W12{i},index_total_b{i}))); + total_12G_B(i)=nansum(w(intersect(index_B12{i},index_total_g{i}))); + total_12B_B(i)=nansum(w(intersect(index_B12{i},index_total_b{i}))); + + total_12G_H(i)=nansum(w(intersect(index_H12{i},index_total_g{i}))); + total_12B_H(i)=nansum(w(intersect(index_H12{i},index_total_b{i}))); + total_12G_O(i)=nansum(w(intersect(index_O12{i},index_total_g{i}))); + total_12B_O(i)=nansum(w(intersect(index_O12{i},index_total_b{i}))); + + total_9_W(i)=nansum(w(intersect(index_noNaN{i},index_W9{i}))); + total_9_B(i)=nansum(w(intersect(index_noNaN{i},index_B9{i}))); + total_9_H(i)=nansum(w(intersect(index_noNaN{i},index_H9{i}))); + total_9_O(i)=nansum(w(intersect(index_noNaN{i},index_O9{i}))); + + total_10_W(i)=nansum(w(intersect(index_noNaN{i},index_W10{i}))); + total_10_B(i)=nansum(w(intersect(index_noNaN{i},index_B10{i}))); + total_10_H(i)=nansum(w(intersect(index_noNaN{i},index_H10{i}))); + total_10_O(i)=nansum(w(intersect(index_noNaN{i},index_O10{i}))); + + total_11_W(i)=nansum(w(intersect(index_noNaN{i},index_W11{i}))); + total_11_B(i)=nansum(w(intersect(index_noNaN{i},index_B11{i}))); + total_11_H(i)=nansum(w(intersect(index_noNaN{i},index_H11{i}))); + total_11_O(i)=nansum(w(intersect(index_noNaN{i},index_O11{i}))); + + total_12_W(i)=nansum(w(intersect(index_noNaN{i},index_W12{i}))); + total_12_B(i)=nansum(w(intersect(index_noNaN{i},index_B12{i}))); + total_12_H(i)=nansum(w(intersect(index_noNaN{i},index_H12{i}))); + total_12_O(i)=nansum(w(intersect(index_noNaN{i},index_O12{i}))); + + n (1)= total_ans(i); + n (2)=total_girls(i); + n (3)=total_boys(i); + n (4)=total_Wg(i); + n (5)=total_Wb(i); + n (6)=total_Bg(i); + n (7)=total_Bb(i); + n (8)=total_Hg(i); + n (9)=total_Hb(i); + n (10)=total_Og(i); + n (11)=total_Ob(i); + n (12)=total_w(i); + n (13)=total_b(i); + n (14)=total_h(i); + n (15)=total_o(i); + n (16)=total_9(i); + n (17)=total_10(i); + n (18)=total_11(i); + n (19)=total_12(i); + n (20)=total_9G(i); + n (21)=total_9B(i); + n (22)=total_10G(i); + n (23)=total_10B(i); + n (24)=total_11G(i); + n (25)=total_11B(i); + n (26)=total_12G(i); + n (27)=total_12B(i); + + n (28)=total_9G_H(i); + n (29)=total_10G_H(i); + n (30)=total_11G_H(i); + n (31)=total_12G_H(i); + n (32)=total_9B_H(i); + n (33)=total_10B_H(i); + n (34)=total_11B_H(i); + n (35)=total_12B_H(i); + + n (36)=total_9G_W(i); + n (37)=total_10G_W(i); + n (38)=total_11G_W(i); + n (39)=total_12G_W(i); + n (40)=total_9B_W(i); + n (41)=total_10B_W(i); + n (42)=total_11B_W(i); + n (43)=total_12B_W(i); + + n (44)=total_9G_B(i); + n (45)=total_10G_B(i); + n (46)=total_11G_B(i); + n (47)=total_12G_B(i); + n (48)=total_9B_B(i); + n (49)=total_10B_B(i); + n (50)=total_11B_B(i); + n (51)=total_12B_B(i); + + n (52)=total_9G_O(i); + n (53)=total_10G_O(i); + n (54)=total_11G_O(i); + n (55)=total_12G_O(i); + n (56)=total_9B_O(i); + n (57)=total_10B_O(i); + n (58)=total_11B_O(i); + n (59)=total_12B_O(i); + + + n_CI_mat(:,i)=n_CI_mat(:,i)+ n; + end + + %FOR PREVALENCE + total_ans(i)=nansum(w(index_nomiss{i})); + index_total_b{i}=intersect(index_nomiss{i},index_boys{i}); %index of all boys who answered + index_total_g{i}=intersect(index_nomiss{i},index_girls{i}); %index of all girls who answered + + total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered + total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered + total_W{i}=nansum(w(intersect(index_nomiss{i}, index_W{i}))); %total # of white students who answered + total_B{i}=nansum(w(intersect(index_nomiss{i}, index_B{i}))); %total # of black students who answered + total_H{i}=nansum(w(intersect(index_nomiss{i}, index_H{i}))); %total # of hispanic students who answered + total_O{i}=nansum(w(intersect(index_nomiss{i}, index_O{i}))); %total # of "other" students who answered + + total_w(i)=total_W{i}; + total_b(i)=total_B{i}; + total_h(i)=total_H{i}; + total_o(i)=total_O{i}; + + total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i}))); + total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i}))); + total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i}))); + total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i}))); + total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i}))); + total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i}))); + total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i}))); + total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i}))); + total_9(i)=nansum(w(intersect((index_9{i}),index_nomiss{i}))); + total_10(i)=nansum(w(intersect((index_10{i}),index_nomiss{i}))); + total_11(i)=nansum(w(intersect((index_11{i}),index_nomiss{i}))); + total_12(i)=nansum(w(intersect((index_12{i}),index_nomiss{i}))); + + total_9G(i)=nansum(w(intersect(index_9{i},index_total_g{i}))); + total_10G(i)=nansum(w(intersect(index_10{i},index_total_g{i}))); + total_11G(i)=nansum(w(intersect(index_11{i},index_total_g{i}))); + total_12G(i)=nansum(w(intersect(index_12{i},index_total_g{i}))); + + total_9B(i)=nansum(w(intersect(index_9{i},index_total_b{i}))); + total_10B(i)=nansum(w(intersect(index_10{i},index_total_b{i}))); + total_11B(i)=nansum(w(intersect(index_11{i},index_total_b{i}))); + total_12B(i)=nansum(w(intersect(index_12{i},index_total_b{i}))); + + total_9G_W(i)=nansum(w(intersect(index_W9{i},index_total_g{i}))); + total_9B_W(i)=nansum(w(intersect(index_W9{i},index_total_b{i}))); + total_9G_B(i)=nansum(w(intersect(index_B9{i},index_total_g{i}))); + total_9B_B(i)=nansum(w(intersect(index_B9{i},index_total_b{i}))); + + total_9G_H(i)=nansum(w(intersect(index_H9{i},index_total_g{i}))); + total_9B_H(i)=nansum(w(intersect(index_H9{i},index_total_b{i}))); + total_9G_O(i)=nansum(w(intersect(index_O9{i},index_total_g{i}))); + total_9B_O(i)=nansum(w(intersect(index_O9{i},index_total_b{i}))); + + total_10G_W(i)=nansum(w(intersect(index_W10{i},index_total_g{i}))); + total_10B_W(i)=nansum(w(intersect(index_W10{i},index_total_b{i}))); + total_10G_B(i)=nansum(w(intersect(index_B10{i},index_total_g{i}))); + total_10B_B(i)=nansum(w(intersect(index_B10{i}, index_total_b{i}))); + + total_10G_H(i)=nansum(w(intersect(index_H10{i},index_total_g{i}))); + total_10B_H(i)=nansum(w(intersect(index_H10{i},index_total_b{i}))); + total_10G_O(i)=nansum(w(intersect(index_O10{i},index_total_g{i}))); + total_10B_O(i)=nansum(w(intersect(index_O10{i},index_total_b{i}))); + + total_11G_W(i)=nansum(w(intersect(index_W11{i},index_total_g{i}))); + total_11B_W(i)=nansum(w(intersect(index_W11{i},index_total_b{i}))); + total_11G_B(i)=nansum(w(intersect(index_B11{i},index_total_g{i}))); + total_11B_B(i)=nansum(w(intersect(index_B11{i},index_total_b{i}))); + + total_11G_H(i)=nansum(w(intersect(index_H11{i},index_total_g{i}))); + total_11B_H(i)=nansum(w(intersect(index_H11{i},index_total_b{i}))); + total_11G_O(i)=nansum(w(intersect(index_O11{i},index_total_g{i}))); + total_11B_O(i)=nansum(w(intersect(index_O11{i},index_total_b{i}))); + + total_12G_W(i)=nansum(w(intersect(index_W12{i},index_total_g{i}))); + total_12B_W(i)=nansum(w(intersect(index_W12{i},index_total_b{i}))); + total_12G_B(i)=nansum(w(intersect(index_B12{i},index_total_g{i}))); + total_12B_B(i)=nansum(w(intersect(index_B12{i},index_total_b{i}))); + + total_12G_H(i)=nansum(w(intersect(index_H12{i},index_total_g{i}))); + total_12B_H(i)=nansum(w(intersect(index_H12{i},index_total_b{i}))); + total_12G_O(i)=nansum(w(intersect(index_O12{i},index_total_g{i}))); + total_12B_O(i)=nansum(w(intersect(index_O12{i},index_total_b{i}))); + + total_9_W(i)=nansum(w(intersect(index_nomiss{i},index_W9{i}))); + total_9_B(i)=nansum(w(intersect(index_nomiss{i},index_B9{i}))); + total_9_H(i)=nansum(w(intersect(index_nomiss{i},index_H9{i}))); + total_9_O(i)=nansum(w(intersect(index_nomiss{i},index_O9{i}))); + + total_10_W(i)=nansum(w(intersect(index_nomiss{i},index_W10{i}))); + total_10_B(i)=nansum(w(intersect(index_nomiss{i},index_B10{i}))); + total_10_H(i)=nansum(w(intersect(index_nomiss{i},index_H10{i}))); + total_10_O(i)=nansum(w(intersect(index_nomiss{i},index_O10{i}))); + + total_11_W(i)=nansum(w(intersect(index_nomiss{i},index_W11{i}))); + total_11_B(i)=nansum(w(intersect(index_nomiss{i},index_B11{i}))); + total_11_H(i)=nansum(w(intersect(index_nomiss{i},index_H11{i}))); + total_11_O(i)=nansum(w(intersect(index_nomiss{i},index_O11{i}))); + + total_12_W(i)=nansum(w(intersect(index_nomiss{i},index_W12{i}))); + total_12_B(i)=nansum(w(intersect(index_nomiss{i},index_B12{i}))); + total_12_H(i)=nansum(w(intersect(index_nomiss{i},index_H12{i}))); + total_12_O(i)=nansum(w(intersect(index_nomiss{i},index_O12{i}))); + if (j==1) + n (1)= total_ans(i); + n (2)=total_girls(i); + n (3)=total_boys(i); + n (4)=total_Wg(i); + n (5)=total_Wb(i); + n (6)=total_Bg(i); + n (7)=total_Bb(i); + n (8)=total_Hg(i); + n (9)=total_Hb(i); + n (10)=total_Og(i); + n (11)=total_Ob(i); + n (12)=total_w(i); + n (13)=total_b(i); + n (14)=total_h(i); + n (15)=total_o(i); + n (16)=total_9(i); + n (17)=total_10(i); + n (18)=total_11(i); + n (19)=total_12(i); + n (20)=total_9G(i); + n (21)=total_9B(i); + n (22)=total_10G(i); + n (23)=total_10B(i); + n (24)=total_11G(i); + n (25)=total_11B(i); + n (26)=total_12G(i); + n (27)=total_12B(i); + + n (28)=total_9G_H(i); + n (29)=total_10G_H(i); + n (30)=total_11G_H(i); + n (31)=total_12G_H(i); + n (32)=total_9B_H(i); + n (33)=total_10B_H(i); + n (34)=total_11B_H(i); + n (35)=total_12B_H(i); + + n (36)=total_9G_W(i); + n (37)=total_10G_W(i); + n (38)=total_11G_W(i); + n (39)=total_12G_W(i); + n (40)=total_9B_W(i); + n (41)=total_10B_W(i); + n (42)=total_11B_W(i); + n (43)=total_12B_W(i); + + n (44)=total_9G_B(i); + n (45)=total_10G_B(i); + n (46)=total_11G_B(i); + n (47)=total_12G_B(i); + n (48)=total_9B_B(i); + n (49)=total_10B_B(i); + n (50)=total_11B_B(i); + n (51)=total_12B_B(i); + + n (52)=total_9G_O(i); + n (53)=total_10G_O(i); + n (54)=total_11G_O(i); + n (55)=total_12G_O(i); + n (56)=total_9B_O(i); + n (57)=total_10B_O(i); + n (58)=total_11B_O(i); + n (59)=total_12B_O(i); + + + n_mat(:,i)=n_mat(:,i)+ n; + end + + index_yesgirls{i}=intersect(index_yes{i},index_girls{i}); + index_yesboys{i}=intersect(index_yes{i},index_boys{i}); + yes_girls(i)=nansum(w(index_yesgirls{i})); + yes_boys(i)=nansum(w(index_yesboys{i})); + total_yes(i)=nansum(w(index_yes{i})); + + yes_W(i)=nansum(w(intersect(index_yes{i}, index_W{i}))); + yes_B(i)=nansum(w(intersect(index_yes{i}, index_B{i}))); + yes_H(i)=nansum(w(intersect(index_yes{i}, index_H{i}))); + yes_O(i)=nansum(w(intersect(index_yes{i}, index_O{i}))); + yes_WG(i)=nansum(w(intersect(index_yesgirls{i},index_W{i}))); + yes_BG(i)=nansum(w(intersect(index_yesgirls{i},index_B{i}))); + yes_HG(i)=nansum(w(intersect(index_yesgirls{i},index_H{i}))); + yes_OG(i)=nansum(w(intersect(index_yesgirls{i},index_O{i}))); + yes_WB(i)=nansum(w(intersect(index_yesboys{i},index_W{i}))); + yes_BB(i)=nansum(w(intersect(index_yesboys{i},index_B{i}))); + yes_HB(i)=nansum(w(intersect(index_yesboys{i},index_H{i}))); + yes_OB(i)=nansum(w(intersect(index_yesboys{i},index_O{i}))); + yes_9(i)=nansum(w(intersect(index_yes{i},index_9{i}))); + yes_10(i)=nansum(w(intersect(index_yes{i},index_10{i}))); + yes_11(i)=nansum(w(intersect(index_yes{i},index_11{i}))); + yes_12(i)=nansum(w(intersect(index_yes{i},index_12{i}))); + yes_9b(i)=nansum(w(intersect(index_yesboys{i},index_9{i}))); + yes_10b(i)=nansum(w(intersect(index_yesboys{i},index_10{i}))); + yes_11b(i)=nansum(w(intersect(index_yesboys{i},index_11{i}))); + yes_12b(i)=nansum(w(intersect(index_yesboys{i},index_12{i}))); + yes_9g(i)=nansum(w(intersect(index_yesgirls{i},index_9{i}))); + yes_10g(i)=nansum(w(intersect(index_yesgirls{i},index_10{i}))); + yes_11g(i)=nansum(w(intersect(index_yesgirls{i},index_11{i}))); + yes_12g(i)=nansum(w(intersect(index_yesgirls{i},index_12{i}))); + + + yes_9WB(i)=nansum(w(intersect(index_yesboys{i},index_W9{i}))); + yes_10WB(i)=nansum(w(intersect(index_yesboys{i},index_W10{i}))); + yes_11WB(i)=nansum(w(intersect(index_yesboys{i},index_W11{i}))); + yes_12WB(i)=nansum(w(intersect(index_yesboys{i},index_W12{i}))); + yes_9WG(i)=nansum(w(intersect(index_yesgirls{i},index_W9{i}))); + yes_10WG(i)=nansum(w(intersect(index_yesgirls{i},index_W10{i}))); + yes_11WG(i)=nansum(w(intersect(index_yesgirls{i},index_W11{i}))); + yes_12WG(i)=nansum(w(intersect(index_yesgirls{i},index_W12{i}))); + + yes_9W(i)=nansum(w(intersect(index_yes{i},index_W9{i}))); + yes_10W(i)=nansum(w(intersect(index_yes{i},(index_W10{i})))); + yes_11W(i)=nansum(w(intersect(index_yes{i},(index_W11{i})))); + yes_12W(i)=nansum(w(intersect(index_yes{i},(index_W12{i})))); + + yes_9B(i)=nansum(w(intersect(index_yes{i},(index_B9{i})))); + yes_10B(i)=nansum(w(intersect(index_yes{i},(index_B10{i})))); + yes_11B(i)=nansum(w(intersect(index_yes{i},(index_B11{i})))); + yes_12B(i)=nansum(w(intersect(index_yes{i},(index_B12{i})))); + + yes_9H(i)=nansum(w(intersect(index_yes{i},(index_H9{i})))); + yes_10H(i)=nansum(w(intersect(index_yes{i},(index_H10{i})))); + yes_11H(i)=nansum(w(intersect(index_yes{i},(index_H11{i})))); + yes_12H(i)=nansum(w(intersect(index_yes{i},(index_H12{i})))); + + yes_9O(i)=nansum(w(intersect(index_yes{i},(index_O9{i})))); + yes_10O(i)=nansum(w(intersect(index_yes{i},(index_O10{i})))); + yes_11O(i)=nansum(w(intersect(index_yes{i},(index_O11{i})))); + yes_12O(i)=nansum(w(intersect(index_yes{i},(index_O12{i})))); + + yes_9BB(i)=nansum(w(intersect(index_yesboys{i},index_B9{i}))); + yes_10BB(i)=nansum(w(intersect(index_yesboys{i},index_B10{i}))); + yes_11BB(i)=nansum(w(intersect(index_yesboys{i},index_B11{i}))); + yes_12BB(i)=nansum(w(intersect(index_yesboys{i},index_B12{i}))); + yes_9BG(i)=nansum(w(intersect(index_yesgirls{i},index_B9{i}))); + yes_10BG(i)=nansum(w(intersect(index_yesgirls{i},index_B10{i}))); + yes_11BG(i)=nansum(w(intersect(index_yesgirls{i},index_B11{i}))); + yes_12BG(i)=nansum(w(intersect(index_yesgirls{i},index_B12{i}))); + + yes_9HB(i)=nansum(w(intersect(index_yesboys{i},index_H9{i}))); + yes_10HB(i)=nansum(w(intersect(index_yesboys{i},index_H10{i}))); + yes_11HB(i)=nansum(w(intersect(index_yesboys{i},index_H11{i}))); + yes_12HB(i)=nansum(w(intersect(index_yesboys{i},index_H12{i}))); + yes_9HG(i)=nansum(w(intersect(index_yesgirls{i},index_H9{i}))); + yes_10HG(i)=nansum(w(intersect(index_yesgirls{i},index_H10{i}))); + yes_11HG(i)=nansum(w(intersect(index_yesgirls{i},index_H11{i}))); + yes_12HG(i)=nansum(w(intersect(index_yesgirls{i},index_H12{i}))); + + yes_9OB(i)=nansum(w(intersect(index_yesboys{i},index_O9{i}))); + yes_10OB(i)=nansum(w(intersect(index_yesboys{i},index_O10{i}))); + yes_11OB(i)=nansum(w(intersect(index_yesboys{i},index_O11{i}))); + yes_12OB(i)=nansum(w(intersect(index_yesboys{i},index_O12{i}))); + yes_9OG(i)=nansum(w(intersect(index_yesgirls{i},index_O9{i}))); + yes_10OG(i)=nansum(w(intersect(index_yesgirls{i},index_O10{i}))); + yes_11OG(i)=nansum(w(intersect(index_yesgirls{i},index_O11{i}))); + yes_12OG(i)=nansum(w(intersect(index_yesgirls{i},index_O12{i}))); + + x(1)=total_yes(i); + x(2)=yes_girls(i); + x(3)=yes_boys(i); + x(4)=yes_WG(i); + x(5)=yes_WB(i); + x(6)=yes_BG(i); + x(7)=yes_BB(i); + x(8)=yes_HG(i); + x(9)=yes_HB(i); + x(10)=yes_OG(i); + x(11)=yes_OB(i); + x(12)=yes_W(i); + x(13)=yes_B(i); + x(14)=yes_H(i); + x(15)=yes_O(i); + x(16)=yes_9(i); + x(17)=yes_10(i); + x(18)=yes_11(i); + x(19)=yes_12(i); + x(20)=yes_9g(i); + x(21)=yes_9b(i); + x(22)=yes_10g(i); + x(23)=yes_10b(i); + x(24)=yes_11g(i); + x(25)=yes_11b(i); + x(26)=yes_12g(i); + x(27)=yes_12b(i); + + x(28)=yes_9HG(i); + x(29)=yes_10HG(i); + x(30)=yes_11HG(i); + x(31)=yes_12HG(i); + x(32)=yes_9HB(i); + x(33)=yes_10HB(i); + x(34)=yes_11HB(i); + x(35)=yes_12HB(i); + + x(36)=yes_9WG(i); + x(37)=yes_10WG(i); + x(38)=yes_11WG(i); + x(39)=yes_12WG(i); + x(40)=yes_9WB(i); + x(41)=yes_10WB(i); + x(42)=yes_11WB(i); + x(43)=yes_12WB(i); + + x(44)=yes_9BG(i); + x(45)=yes_10BG(i); + x(46)=yes_11BG(i); + x(47)=yes_12BG(i); + x(48)=yes_9BB(i); + x(49)=yes_10BB(i); + x(50)=yes_11BB(i); + x(51)=yes_12BB(i); + + x(52)=yes_9OG(i); + x(53)=yes_10OG(i); + x(54)=yes_11OG(i); + x(55)=yes_12OG(i); + x(56)=yes_9OB(i); + x(57)=yes_10OB(i); + x(58)=yes_11OB(i); + x(59)=yes_12OB(i); + + if (j==1) + x_mat(:,1,i)=x_mat(:,1,i)+x; + elseif (j>1 && j<5) %1-20 times + x_mat(:,2, i)=x_mat(:,2, i)+x; + elseif (j>4); + x_mat(:,3, i)=x_mat(:,3, i)+x; + end + + end +end + +%%confidence interval + +lower_mat=double.empty; +upper_mat=double.empty; +plot_mat=double.empty; +z=1.96; +for k=1:59 + count=2; + for i=1:r + n=n_mat(k,i); + n_CI=n_CI_mat(k,i); + for j=1:3 + x=x_mat(k,j,i); %x_mat=zeros(59,5,r); + p=x/n_CI; %x is the number of subjects saying "yes", n is the total subjects + P=x/n; + upper=((P+z*sqrt(p*(1-p)/n_CI))*100); + lower=((P-z*sqrt(p*(1-p)/n_CI))*100); + lower_mat(k,count-1)=lower; + upper_mat(k,count-1)=upper; + upper=sprintf('%0.1f',round(upper*10)/10); + lower=sprintf('%0.1f',round(lower*10)/10); + P=P*100; + plot_mat(k,count-1)=P; + p_num=sprintf('%0.1f', round(P*10)/10); + conf_mat{k+1,count}=[p_num ' [' lower ', ' upper ']']; + N_MAT{k+1,count}=x; + count=count+1; + end + end +end diff --git a/raw_odds_ratio/create_raw_OR_plot_2013_mental_health.m b/raw_odds_ratio/create_raw_OR_plot_2013_mental_health.m new file mode 100644 index 0000000..daa689d --- /dev/null +++ b/raw_odds_ratio/create_raw_OR_plot_2013_mental_health.m @@ -0,0 +1,176 @@ +%make heatmaps out of the relative risk matrix +cd .. +cd .. +cd matrices +load OR_2013_HISPANIC_GIRLS.mat +or_all=odds_ratio_cell; +load qlabel_090914.mat +load OR_2013_HISPANIC_GIRLS +or_HG=odds_ratio_cell; +cd .. +xlab={'2001', '2003', '2005', '2007', '2009', '2011', '2013'}; + +purple_grey=[99/255 86/255 136/255]; +light_purple=[106/255 90/255 205/255]; +green_blue=[67/255 205/255 128/255]; +green=[0/255 205/255 102/255]; +orange=[255/255 127/255 0/255]; +blue=[24/255 116/255 205/255]; +salmon=[205/255 51/255 51/255]; +mauve=[205/255 96/255 144/255]; +magenta=[205/255 41/255 144/255]; +grey1=[192/255 192/255 192/255]; +grey2=[97/255 97/255 97/255]; + +ques=input ('Enter in the question number you want to use (ex. Q01): ', 's'); +for i=1:82 + i_char=num2str(i); + q1_=i_char; + num2=i; + if length(i_char)<2 + i_char=['0' i_char]; + end + q1=['Q' i_char]; + if strcmp(q1,ques)==1 + indx_all=find (strcmp(or_all(:,1),q1)==1); + indx_HG=find(strcmp(or_HG(:,1),q1)==1); + if isempty(indx_all)==0 && isempty(indx_HG)==0 + lab=or_all(indx_all,2); + P=or_all(indx_all,3:9); + P2=cell.empty; + qlabel2=cell.empty; + [rl,cl]=size(qlabel); + counter=1; + for j=1:rl + indx=find(strcmp(qlabel{j,2},lab)==1); + if numel(indx)>0 + P2(counter,:)=P(indx,:); + qlabel2(counter,:)=qlabel(j,:); + counter=counter+1; + end + + end + %replace NaN with -10000 + indx=find(strcmp(P2,'NaN')==1); + for j=1:numel(indx) + P2{indx(j)}=-10000; + end + indx=find(strcmp(P2,'Inf')==1); + for j=1:numel(indx) + P2{indx(j)}=10000; + end + emptycells=cellfun(@isempty, P2); + [r,c]=size(emptycells); + for j=1:r + for k=1:c + if (emptycells(j,k)==1) + P2{j,k}=-10000; + end + end + end + plot_mat=cell2mat(P2); + plot_mat(plot_mat==-10000)=NaN; + plot_mat(plot_mat==10000)=NaN; + + q2=or_all(indx,2); + q2_=cell(length(q2)-1,1); + for p=1:length(q2)-1 + s=q2{p,1}; + q2_{p,1}=s(2:3); + end + plot_mat=rot90(plot_mat); + plot_mat=flipud(plot_mat); + indx=find(isinf(plot_mat(1,:))==0); + plot_mat_all=plot_mat(:,indx); + qlabel_all=qlabel2(indx,:); + + %hispanic girls matrix ----------------------- + lab=or_HG(indx_HG,2); + P=or_HG(indx_HG,3:9); + P2=cell.empty; + qlabel2=cell.empty; + [rl,cl]=size(qlabel); + counter=1; + for j=1:rl + indx=find(strcmp(qlabel{j,2},lab)==1); + if numel(indx)>0 + P2(counter,:)=P(indx,:); + qlabel2(counter,:)=qlabel(j,:); + counter=counter+1; + end + + end + %replace NaN with -10000 + indx=find(strcmp(P2,'NaN')==1); + for j=1:numel(indx) + P2{indx(j)}=-10000; + end + indx=find(strcmp(P2,'Inf')==1); + for j=1:numel(indx) + P2{indx(j)}=10000; + end + emptycells=cellfun(@isempty, P2); + [r,c]=size(emptycells); + for j=1:r + for k=1:c + if (emptycells(j,k)==1) + P2{j,k}=-10000; + end + end + end + plot_mat=cell2mat(P2); + plot_mat(plot_mat==-10000)=NaN; + plot_mat(plot_mat==10000)=NaN; + + q2=or_HG(indx,2); + q2_=cell(length(q2)-1,1); + for p=1:length(q2)-1 + s=q2{p,1}; + q2_{p,1}=s(2:3); + end + plot_mat=rot90(plot_mat); + plot_mat=flipud(plot_mat); + indx=find(isinf(plot_mat)==1); + plot_mat(indx)=0; + max_=max(max(plot_mat_all)); + max2=max(max(plot_mat)); + max_final=max(max_, max2)+10; + if (max_final<20) + max_final=20; + end + plot_mat(indx)=max_final; + plot_mat_HG=plot_mat; + qlabel_HG=qlabel2; + + + + [r,c]=size(plot_mat_all); + x=1; + for k=1:c + lab=qlabel_all{k,2}; + indx_lab=find(strcmp(qlabel_HG(:,2), lab)==1); + for j=1:r + plot(x, plot_mat_all(j,k),'Marker','o', 'MarkerSize', 2, 'Color', blue, 'MarkerFaceColor', blue ); + hold on + end + x=x+1; + for j=1:r + plot(x, plot_mat_HG(j, indx_lab),'Marker','o', 'MarkerSize', 2, 'Color', magenta , 'MarkerFaceColor', magenta ); + end + x=x+1; + x2=[x x]; + y2=[0 max_final+50]; + plot(x2, y2, '-k'); + x=x+1; + end + end + hold off + set(gca,'XTick', 1:3:x-1); + set(gca, 'XTicklabel', qlabel_all(:,1)); + xlim([0 x-1]); + ylim([0 max_final+10]); + print (gcf, '-dpdf', [ques '_raw_OR_metnalhealth_hispanic_girls']); + saveas(gcf, [ques '_raw_OR_metnalhealth_hispanic_girls.fig']); + close + end +end diff --git a/raw_odds_ratio/create_raw_OR_plot_2013v2.m b/raw_odds_ratio/create_raw_OR_plot_2013v2.m new file mode 100644 index 0000000..c1a859c --- /dev/null +++ b/raw_odds_ratio/create_raw_OR_plot_2013v2.m @@ -0,0 +1,185 @@ +%make heatmaps out of the relative risk matrix +cd .. +cd .. +cd matrices +load OR_2013_110314.mat +or_all=odds_ratio_cell; +load qlabel_090914.mat +load OR_2013_HISPANIC_GIRLS +or_HG=odds_ratio_cell; +cd .. +xlab={'2001', '2003', '2005', '2007', '2009', '2011', '2013'}; + +purple_grey=[99/255 86/255 136/255]; +light_purple=[106/255 90/255 205/255]; +green_blue=[67/255 205/255 128/255]; +green=[0/255 205/255 102/255]; +orange=[255/255 127/255 0/255]; +blue=[24/255 116/255 205/255]; +salmon=[205/255 51/255 51/255]; +mauve=[205/255 96/255 144/255]; +magenta=[205/255 41/255 144/255]; +grey1=[192/255 192/255 192/255]; +grey2=[97/255 97/255 97/255]; + +ques=input ('Enter in the question number you want to use (ex. Q01): ', 's'); +for i=1:82 + i_char=num2str(i); + q1_=i_char; + num2=i; + if length(i_char)<2 + i_char=['0' i_char]; + end + q1=['Q' i_char]; + if strcmp(q1,ques)==1 + indx_all=find (strcmp(or_all(:,1),q1)==1); + indx_HG=find(strcmp(or_HG(:,1),q1)==1); + if isempty(indx_all)==0 && isempty(indx_HG)==0 + lab=or_all(indx_all,2); + P=or_all(indx_all,3:9); + P2=cell.empty; + qlabel2=cell.empty; + [rl,cl]=size(qlabel); + counter=1; + for j=1:rl + indx=find(strcmp(qlabel{j,2},lab)==1); + if numel(indx)>0 + P2(counter,:)=P(indx,:); + qlabel2(counter,:)=qlabel(j,:); + counter=counter+1; + end + + end + %replace NaN with -10000 + indx=find(strcmp(P2,'NaN')==1); + for j=1:numel(indx) + P2{indx(j)}=-10000; + end + indx=find(strcmp(P2,'Inf')==1); + for j=1:numel(indx) + P2{indx(j)}=10000; + end + emptycells=cellfun(@isempty, P2); + [r,c]=size(emptycells); + for j=1:r + for k=1:c + if (emptycells(j,k)==1) + P2{j,k}=-10000; + end + end + end + plot_mat=cell2mat(P2); + plot_mat(plot_mat==-10000)=NaN; + plot_mat(plot_mat==10000)=NaN; + + q2=or_all(indx,2); + q2_=cell(length(q2)-1,1); + for p=1:length(q2)-1 + s=q2{p,1}; + q2_{p,1}=s(2:3); + end + plot_mat=rot90(plot_mat); + plot_mat=flipud(plot_mat); + indx=find(isinf(plot_mat(1,:))==0); + plot_mat_all=plot_mat(:,indx); + qlabel_all=qlabel2(indx,:); + + %hispanic girls matrix ----------------------- + lab=or_HG(indx_HG,2); + P=or_HG(indx_HG,3:9); + P2=cell.empty; + qlabel2=cell.empty; + [rl,cl]=size(qlabel); + counter=1; + for j=1:rl + indx=find(strcmp(qlabel{j,2},lab)==1); + if numel(indx)>0 + P2(counter,:)=P(indx,:); + qlabel2(counter,:)=qlabel(j,:); + counter=counter+1; + end + + end + %replace NaN with -10000 + indx=find(strcmp(P2,'NaN')==1); + for j=1:numel(indx) + P2{indx(j)}=-10000; + end + indx=find(strcmp(P2,'Inf')==1); + for j=1:numel(indx) + P2{indx(j)}=10000; + end + emptycells=cellfun(@isempty, P2); + [r,c]=size(emptycells); + for j=1:r + for k=1:c + if (emptycells(j,k)==1) + P2{j,k}=-10000; + end + end + end + plot_mat=cell2mat(P2); + plot_mat(plot_mat==-10000)=NaN; + plot_mat(plot_mat==10000)=NaN; + + q2=or_HG(indx,2); + q2_=cell(length(q2)-1,1); + for p=1:length(q2)-1 + s=q2{p,1}; + q2_{p,1}=s(2:3); + end + plot_mat=rot90(plot_mat); + plot_mat=flipud(plot_mat); + indx=find(isinf(plot_mat)==1); + plot_mat(indx)=0; + max_=max(max(plot_mat_all)); + max2=max(max(plot_mat)); + max_final=max(max_, max2)+10; + if (max_final<20) + max_final=20; + end + plot_mat(indx)=max_final; + plot_mat_HG=plot_mat; + qlabel_HG=qlabel2; + + + + [r,c]=size(plot_mat_all); + x=1; + for k=1:c + lab=qlabel_all{k,2}; + indx_lab=find(strcmp(qlabel_HG(:,2), lab)==1); + for j=1:r + plot(x, plot_mat_all(j,k),'Marker','o', 'MarkerSize', 2, 'Color', blue, 'MarkerFaceColor', blue ); + hold on + end + x=x+1; + for j=1:r + plot(x, plot_mat_HG(j, indx_lab),'Marker','o', 'MarkerSize', 2, 'Color', magenta , 'MarkerFaceColor', magenta ); + end + x=x+1; + x2=[x x]; + y2=[0 max_final+50]; + plot(x2, y2, '-k'); + x=x+1; + end + end + hold off + set(gca,'XTick', 1:3:x-1); + set(gca, 'XTicklabel', qlabel_all(:,1)); + xlim([0 x-1]); + ylim([0 max_final+10]); + print (gcf, '-dpdf', [ques '_raw_OR_all_vs_hispanic_girlsv2']); + saveas(gcf, [ques '_raw_OR_all_vs_hispanic_girlsv2.fig']); + close + end +end + +% cd programs +% cd raw_odds_ratio +% + +% plot_mat3=rot90(plot_mat2); +% plot_mat3=rot90(plot_mat3); +% plot_mat3=rot90(plot_mat3); +% plot_mat3=fliplr(plot_mat3); \ No newline at end of file diff --git a/raw_odds_ratio/raw_OR_highrisk_mentalhealth.m b/raw_odds_ratio/raw_OR_highrisk_mentalhealth.m new file mode 100644 index 0000000..c4ca9bb --- /dev/null +++ b/raw_odds_ratio/raw_OR_highrisk_mentalhealth.m @@ -0,0 +1,191 @@ +%make heatmaps out of the relative risk matrix +lab='hispanic_girls'; +years={'2003', '2005', '2007', '2009', '2011', '2013'}; +cd .. +cd .. +cd matrices +load OR_2013_HISPANIC_GIRLS_MentalHealth +load qlabel_090914.mat +load order_090914.mat +cd .. +cd programs +cd clustergrams + +xlab={'2003', '2005', '2007', '2009', '2011', '2013'}; + +P2=odds_ratio_cell(:,4:9); +%replace NaN with -10000 +indx=find(strcmp(P2,'NaN')==1); +for j=1:numel(indx) + P2{indx(j)}=-10000; +end +indx=find(strcmp(P2,'Inf')==1); +for j=1:numel(indx) + P2{indx(j)}=10000; +end +emptycells=cellfun(@isempty, P2); +[r,c]=size(emptycells); +for j=1:r + for k=1:c + if (emptycells(j,k)==1) + P2{j,k}=-10000; + end + end +end +plot_mat=cell2mat(P2); +plot_mat(plot_mat==-10000)=NaN; +plot_mat(plot_mat==10000)=NaN; + +q2=odds_ratio_cell(:,2); +q2_=cell(length(q2)-1,1); +for p=1:length(q2)-1 + s=q2{p,1}; + q2_{p,1}=s(2:3); +end + +plot_mat=rot90(plot_mat); +plot_mat=flipud(plot_mat); +indx=find(isinf(plot_mat(1,:))==0); +plot_mat2=plot_mat(:,indx); +qlabel2=qlabel(indx,:); +[r,c]=size(plot_mat2); +x=[2003 2005 2007 2009 2011 2013]; +for j=1:c + if min(plot_mat2(:,j))<1 + color=[97/255 97/255 97/255]; %grey2 + elseif min(plot_mat2(:,j))<2 + color=[24/255 116/255 205/255]; %blue + elseif min(plot_mat2(:,j))<5 + color=[0/255 205/255 102/255]; %green + elseif min(plot_mat2(:,j))<10 + color=[255/255 127/255 0/255]; %orange + else + color=[205/255 0/255 0/255]; %red + end + plot(x, plot_mat2(:,j),'Marker','o', 'MarkerFaceColor', color, 'MarkerSize', 2, 'Color', color ); + hold on +end +hold off +set(gca,'XTick', [2003 2005 2007 2009 2011 2013]); +xlim([2003 2013]); + +print (gcf, '-dpng', 'raw_OR_mentalhealth_HG'); +plot_mat3=rot90(plot_mat2); +plot_mat3=rot90(plot_mat3); +plot_mat_HG=rot90(plot_mat3); +close all + +%ALL!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +years={'2003', '2005', '2007', '2009', '2011', '2013'}; +cd .. +cd .. +cd matrices +load OR_2013_ALL_MentalHealth +load qlabel_090914.mat +load order_090914.mat +cd .. +cd programs +cd clustergrams + +xlab={'2003', '2005', '2007', '2009', '2011', '2013'}; + +P2=odds_ratio_cell(:,4:9); +%replace NaN with -10000 +indx=find(strcmp(P2,'NaN')==1); +for j=1:numel(indx) + P2{indx(j)}=-10000; +end +indx=find(strcmp(P2,'Inf')==1); +for j=1:numel(indx) + P2{indx(j)}=10000; +end +emptycells=cellfun(@isempty, P2); +[r,c]=size(emptycells); +for j=1:r + for k=1:c + if (emptycells(j,k)==1) + P2{j,k}=-10000; + end + end +end +plot_mat=cell2mat(P2); +plot_mat(plot_mat==-10000)=NaN; +plot_mat(plot_mat==10000)=NaN; + +q2=odds_ratio_cell(:,2); +q2_=cell(length(q2)-1,1); +for p=1:length(q2)-1 + s=q2{p,1}; + q2_{p,1}=s(2:3); +end + +plot_mat=rot90(plot_mat); +plot_mat=flipud(plot_mat); +indx=find(isinf(plot_mat(1,:))==0); +plot_mat2=plot_mat(:,indx); +qlabel2=qlabel(indx,:); +[r,c]=size(plot_mat2); +x=[2003 2005 2007 2009 2011 2013]; +for j=1:c + if min(plot_mat2(:,j))<1 + color=[97/255 97/255 97/255]; %grey2 + elseif min(plot_mat2(:,j))<2 + color=[24/255 116/255 205/255]; %blue + elseif min(plot_mat2(:,j))<5 + color=[0/255 205/255 102/255]; %green + elseif min(plot_mat2(:,j))<10 + color=[255/255 127/255 0/255]; %orange + else + color=[205/255 0/255 0/255]; %red + end + plot(x, plot_mat2(:,j),'Marker','o', 'MarkerFaceColor', color, 'MarkerSize', 2, 'Color', color ); + hold on +end +hold off +set(gca,'XTick', [2003 2005 2007 2009 2011 2013]); +xlim([2003 2013]); + +print (gcf, '-dpng', 'raw_OR_mentalhealth_all'); +plot_mat3=rot90(plot_mat2); +plot_mat3=rot90(plot_mat3); +plot_mat3=rot90(plot_mat3); + +%make a combination plot with both all and hispanic girls --------------- +%change the ranking based on most to least +median_OR=nanmedian(plot_mat_HG,2); +[r,c]=size(plot_mat_HG); +count=1; +plot_mat_HG_=nan(r,c); +plot_mat3_=nan(r,c); +qlabels_=qlabel2; +for i=1:r + max_=max(median_OR); + indx=find(median_OR==max_); + median_OR(indx)=0; + for j=1:numel(indx) + plot_mat_HG_(count,:)=plot_mat_HG(indx(j),:); + plot_mat3_(count,:)=plot_mat3(indx(j),:); + qlabels_(count,:)=qlabel2(indx(j),:); + count=count+1; + end +end + +[r,c]=size(plot_mat3_); +n=1; +for i=1:r + x=[n n n n n n]; + scatter(x, plot_mat3_(i,:),7, 'b', 'fill'); + hold on + n=n+1; x=[n n n n n n]; + scatter(x, plot_mat_HG_(i,:),7, 'm', 'fill'); + n=n+1; + x=[n n]; + y=[0 100]; + plot (x,y, '-k'); + n=n+1; +end +ylim([0 50]); +xticks_=1.5:3:r*3; +set(gca, 'xtick', xticks_); +set(gca, 'xticklabel', qlabels_(:,1)); +rotateXLabels(gca,90) diff --git a/statistics/check_missing_data.m b/statistics/check_missing_data.m index 5a2a3a5..6382ceb 100644 --- a/statistics/check_missing_data.m +++ b/statistics/check_missing_data.m @@ -1,9 +1,12 @@ k=1; %counter for rows in rel_risk_cell and odds_ratio_cell -files1=dir(fullfile('C:','Users','kruggles7','Documents','MATLAB', 'Rajan','NaN_results', '*.txt')); -load quest_55.mat -cd NaN_results -missing=double.empty; +files1=dir(fullfile('C:','Users','kruggles7','Dropbox (Personal)','CDC', 'data', 'results_103114','NaN', '*.txt')); +cd .. +cd .. +cd data +cd results_103114 +cd NaN +missing=cell.empty; P=length(files1); for p=1:P quest_1=importdata(files1(p).name, '\t'); @@ -22,17 +25,14 @@ end end ct=str2num(ct); - indx=find(quest_55==ct); - if numel(indx)>0 - [r,c]=size(quest_1); - missing(k,1)=ct; - for i=1:r - indx_t=find(isnan(quest_1(i,2:c))~=1); - total=numel(indx_t); - indx_m=find(quest_1(i,2:c)==9); - miss=numel(indx_m); - missing(k,i+1)=(miss/total)*100; - end - k=k+1; + [r,c]=size(quest_1); + missing{k,1}=filename1; + for i=1:r + indx_t=find(isnan(quest_1(i,2:c))==0); + total=numel(indx_t); + indx_m=find(quest_1(i,2:c)==9); + miss=numel(indx_m); + missing{k,i+1}=(miss/total)*100; end + k=k+1; end \ No newline at end of file diff --git a/statistics/make_SPSS_input.m b/statistics/make_SPSS_input.m new file mode 100644 index 0000000..5cca2c2 --- /dev/null +++ b/statistics/make_SPSS_input.m @@ -0,0 +1,68 @@ +cd .. +cd .. +cd data +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +weight=importdata('weights-NaN.txt','\t'); +grade=importdata('grade-NaN.txt','\t'); +cd .. +cd results_103114 + +%PO Q79 +%Heroin %Q28 +%Injection Drugs %Q31 +cat_mat=double.empty; +NaN_mat=double.empty; +start_year=5; +end_year=7; +title={'year', 'weight', 'race', 'sex', 'grade'}; +Q={'Q79' 'Q28', 'Q31'}; +[r,c]=size(sex); +weight=weight(start_year:end_year,2:c); +race=race(start_year:end_year,2:c); +sex=sex(start_year:end_year,2:c); +grade=grade(start_year:end_year,2:c); + +for j=1:numel(Q) + ques=Q{j}; + cd cat + title{5+j}=ques; + question_mat=importdata([ques '-cat-NaN.txt'], '\t'); + cd .. + cd NaN + question_nan=importdata([ques '--NaN.txt'], '\t'); + cd .. + [r,c]=size(question_mat); + labels=question_mat(:,1); + question_mat=question_mat(:,2:c); + question_nan=question_nan(:,2:c); + years=[2009 2011 2013]; + count=1; + count_end=C; + C=c-1; + for i=1:numel(years) + cat_mat(count:count_end,1)=ones(C,1)*years(i); + NaN_mat(count:count_end,1)=ones(C,1)*years(i); + cat_mat(count:count_end,2)=weight(i,:)'; + NaN_mat(count:count_end,2)=weight(i,:)'; + cat_mat(count:count_end,3)=race(i,:)'; + NaN_mat(count:count_end,3)=race(i,:)'; + cat_mat(count:count_end,4)=sex(i,:)'; + NaN_mat(count:count_end,4)=sex(i,:)'; + cat_mat(count:count_end,5)=grade(i,:)'; + NaN_mat(count:count_end,5)=grade(i,:)'; + indx=find(labels==years(i)); + cat_mat(count:count_end,5+j)=question_mat(indx,:)'; + NaN_mat(count:count_end,5+j)=question_nan(indx,:)'; + count=count+C; + count_end=count_end+C; + end +end + + [r,c]=size(cat_mat); + %remove NaNs + indx=find(isnan(cat_mat(:,6))==0 & isnan(cat_mat(:,7))==0 & isnan(cat_mat(:,8))==0); + cat_mat_=cat_mat(indx,:); + indx=find(isnan(NaN_mat(:,6))==0 & isnan(NaN_mat(:,7))==0 & isnan(NaN_mat(:,8))==0); + NaN_mat_=NaN_mat(indx,:); \ No newline at end of file diff --git a/suicide/find_mental_health_overlap.m b/suicide/find_mental_health_overlap.m new file mode 100644 index 0000000..5928ed3 --- /dev/null +++ b/suicide/find_mental_health_overlap.m @@ -0,0 +1,97 @@ +%%The proportion here is different from the total proportion because +%I didn't apply a weight like we did with the heatmaps +%Find out if we should add a weight here!!! + +%This is for ALL of the population +filename1='Q11-Q16'; +k=1; + +cd .. +cd .. +cd matrices +load reverse_code_091914 +cd .. +cd data +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +weight=importdata('weight-NaN.txt', '\t'); +cd .. +Q={'Q11', 'Q12', 'Q13', 'Q16', 'Q14'}; +Q_lab={'Felt sad or hopeless', 'Considered suicide', 'Made a plan to commit suicide','Attempted Suicide', 'Injured from suicide attempt' }; +%files1=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_053015/NaN/', '*.txt')); +files1=dir(fullfile('C:/Users/rugglk01/Dropbox (Personal)/CDC/data/results_053015/NaN/', '*.txt')); + + +N=length(files1); +QUEST_MAT=double.empty; +count=1; +for j=1:N + cd results_053015 + cd NaN + question_mat=importdata(files1(j).name, '\t'); + cd .. + cd .. + filename=''; + a=char(files1(j).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename=[filename c]; + end + + indx=find(strcmp(filename, Q)==1); + if numel(indx)>0 + [r,c]=size(question_mat); + label_=question_mat(2:r,1); + QUEST_MAT(:,:,count)=question_mat; + count=count+1; + end +end + +[r,c,z]=size(QUEST_MAT); +count_mat=zeros(z,z,k); +total_mat=zeros(z,z,k); +per_mat=zeros(z,z,k); +for k=1:r + for j=1:z + mat1=QUEST_MAT(k,2:c,j); + indx1=find(mat1==1); + indx_nan1=find(isnan(mat1)==0 & mat1<9); + for i=1:z + mat2=QUEST_MAT(k,2:c,i); + indx2=find(mat2==1); + indx_nan2=find(isnan(mat2)==0 & mat2<9); + over=intersect(indx1,indx2); + count_mat(j,i,k)=numel(over); + over_nan=intersect(indx_nan1, indx_nan2); + total_mat(j,i,k)=numel(over_nan); + per_mat(j,i,k)=numel(over)/numel(over_nan)*100; + end + end +end + +%make one across 2003-2013 +count_mat_all=zeros(z,z); +total_mat_all=zeros(z,z); +for k=2:r + for j=1:z + for i=1:z + count_mat_all(j,i)=count_mat_all(j,i)+count_mat(j,i,k); + total_mat_all(j,i)=total_mat_all(j,i)+total_mat(j,i,k); + end + end +end +per_mat_all=count_mat_all./total_mat_all*100; +%round +per_mat_all=per_mat_all*10; +per_mat_all=round(per_mat_all); +per_mat_all=per_mat_all/10; +cd .. +cd programs +cd suicide +M=50; +heatmap_rb(per_mat_all, Q_lab, Q_lab, 1, M, 0, 'Colormap','money', 'UseLogColormap', false, 'ShowAllTicks',true, 'Colorbar',true,'TextColor','k', 'FontSize', 12); +saveas (gcf, 'mental_health_overlap_all.fig' ); %can make pdf, jnp, or jpg +print (gcf, '-dpdf', 'mental_health_overlap_all'); +close \ No newline at end of file diff --git a/suicide/find_mental_health_overlap_hisp_girls.m b/suicide/find_mental_health_overlap_hisp_girls.m new file mode 100644 index 0000000..35731ff --- /dev/null +++ b/suicide/find_mental_health_overlap_hisp_girls.m @@ -0,0 +1,107 @@ +%%The proportion here is different from the total proportion because +%I didn't apply a weight like we did with the heatmaps +%Find out if we should add a weight here!!! + +R=3; +G=1; +filename1='Q11-Q16'; +k=1; + +cd .. +cd .. +cd matrices +load reverse_code_091914 +cd .. +cd data +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +weight=importdata('weight-NaN.txt', '\t'); +cd .. +Q={'Q11', 'Q12', 'Q13', 'Q16', 'Q14'}; +Q_lab={'Felt sad or hopeless', 'Considered suicide', 'Made a plan to commit suicide','Attempted Suicide', 'Injured from suicide attempt' }; + +%files1=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_053015/NaN/', '*.txt')); +files1=dir(fullfile('C:/Users/rugglk01/Dropbox (Personal)/CDC/data/results_053015/NaN/', '*.txt')); + +[r,c]=size(race); +race=race(:,2:c); +sex=sex(:,2:c); + +N=length(files1); +QUEST_MAT=double.empty; +count=1; +for j=1:N + cd results_053015 + cd NaN + question_mat=importdata(files1(j).name, '\t'); + cd .. + cd .. + filename=''; + a=char(files1(j).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename=[filename c]; + end + + indx=find(strcmp(filename, Q)==1); + if numel(indx)>0 + [r,c]=size(question_mat); + label_=question_mat(2:r,1); + QUEST_MAT(:,:,count)=question_mat; + count=count+1; + end +end + +[r,c,z]=size(QUEST_MAT); +count_mat=zeros(z,z,k); +total_mat=zeros(z,z,k); +per_mat=zeros(z,z,k); +for k=1:r + indx_final=find(race(k,:)== R & sex(k,:)==G ); + for j=1:z + mat1=QUEST_MAT(k,2:c,j); + indx1=find(mat1==1); + indx_nan1=find(isnan(mat1)==0 & mat1<9); + indx1_=intersect(indx1, indx_final); + indx_nan1_=intersect(indx_nan1, indx_final); + for i=1:z + mat2=QUEST_MAT(k,2:c,i); + indx2=find(mat2==1); + indx_nan2=find(isnan(mat2)==0 & mat2<9); + indx2_=intersect(indx2, indx_final); + indx_nan2_=intersect(indx_nan2, indx_final); + over=intersect(indx1_,indx2_); + count_mat(j,i,k)=numel(over); + over_nan=intersect(indx_nan1_, indx_nan2_); + total_mat(j,i,k)=numel(over_nan); + per_mat(j,i,k)=numel(over)/numel(over_nan)*100; + end + end +end + +%make one across 2003-2013 +count_mat_all=zeros(z,z); +total_mat_all=zeros(z,z); +for k=2:r + for j=1:z + for i=1:z + count_mat_all(j,i)=count_mat_all(j,i)+count_mat(j,i,k); + total_mat_all(j,i)=total_mat_all(j,i)+total_mat(j,i,k); + end + end +end +per_mat_all_hisp=count_mat_all./total_mat_all*100; +%round +per_mat_all_hisp=per_mat_all_hisp*10; +per_mat_all_hisp=round(per_mat_all_hisp); +per_mat_all_hisp=per_mat_all_hisp/10; +cd .. +cd programs +cd suicide +M=50; +heatmap_rb(per_mat_all_hisp, Q_lab, Q_lab, 1, M, 0, 'Colormap','money', 'UseLogColormap', false, 'ShowAllTicks',true, 'Colorbar',true,'TextColor','k', 'FontSize', 12); +saveas (gcf, 'mental_health_overlap_hisp_girls.fig' ); %can make pdf, jnp, or jpg +print (gcf, '-dpdf', 'mental_health_overlap_hisp_girls'); +close \ No newline at end of file diff --git a/suicide/find_mental_health_overlap_hisp_girlsv2.m b/suicide/find_mental_health_overlap_hisp_girlsv2.m new file mode 100644 index 0000000..b46a030 --- /dev/null +++ b/suicide/find_mental_health_overlap_hisp_girlsv2.m @@ -0,0 +1,109 @@ + +%This one differs from version 1 in that version 1 looked at the proportion +%of total participants who answered yes to both questions. This one looks +%at of those answering yes to question 1, what percent also answered yes to +%question 2. + +R=3; +G=1; +filename1='Q11-Q16'; +k=1; + +cd .. +cd .. +cd matrices +load reverse_code_091914 +cd .. +cd data +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +%weight=importdata('weight-NaN.txt', '\t'); +cd .. +Q={'Q11', 'Q12', 'Q13', 'Q16', 'Q14'}; +Q_lab={'Felt sad or hopeless', 'Considered suicide', 'Made a plan to commit suicide','Attempted Suicide', 'Injured from suicide attempt' }; + +%files1=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_053015/NaN/', '*.txt')); +files1=dir(fullfile('C:/Users/rugglk01/Dropbox (Personal)/CDC/data/results_053015/NaN/', '*.txt')); + +[r,c]=size(race); +race=race(:,2:c); +sex=sex(:,2:c); + +N=length(files1); +QUEST_MAT=double.empty; +count=1; +for j=1:N + cd results_053015 + cd NaN + question_mat=importdata(files1(j).name, '\t'); + cd .. + cd .. + filename=''; + a=char(files1(j).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename=[filename c]; + end + + indx=find(strcmp(filename, Q)==1); + if numel(indx)>0 + [r,c]=size(question_mat); + label_=question_mat(2:r,1); + QUEST_MAT(:,:,count)=question_mat; + count=count+1; + end +end + +[r,c,z]=size(QUEST_MAT); +count_mat=zeros(z,z,k); +total_mat=zeros(z,z,k); +per_mat=zeros(z,z,k); +for k=1:r + indx_final=find(race(k,:)== R & sex(k,:)==G ); + for j=1:z + mat1=QUEST_MAT(k,2:c,j); + indx1=find(mat1==1); + indx_nan1=find(isnan(mat1)==0 & mat1<9); + indx1_=intersect(indx1, indx_final); + indx_nan1_=intersect(indx_nan1, indx_final); + for i=1:z + mat2=QUEST_MAT(k,2:c,i); + indx2=find(mat2==1); + indx_nan2=find(isnan(mat2)==0 & mat2<9); + indx2_=intersect(indx2, indx_final); + indx_nan2_=intersect(indx_nan2, indx_final); + over=intersect(indx1_,indx2_); + count_mat(j,i,k)=numel(over); + over_nan=intersect(indx_nan1_, indx_nan2_); + total_mat(j,i,k)=numel(indx1_); + per_mat(j,i,k)=numel(over)/numel(indx1_)*100; + end + end +end + +%make one across 2003-2013 +count_mat_all=zeros(z,z); +total_mat_all=zeros(z,z); +for k=2:r + for j=1:z + for i=1:z + count_mat_all(j,i)=count_mat_all(j,i)+count_mat(j,i,k); + total_mat_all(j,i)=total_mat_all(j,i)+total_mat(j,i,k); + end + end +end +per_mat_all_hisp=count_mat_all./total_mat_all*100; +%round +per_mat_all_hisp=per_mat_all_hisp*10; +per_mat_all_hisp=round(per_mat_all_hisp); +per_mat_all_hisp=per_mat_all_hisp/10; +cd .. +cd programs +cd suicide +M=100; +heatmap_rb(per_mat_all_hisp, Q_lab, Q_lab, 1, M, 0, 'Colormap','money', 'UseLogColormap', false, 'ShowAllTicks',true, 'Colorbar',true,'TextColor','k', 'FontSize', 12); +saveas (gcf, 'mental_health_overlap_hisp_girlsv2.fig' ); %can make pdf, jnp, or jpg +print (gcf, '-dpdf', 'mental_health_overlap_hisp_girlsv2'); +close \ No newline at end of file diff --git a/suicide/find_mental_health_overlapv2.m b/suicide/find_mental_health_overlapv2.m new file mode 100644 index 0000000..d101ed9 --- /dev/null +++ b/suicide/find_mental_health_overlapv2.m @@ -0,0 +1,99 @@ + +%This one differs from version 1 in that version 1 looked at the proportion +%of total participants who answered yes to both questions. This one looks +%at of those answering yes to question 1, what percent also answered yes to +%question 2. + +%This is for ALL of the population +filename1='Q11-Q16'; +k=1; + +cd .. +cd .. +cd matrices +load reverse_code_091914 +cd .. +cd data +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +%weight=importdata('weight-NaN.txt', '\t'); +cd .. +Q={'Q11', 'Q12', 'Q13', 'Q16', 'Q14'}; +Q_lab={'Felt sad or hopeless', 'Considered suicide', 'Made a plan to commit suicide','Attempted Suicide', 'Injured from suicide attempt' }; +%files1=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_053015/NaN/', '*.txt')); +files1=dir(fullfile('C:/Users/rugglk01/Dropbox (Personal)/CDC/data/results_053015/NaN/', '*.txt')); + + +N=length(files1); +QUEST_MAT=double.empty; +count=1; +for j=1:N + cd results_053015 + cd NaN + question_mat=importdata(files1(j).name, '\t'); + cd .. + cd .. + filename=''; + a=char(files1(j).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename=[filename c]; + end + + indx=find(strcmp(filename, Q)==1); + if numel(indx)>0 + [r,c]=size(question_mat); + label_=question_mat(2:r,1); + QUEST_MAT(:,:,count)=question_mat; + count=count+1; + end +end + +[r,c,z]=size(QUEST_MAT); +count_mat=zeros(z,z,k); +total_mat=zeros(z,z,k); +per_mat=zeros(z,z,k); +for k=1:r + for j=1:z + mat1=QUEST_MAT(k,2:c,j); + indx1=find(mat1==1); + indx_nan1=find(isnan(mat1)==0 & mat1<9); + for i=1:z + mat2=QUEST_MAT(k,2:c,i); + indx2=find(mat2==1); + indx_nan2=find(isnan(mat2)==0 & mat2<9); + over=intersect(indx1,indx2); + count_mat(j,i,k)=numel(over); + over_nan=intersect(indx_nan1, indx_nan2); + total_mat(j,i,k)=numel(indx1); + per_mat(j,i,k)=numel(over)/numel(indx1)*100; + end + end +end + +%make one across 2003-2013 +count_mat_all=zeros(z,z); +total_mat_all=zeros(z,z); +for k=2:r + for j=1:z + for i=1:z + count_mat_all(j,i)=count_mat_all(j,i)+count_mat(j,i,k); + total_mat_all(j,i)=total_mat_all(j,i)+total_mat(j,i,k); + end + end +end +per_mat_all=count_mat_all./total_mat_all*100; +%round +per_mat_all=per_mat_all*10; +per_mat_all=round(per_mat_all); +per_mat_all=per_mat_all/10; +cd .. +cd programs +cd suicide +M=100; +heatmap_rb(per_mat_all, Q_lab, Q_lab, 1, M, 0, 'Colormap','money', 'UseLogColormap', false, 'ShowAllTicks',true, 'Colorbar',true,'TextColor','k', 'FontSize', 12); +saveas (gcf, 'mental_health_overlap_allv2.fig' ); %can make pdf, jnp, or jpg +print (gcf, '-dpdf', 'mental_health_overlap_allv2'); +close \ No newline at end of file diff --git a/sun/count_risks_tanning.m b/sun/count_risks_tanning.m new file mode 100644 index 0000000..80dfac4 --- /dev/null +++ b/sun/count_risks_tanning.m @@ -0,0 +1,206 @@ + +%White Girls only +R=1; +G=1; +k=1; + +cd .. +cd .. +cd matrices +load reverse_code_091914 +cd .. +cd data +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +cd .. +[r,c]=size(sex); +%files1=dir(fullfile('/ifs/data/proteomics/projects/cdc/matlab/results_053015/NaN/', '*.txt')); +%files1=dir(fullfile('C:/Users/rugglk01/Dropbox (Personal)/CDC/data/results_053015/NaN/', '*.txt')); +files1=dir(fullfile('C:/Users/kruggles7/Dropbox (Personal)/CDC/data/results_053015/NaN/', '*.txt')); + +%files2=dir(fullfile('C:/Users/rugglk01/Dropbox (Personal)/CDC/data/results_053015/cat/', '*.txt')); +files2=dir(fullfile('C:/Users/kruggles7/Dropbox (Personal)/CDC/data/results_053015/cat/', '*.txt')); +Q='Q78'; %how many times have you gone indoor tanning? + +n_count=0; + +[r,c]=size(sex); +temp=zeros(r,c,2); +for i=1:r + indx=find(isnan(sex(i,:))==1); + temp(i,indx,1)=NaN; + temp(i,indx,2)=NaN; +end +count_mat(:,:,1)=temp(:,2:c,1); %remove the year column +count_mat(:,:,2)=temp(:,2:c,2); + +N=length(files1); + +for J=1:N + cd results_053015 + cd cat + quest_1=importdata(files2(J).name, '\t'); + cd .. + cd .. + + filename1=''; + a=char(files2(J).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename1=[filename1 c]; + end + indx=find(strcmp(filename1, Q)==1); + + if numel(indx)>0 + %get rid of the '0s' associated with the other subset of participants + [r,c]=size(quest_1); + count_mat_final=nan(r,c-1,2); + for i=1:r + indx_yes=find(quest_1(i,2:c)>=5); + indx_no=find(quest_1(i,2:c)<5); +% indx_miss=find(quest_1(i,2:c)==0); +% count_mat_final(i , :, :)=count_mat(i+4, : , :); +% count_mat_final(i, indx_miss, 1)=NaN; +% count_mat_final(i, indx_miss, 2)=NaN; +% count_mat_final(i, indx_no, 1)=NaN; +% count_mat_final(i, indx_yes, 2)=NaN; + end + for j=1:N + cd results_053015 + cd NaN + quest_2=importdata(files1(j).name, '\t'); + cd .. + cd .. + filename2=''; + a=char(files1(j).name); + b=strfind(a,'-'); + for p=1:(b(1)-1) + c=a(p); + filename2=[filename2 c]; + end + ct=[]; + for q=2:(b(1)-1) + c1=a(q) ; + if c1~=0 + ct=[ct c1]; + end + end + ct=str2num(ct); + q2_RC=reverse_code(ct,1); + + %PROCESS THE COMBO + if (isnan(q2_RC)==0) + n_count=n_count+1; + [r1,c1]=size(quest_1); + [r2,c2]=size(quest_2) ; + [r3,c3]=size(sex); + year=[2001 2003 2005 2007 2009 2011 2013]; + if j==1 + year1=quest_1(:,1); + year3=sex(:,1); + quest_1=quest_1(:,2:c1); + sex=sex(:,2:c3); + race=race(:,2:c3); + end + year2=quest_2(:,1); + quest_2=quest_2(:,2:c2); + year_final=double.empty; + quest_1F=double.empty; + quest_2F=double.empty; + sex_=double.empty; + race_=double.empty; + counter=1; + s=0; %start for 1 + e=1; %start for 2 + for ii=1:numel(year) + y=year(ii); + indx1=find(year1==y); + indx2=find(year2==y); + indx3=find(year3==y); + if numel(indx1)>0 && numel(indx2)>0 %both in the matrix + year_final(counter,1)=y; + quest_1F(counter,:)=quest_1(indx1,:); + quest_2F(counter,:)=quest_2(indx2,:); + sex_(counter,:)=sex(indx3,:); + race_(counter,:)=race(indx3,:); + e=ii; + if (counter==1) + s=ii; + end + counter=counter+1; + end + end + K=s+2; + [r_,c]=size(quest_1F); + for i=1:r_ + %Q1: + index_final{i}=find(race_(i,:)== R & sex_(i,:)==G ); + index_yes{i}=find(quest_1F(i,:)>=5); %students who answered yes to Q1 + index_no{i}=find(quest_1F(i,:)<5 & quest_1F(i,:)>0); %students who answered no to Q1 + index_yes_1{i}=intersect(index_yes{i},index_final{i}); + index_no_1{i}=intersect(index_no{i},index_final{i}); + index_miss{i}=find(quest_1F(i,:)==0 ); %students who didn't answer Q1 + index_miss_1{i}=intersect(index_miss{i}, index_final{i}); + if (j==1) + count_mat_final(i,index_yes_1{i},1)=0; + count_mat_final(i, index_no_1{i},2)=0; + end + %Q2: + if q2_RC==1 + index_no{i}=find(quest_2F(i,:)==1); + index_yes{i}=find(quest_2F(i,:)==0); + index_yes_2{i}=intersect(index_yes{i},index_final{i}); + index_no_2{i}=intersect(index_no{i},index_final{i}); + index_miss{i}=find(quest_2F(i,:)==9); + index_miss_2{i}=intersect(index_miss{i}, index_final{i}); + else + index_yes{i}=find(quest_2F(i,:)==1); + index_no{i}=find(quest_2F(i,:)==0); + index_yes_2{i}=intersect(index_yes{i},index_final{i}); + index_no_2{i}=intersect(index_no{i},index_final{i}); + index_miss{i}=find(quest_2F(i,:)==9); + index_miss_2{i}=intersect(index_miss{i}, index_final{i}); + end + + index_yes_both{i}=intersect(index_yes_1{i}, index_yes_2{i}); %students who said yes to both Qs + index_no1_yes2{i}=intersect(index_no_1{i}, index_yes_2{i}); %students who said no to Q1 and yes to Q2 + + count_mat_final(i, index_yes_both{i}, 1)=count_mat_final(i, index_yes_both{i}, 1) + 1; + count_mat_final(i, index_no1_yes2{i}, 2)=count_mat_final(i, index_no1_yes2{i}, 2) + 1; + + end + end + end + end +end + +cat1=cat(2, count_mat_final(1,:,1), count_mat_final(2,:,1)); +cat1=cat(2, cat1, count_mat_final(3,:,1)); + +cat2=cat(2, count_mat_final(1,:,2), count_mat_final(2,:,2)); +cat2=cat(2, cat2, count_mat_final(3,:,2)); +cat_(:,1)=cat1; +cat_(:,2)=cat2; +[p, an]=anova1(cat_); + +cat_2=fliplr(cat_); +n1=numel(find(isnan(cat_2(:,1))==0)); +n2=numel(find(isnan(cat_2(:,2))==0)); +boxplot(cat_2); +set(gca, 'xticklabel', {['Indoor tanning never or <20 times per year (N=' num2str(n1) ')'], ['Indoor tanning 20+ times per year (N=' num2str(n2) ')']}); +ylabel('Number of risk behaviors each subject has participated in'); +title ('During the past 12 months, how many times did you use an indoor tanning device?'); + + +cd .. +cd matrices +save('risk_counts_WHITE_GIRLS_tanning20', 'count_mat'); + + +indx1=find(isnan(cat_2(:,1))==0); +indx2=find(isnan(cat_2(:,2))==0); + +N1=cat_2(indx1,1); +N2=cat_2(indx2,2); diff --git a/sun/process_cluster_OR_Q79_CAT2_white_boys_girls.m b/sun/process_cluster_OR_Q79_CAT2_white_boys_girls.m new file mode 100644 index 0000000..8a1f29e --- /dev/null +++ b/sun/process_cluster_OR_Q79_CAT2_white_boys_girls.m @@ -0,0 +1,71 @@ +cd .. +cd odds_ratios +cd OR_2014_11_04_2decimals +load OR_2013_WHITE_BOYS_Q79_CAT2.mat +load OR_CI_2013_WHITE_BOYS_Q79_CAT2.mat +OR_boys=odds_ratio_cell; +OR_CI_boys=OR_CI; +load OR_2013_WHITE_GIRLS_Q79_CAT2.mat +load OR_CI_2013_WHITE_GIRLS_Q79_CAT2.mat +OR_girls=odds_ratio_cell; +OR_CI_girls=OR_CI; +cd .. +cd .. +cd .. +cd matrices +load qlabel_090914 +cd .. +cd programs +cd sun + +%BOYS +plot_mat=OR_boys(:,7:9); +CI_mat=OR_CI_boys(:,7:9); +[r,c]=size(plot_mat); +table_OR_boys=cell(r+1,c+1); +q2=OR_boys(:,2); +for j=1:numel(q2) + indx2=find(strcmp(q2{j},qlabel(:,2))==1); + if numel(indx2)>0 + table_OR_boys{j+1,1}=qlabel{indx2,1}; + end +end +x={'2009' '2011' '2013'}; +table_OR_boys(1,2:c+1)=x; +for j=1:r + for k=1:c + %check for empty + i=cellfun(@isempty,plot_mat(j,k)); + if i==0 + n_round=plot_mat{j,k}; + n_round=sprintf('%0.2f',round(n_round*100)/100); + table_OR_boys{j+1,k+1}=[n_round ' (' num2str(CI_mat{j,k}) ')']; + end + end +end + +%GIRLS +plot_mat=OR_girls(:,7:9); +CI_mat=OR_CI_girls(:,7:9); +[r,c]=size(plot_mat); +table_OR_girls=cell(r+1,c+1); +q2=OR_girls(:,2); +for j=1:numel(q2) + indx2=find(strcmp(q2{j},qlabel(:,2))==1); + if numel(indx2)>0 + table_OR_girls{j+1,1}=qlabel{indx2,1}; + end +end +x={'2009' '2011' '2013'}; +table_OR_girls(1,2:c+1)=x; +for j=1:r + for k=1:c + %check for empty + i=cellfun(@isempty,plot_mat(j,k)); + if i==0 + n_round=plot_mat{j,k}; + n_round=sprintf('%0.2f',round(n_round*100)/100); + table_OR_girls{j+1,k+1}=[n_round ' (' num2str(CI_mat{j,k}) ')']; + end + end +end \ No newline at end of file diff --git a/sun/process_cluster_OR_Q79_CAT5_white_boys_girls.m b/sun/process_cluster_OR_Q79_CAT5_white_boys_girls.m new file mode 100644 index 0000000..9086a8b --- /dev/null +++ b/sun/process_cluster_OR_Q79_CAT5_white_boys_girls.m @@ -0,0 +1,71 @@ +cd .. +cd odds_ratios +cd OR_2014_11_04_2decimals +load OR_2013_WHITE_BOYS_Q79_CAT5.mat +load OR_CI_2013_WHITE_BOYS_Q79_CAT5.mat +OR_boys=odds_ratio_cell; +OR_CI_boys=OR_CI; +load OR_2013_WHITE_GIRLS_Q79_CAT5.mat +load OR_CI_2013_WHITE_GIRLS_Q79_CAT5.mat +OR_girls=odds_ratio_cell; +OR_CI_girls=OR_CI; +cd .. +cd .. +cd .. +cd matrices +load qlabel_090914 +cd .. +cd programs +cd sun + +%BOYS +plot_mat=OR_boys(:,7:9); +CI_mat=OR_CI_boys(:,7:9); +[r,c]=size(plot_mat); +table_OR_boys=cell(r+1,c+1); +q2=OR_boys(:,2); +for j=1:numel(q2) + indx2=find(strcmp(q2{j},qlabel(:,2))==1); + if numel(indx2)>0 + table_OR_boys{j+1,1}=qlabel{indx2,1}; + end +end +x={'2009' '2011' '2013'}; +table_OR_boys(1,2:c+1)=x; +for j=1:r + for k=1:c + %check for empty + i=cellfun(@isempty,plot_mat(j,k)); + if i==0 + n_round=plot_mat{j,k}; + n_round=sprintf('%0.2f',round(n_round*100)/100); + table_OR_boys{j+1,k+1}=[n_round ' (' num2str(CI_mat{j,k}) ')']; + end + end +end + +%GIRLS +plot_mat=OR_girls(:,7:9); +CI_mat=OR_CI_girls(:,7:9); +[r,c]=size(plot_mat); +table_OR_girls=cell(r+1,c+1); +q2=OR_girls(:,2); +for j=1:numel(q2) + indx2=find(strcmp(q2{j},qlabel(:,2))==1); + if numel(indx2)>0 + table_OR_girls{j+1,1}=qlabel{indx2,1}; + end +end +x={'2009' '2011' '2013'}; +table_OR_girls(1,2:c+1)=x; +for j=1:r + for k=1:c + %check for empty + i=cellfun(@isempty,plot_mat(j,k)); + if i==0 + n_round=plot_mat{j,k}; + n_round=sprintf('%0.2f',round(n_round*100)/100); + table_OR_girls{j+1,k+1}=[n_round ' (' num2str(CI_mat{j,k}) ')']; + end + end +end \ No newline at end of file diff --git a/sun/tanning_white_boys_girls_2013_CIv2.m b/sun/tanning_white_boys_girls_2013_CIv2.m new file mode 100644 index 0000000..2dfd0f9 --- /dev/null +++ b/sun/tanning_white_boys_girls_2013_CIv2.m @@ -0,0 +1,681 @@ + +clear +cd .. +cd .. +cd data +cd Controls_061514 +sex=importdata('sex-NaN.txt', '\t'); +race=importdata('race-NaN.txt', '\t'); +weight=importdata('weights-NaN.txt','\t'); +grade=importdata('grade-NaN.txt','\t'); +[r,c]=size(sex); +weight=weight(5:7,2:c); +race=race(5:7,2:c); +sex=sex(5:7,2:c); +grade=grade(5:7,2:c); +cd .. +cd results_103114 +cd cat +question_mat=importdata('Q78-cat-NaN.txt', '\t'); +[r,c]=size(question_mat); +question_mat=question_mat(:,2:c); +cd .. +cd .. +cd .. +cd programs +cd sun + +conf_mat=cell(59,10); +conf_mat{1,2}='Ever'; +conf_mat{1,3}='1-20 times'; +conf_mat{1,4}='20+ times'; + +conf_mat{1,5}='Ever'; +conf_mat{1,6}='1-20 times'; +conf_mat{1,7}='20+ times'; + +conf_mat{1,8}='Ever'; +conf_mat{1,9}='1-20 times'; +conf_mat{1,10}='20+ times'; + +conf_mat{2,1}='total'; +conf_mat{3,1}='girls'; +conf_mat{4,1}='boys'; +conf_mat{5,1}='Wg'; +conf_mat{6,1}='Wb'; +conf_mat{7,1}='Bg'; +conf_mat{8,1}='Bb'; +conf_mat{9,1}='Hg'; +conf_mat{10,1}='Hb'; +conf_mat{11,1}='Og'; +conf_mat{12,1}='Ob'; +conf_mat{13,1}='W'; +conf_mat{14,1}='B'; +conf_mat{15,1}='H'; +conf_mat{16,1}='O'; +conf_mat{17,1}='9'; +conf_mat{18,1}='10'; +conf_mat{19,1}='11'; +conf_mat{20,1}='12'; +conf_mat{21,1}='9g'; +conf_mat{22,1}='9b'; +conf_mat{23,1}='10g'; +conf_mat{24,1}='10b'; +conf_mat{25,1}='11g'; +conf_mat{26,1}='11b'; +conf_mat{27,1}='12g'; +conf_mat{28,1}='12b'; + +conf_mat{29,1}='H9g'; +conf_mat{30,1}='H10g'; +conf_mat{31,1}='H11g'; +conf_mat{32,1}='H12g'; +conf_mat{33,1}='H9b'; +conf_mat{34,1}='H10b'; +conf_mat{35,1}='H11b'; +conf_mat{36,1}='H12b'; +conf_mat{37,1}='W9g'; +conf_mat{38,1}='W10g'; +conf_mat{39,1}='W11g'; +conf_mat{40,1}='W12g'; +conf_mat{41,1}='W9b'; +conf_mat{42,1}='W10b'; +conf_mat{43,1}='W11b'; +conf_mat{44,1}='W12b'; + +conf_mat{45,1}='B9g'; +conf_mat{46,1}='B10g'; +conf_mat{47,1}='B11g'; +conf_mat{48,1}='B12g'; +conf_mat{49,1}='B9b'; +conf_mat{50,1}='B10b'; +conf_mat{51,1}='B11b'; +conf_mat{52,1}='B12b'; +conf_mat{53,1}='O9g'; +conf_mat{54,1}='O10g'; +conf_mat{55,1}='O11g'; +conf_mat{56,1}='O12g'; +conf_mat{57,1}='O9b'; +conf_mat{58,1}='O10b'; +conf_mat{59,1}='O11b'; +conf_mat{60,1}='O12b'; + +N_MAT=conf_mat; + +n_mat=zeros(59,r); +x_mat=zeros(59,3,r); +n_CI_mat=zeros(59,r); +c=1; + + +for i=1:r +% total(i)=TOTAL(i,1); + for j=1:6 + n=zeros(59,1); + x=zeros(59,1); + + index_yes{i}=find(question_mat(i,:)==j); + index_girls{i}=find(sex(i,:)==1); + index_boys{i}=find(sex(i,:)==2); + index_W{i}=find(race(i,:)== 1 ); + index_B{i}=find(race(i,:)== 2 ); + index_H{i}=find(race(i,:)== 3 ); + index_O{i}=find(race(i,:)== 4 ); + index_9{i}=find(grade(i,:)== 1 ); + index_10{i}=find(grade(i,:)== 2 ); + index_11{i}=find(grade(i,:)== 3 ); + index_12{i}=find(grade(i,:)== 4 ); + + index_W9{i}=intersect(index_9{i},index_W{i}); + index_W10{i}=intersect(index_10{i},index_W{i}); + index_W11{i}=intersect(index_11{i},index_W{i}); + index_W12{i}=intersect(index_12{i},index_W{i}); + + index_B9{i}=intersect(index_9{i},index_B{i}); + index_B10{i}=intersect(index_10{i},index_B{i}); + index_B11{i}=intersect(index_11{i},index_B{i}); + index_B12{i}=intersect(index_12{i},index_B{i}); + + index_H9{i}=intersect(index_9{i},index_H{i}); + index_H10{i}=intersect(index_10{i},index_H{i}); + index_H11{i}=intersect(index_11{i},index_H{i}); + index_H12{i}=intersect(index_12{i},index_H{i}); + + index_O9{i}=intersect(index_9{i},index_O{i}); + index_O10{i}=intersect(index_10{i},index_O{i}); + index_O11{i}=intersect(index_11{i},index_O{i}); + index_O12{i}=intersect(index_12{i},index_O{i}); + + index_missQ{i}=find(question_mat(i,:)== 0); %students who didn't answer the Q + index_nomiss{i}=find(question_mat(i,:)>0); %answers that were NOT missing (ie. 0's and 1's / no's and yes's) + index_noNaN{i}=find(isnan(question_mat(i,:))==0); + + index_total_b{i}=intersect(index_nomiss{i},index_boys{i}); %index of all boys who answered + index_total_g{i}=intersect(index_nomiss{i},index_girls{i}); %index of all girls who answered + + w=weight(i,:)'; + %TOTAL FOR CI + if (j==1) + total_ans(i)=nansum(w(index_noNaN{i})); + total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered + total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered + total_W{i}=nansum(w(intersect(index_noNaN{i}, index_W{i}))); %total # of white students who answered + total_B{i}=nansum(w(intersect(index_noNaN{i}, index_B{i}))); %total # of black students who answered + total_H{i}=nansum(w(intersect(index_noNaN{i}, index_H{i}))); %total # of hispanic students who answered + total_O{i}=nansum(w(intersect(index_noNaN{i}, index_O{i}))); %total # of "other" students who answered + + total_w(i)=total_W{i}; + total_b(i)=total_B{i}; + total_h(i)=total_H{i}; + total_o(i)=total_O{i}; + + total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i}))); + total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i}))); + total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i}))); + total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i}))); + total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i}))); + total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i}))); + total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i}))); + total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i}))); + total_9(i)=nansum(w(intersect((index_9{i}),index_noNaN{i}))); + total_10(i)=nansum(w(intersect((index_10{i}),index_noNaN{i}))); + total_11(i)=nansum(w(intersect((index_11{i}),index_noNaN{i}))); + total_12(i)=nansum(w(intersect((index_12{i}),index_noNaN{i}))); + + total_9G(i)=nansum(w(intersect(index_9{i},index_total_g{i}))); + total_10G(i)=nansum(w(intersect(index_10{i},index_total_g{i}))); + total_11G(i)=nansum(w(intersect(index_11{i},index_total_g{i}))); + total_12G(i)=nansum(w(intersect(index_12{i},index_total_g{i}))); + + total_9B(i)=nansum(w(intersect(index_9{i},index_total_b{i}))); + total_10B(i)=nansum(w(intersect(index_10{i},index_total_b{i}))); + total_11B(i)=nansum(w(intersect(index_11{i},index_total_b{i}))); + total_12B(i)=nansum(w(intersect(index_12{i},index_total_b{i}))); + + total_9G_W(i)=nansum(w(intersect(index_W9{i},index_total_g{i}))); + total_9B_W(i)=nansum(w(intersect(index_W9{i},index_total_b{i}))); + total_9G_B(i)=nansum(w(intersect(index_B9{i},index_total_g{i}))); + total_9B_B(i)=nansum(w(intersect(index_B9{i},index_total_b{i}))); + + total_9G_H(i)=nansum(w(intersect(index_H9{i},index_total_g{i}))); + total_9B_H(i)=nansum(w(intersect(index_H9{i},index_total_b{i}))); + total_9G_O(i)=nansum(w(intersect(index_O9{i},index_total_g{i}))); + total_9B_O(i)=nansum(w(intersect(index_O9{i},index_total_b{i}))); + + total_10G_W(i)=nansum(w(intersect(index_W10{i},index_total_g{i}))); + total_10B_W(i)=nansum(w(intersect(index_W10{i},index_total_b{i}))); + total_10G_B(i)=nansum(w(intersect(index_B10{i},index_total_g{i}))); + total_10B_B(i)=nansum(w(intersect(index_B10{i}, index_total_b{i}))); + + total_10G_H(i)=nansum(w(intersect(index_H10{i},index_total_g{i}))); + total_10B_H(i)=nansum(w(intersect(index_H10{i},index_total_b{i}))); + total_10G_O(i)=nansum(w(intersect(index_O10{i},index_total_g{i}))); + total_10B_O(i)=nansum(w(intersect(index_O10{i},index_total_b{i}))); + + total_11G_W(i)=nansum(w(intersect(index_W11{i},index_total_g{i}))); + total_11B_W(i)=nansum(w(intersect(index_W11{i},index_total_b{i}))); + total_11G_B(i)=nansum(w(intersect(index_B11{i},index_total_g{i}))); + total_11B_B(i)=nansum(w(intersect(index_B11{i},index_total_b{i}))); + + total_11G_H(i)=nansum(w(intersect(index_H11{i},index_total_g{i}))); + total_11B_H(i)=nansum(w(intersect(index_H11{i},index_total_b{i}))); + total_11G_O(i)=nansum(w(intersect(index_O11{i},index_total_g{i}))); + total_11B_O(i)=nansum(w(intersect(index_O11{i},index_total_b{i}))); + + total_12G_W(i)=nansum(w(intersect(index_W12{i},index_total_g{i}))); + total_12B_W(i)=nansum(w(intersect(index_W12{i},index_total_b{i}))); + total_12G_B(i)=nansum(w(intersect(index_B12{i},index_total_g{i}))); + total_12B_B(i)=nansum(w(intersect(index_B12{i},index_total_b{i}))); + + total_12G_H(i)=nansum(w(intersect(index_H12{i},index_total_g{i}))); + total_12B_H(i)=nansum(w(intersect(index_H12{i},index_total_b{i}))); + total_12G_O(i)=nansum(w(intersect(index_O12{i},index_total_g{i}))); + total_12B_O(i)=nansum(w(intersect(index_O12{i},index_total_b{i}))); + + total_9_W(i)=nansum(w(intersect(index_noNaN{i},index_W9{i}))); + total_9_B(i)=nansum(w(intersect(index_noNaN{i},index_B9{i}))); + total_9_H(i)=nansum(w(intersect(index_noNaN{i},index_H9{i}))); + total_9_O(i)=nansum(w(intersect(index_noNaN{i},index_O9{i}))); + + total_10_W(i)=nansum(w(intersect(index_noNaN{i},index_W10{i}))); + total_10_B(i)=nansum(w(intersect(index_noNaN{i},index_B10{i}))); + total_10_H(i)=nansum(w(intersect(index_noNaN{i},index_H10{i}))); + total_10_O(i)=nansum(w(intersect(index_noNaN{i},index_O10{i}))); + + total_11_W(i)=nansum(w(intersect(index_noNaN{i},index_W11{i}))); + total_11_B(i)=nansum(w(intersect(index_noNaN{i},index_B11{i}))); + total_11_H(i)=nansum(w(intersect(index_noNaN{i},index_H11{i}))); + total_11_O(i)=nansum(w(intersect(index_noNaN{i},index_O11{i}))); + + total_12_W(i)=nansum(w(intersect(index_noNaN{i},index_W12{i}))); + total_12_B(i)=nansum(w(intersect(index_noNaN{i},index_B12{i}))); + total_12_H(i)=nansum(w(intersect(index_noNaN{i},index_H12{i}))); + total_12_O(i)=nansum(w(intersect(index_noNaN{i},index_O12{i}))); + + n (1)= total_ans(i); + n (2)=total_girls(i); + n (3)=total_boys(i); + n (4)=total_Wg(i); + n (5)=total_Wb(i); + n (6)=total_Bg(i); + n (7)=total_Bb(i); + n (8)=total_Hg(i); + n (9)=total_Hb(i); + n (10)=total_Og(i); + n (11)=total_Ob(i); + n (12)=total_w(i); + n (13)=total_b(i); + n (14)=total_h(i); + n (15)=total_o(i); + n (16)=total_9(i); + n (17)=total_10(i); + n (18)=total_11(i); + n (19)=total_12(i); + n (20)=total_9G(i); + n (21)=total_9B(i); + n (22)=total_10G(i); + n (23)=total_10B(i); + n (24)=total_11G(i); + n (25)=total_11B(i); + n (26)=total_12G(i); + n (27)=total_12B(i); + + n (28)=total_9G_H(i); + n (29)=total_10G_H(i); + n (30)=total_11G_H(i); + n (31)=total_12G_H(i); + n (32)=total_9B_H(i); + n (33)=total_10B_H(i); + n (34)=total_11B_H(i); + n (35)=total_12B_H(i); + + n (36)=total_9G_W(i); + n (37)=total_10G_W(i); + n (38)=total_11G_W(i); + n (39)=total_12G_W(i); + n (40)=total_9B_W(i); + n (41)=total_10B_W(i); + n (42)=total_11B_W(i); + n (43)=total_12B_W(i); + + n (44)=total_9G_B(i); + n (45)=total_10G_B(i); + n (46)=total_11G_B(i); + n (47)=total_12G_B(i); + n (48)=total_9B_B(i); + n (49)=total_10B_B(i); + n (50)=total_11B_B(i); + n (51)=total_12B_B(i); + + n (52)=total_9G_O(i); + n (53)=total_10G_O(i); + n (54)=total_11G_O(i); + n (55)=total_12G_O(i); + n (56)=total_9B_O(i); + n (57)=total_10B_O(i); + n (58)=total_11B_O(i); + n (59)=total_12B_O(i); + + + n_CI_mat(:,i)=n_CI_mat(:,i)+ n; + end + + %FOR PREVALENCE + total_ans(i)=nansum(w(index_nomiss{i})); + total_girls(i)=nansum(w(index_total_g{i})); %total # of girls who answered + total_boys(i)=nansum(w(index_total_b{i})); %total number of boys who answered + total_W{i}=nansum(w(intersect(index_nomiss{i}, index_W{i}))); %total # of white students who answered + total_B{i}=nansum(w(intersect(index_nomiss{i}, index_B{i}))); %total # of black students who answered + total_H{i}=nansum(w(intersect(index_nomiss{i}, index_H{i}))); %total # of hispanic students who answered + total_O{i}=nansum(w(intersect(index_nomiss{i}, index_O{i}))); %total # of "other" students who answered + + total_w(i)=total_W{i}; + total_b(i)=total_B{i}; + total_h(i)=total_H{i}; + total_o(i)=total_O{i}; + + total_Wb(i)=nansum(w(intersect(index_total_b{i},index_W{i}))); + total_Wg(i)=nansum(w(intersect(index_total_g{i},index_W{i}))); + total_Bb(i)=nansum(w(intersect(index_total_b{i},index_B{i}))); + total_Bg(i)=nansum(w(intersect(index_total_g{i},index_B{i}))); + total_Hb(i)=nansum(w(intersect(index_total_b{i},index_H{i}))); + total_Hg(i)=nansum(w(intersect(index_total_g{i},index_H{i}))); + total_Ob(i)=nansum(w(intersect(index_total_b{i},index_O{i}))); + total_Og(i)=nansum(w(intersect(index_total_g{i},index_O{i}))); + total_9(i)=nansum(w(intersect((index_9{i}),index_nomiss{i}))); + total_10(i)=nansum(w(intersect((index_10{i}),index_nomiss{i}))); + total_11(i)=nansum(w(intersect((index_11{i}),index_nomiss{i}))); + total_12(i)=nansum(w(intersect((index_12{i}),index_nomiss{i}))); + + total_9G(i)=nansum(w(intersect(index_9{i},index_total_g{i}))); + total_10G(i)=nansum(w(intersect(index_10{i},index_total_g{i}))); + total_11G(i)=nansum(w(intersect(index_11{i},index_total_g{i}))); + total_12G(i)=nansum(w(intersect(index_12{i},index_total_g{i}))); + + total_9B(i)=nansum(w(intersect(index_9{i},index_total_b{i}))); + total_10B(i)=nansum(w(intersect(index_10{i},index_total_b{i}))); + total_11B(i)=nansum(w(intersect(index_11{i},index_total_b{i}))); + total_12B(i)=nansum(w(intersect(index_12{i},index_total_b{i}))); + + total_9G_W(i)=nansum(w(intersect(index_W9{i},index_total_g{i}))); + total_9B_W(i)=nansum(w(intersect(index_W9{i},index_total_b{i}))); + total_9G_B(i)=nansum(w(intersect(index_B9{i},index_total_g{i}))); + total_9B_B(i)=nansum(w(intersect(index_B9{i},index_total_b{i}))); + + total_9G_H(i)=nansum(w(intersect(index_H9{i},index_total_g{i}))); + total_9B_H(i)=nansum(w(intersect(index_H9{i},index_total_b{i}))); + total_9G_O(i)=nansum(w(intersect(index_O9{i},index_total_g{i}))); + total_9B_O(i)=nansum(w(intersect(index_O9{i},index_total_b{i}))); + + total_10G_W(i)=nansum(w(intersect(index_W10{i},index_total_g{i}))); + total_10B_W(i)=nansum(w(intersect(index_W10{i},index_total_b{i}))); + total_10G_B(i)=nansum(w(intersect(index_B10{i},index_total_g{i}))); + total_10B_B(i)=nansum(w(intersect(index_B10{i}, index_total_b{i}))); + + total_10G_H(i)=nansum(w(intersect(index_H10{i},index_total_g{i}))); + total_10B_H(i)=nansum(w(intersect(index_H10{i},index_total_b{i}))); + total_10G_O(i)=nansum(w(intersect(index_O10{i},index_total_g{i}))); + total_10B_O(i)=nansum(w(intersect(index_O10{i},index_total_b{i}))); + + total_11G_W(i)=nansum(w(intersect(index_W11{i},index_total_g{i}))); + total_11B_W(i)=nansum(w(intersect(index_W11{i},index_total_b{i}))); + total_11G_B(i)=nansum(w(intersect(index_B11{i},index_total_g{i}))); + total_11B_B(i)=nansum(w(intersect(index_B11{i},index_total_b{i}))); + + total_11G_H(i)=nansum(w(intersect(index_H11{i},index_total_g{i}))); + total_11B_H(i)=nansum(w(intersect(index_H11{i},index_total_b{i}))); + total_11G_O(i)=nansum(w(intersect(index_O11{i},index_total_g{i}))); + total_11B_O(i)=nansum(w(intersect(index_O11{i},index_total_b{i}))); + + total_12G_W(i)=nansum(w(intersect(index_W12{i},index_total_g{i}))); + total_12B_W(i)=nansum(w(intersect(index_W12{i},index_total_b{i}))); + total_12G_B(i)=nansum(w(intersect(index_B12{i},index_total_g{i}))); + total_12B_B(i)=nansum(w(intersect(index_B12{i},index_total_b{i}))); + + total_12G_H(i)=nansum(w(intersect(index_H12{i},index_total_g{i}))); + total_12B_H(i)=nansum(w(intersect(index_H12{i},index_total_b{i}))); + total_12G_O(i)=nansum(w(intersect(index_O12{i},index_total_g{i}))); + total_12B_O(i)=nansum(w(intersect(index_O12{i},index_total_b{i}))); + + total_9_W(i)=nansum(w(intersect(index_nomiss{i},index_W9{i}))); + total_9_B(i)=nansum(w(intersect(index_nomiss{i},index_B9{i}))); + total_9_H(i)=nansum(w(intersect(index_nomiss{i},index_H9{i}))); + total_9_O(i)=nansum(w(intersect(index_nomiss{i},index_O9{i}))); + + total_10_W(i)=nansum(w(intersect(index_nomiss{i},index_W10{i}))); + total_10_B(i)=nansum(w(intersect(index_nomiss{i},index_B10{i}))); + total_10_H(i)=nansum(w(intersect(index_nomiss{i},index_H10{i}))); + total_10_O(i)=nansum(w(intersect(index_nomiss{i},index_O10{i}))); + + total_11_W(i)=nansum(w(intersect(index_nomiss{i},index_W11{i}))); + total_11_B(i)=nansum(w(intersect(index_nomiss{i},index_B11{i}))); + total_11_H(i)=nansum(w(intersect(index_nomiss{i},index_H11{i}))); + total_11_O(i)=nansum(w(intersect(index_nomiss{i},index_O11{i}))); + + total_12_W(i)=nansum(w(intersect(index_nomiss{i},index_W12{i}))); + total_12_B(i)=nansum(w(intersect(index_nomiss{i},index_B12{i}))); + total_12_H(i)=nansum(w(intersect(index_nomiss{i},index_H12{i}))); + total_12_O(i)=nansum(w(intersect(index_nomiss{i},index_O12{i}))); + if (j==1) + n (1)= total_ans(i); + n (2)=total_girls(i); + n (3)=total_boys(i); + n (4)=total_Wg(i); + n (5)=total_Wb(i); + n (6)=total_Bg(i); + n (7)=total_Bb(i); + n (8)=total_Hg(i); + n (9)=total_Hb(i); + n (10)=total_Og(i); + n (11)=total_Ob(i); + n (12)=total_w(i); + n (13)=total_b(i); + n (14)=total_h(i); + n (15)=total_o(i); + n (16)=total_9(i); + n (17)=total_10(i); + n (18)=total_11(i); + n (19)=total_12(i); + n (20)=total_9G(i); + n (21)=total_9B(i); + n (22)=total_10G(i); + n (23)=total_10B(i); + n (24)=total_11G(i); + n (25)=total_11B(i); + n (26)=total_12G(i); + n (27)=total_12B(i); + + n (28)=total_9G_H(i); + n (29)=total_10G_H(i); + n (30)=total_11G_H(i); + n (31)=total_12G_H(i); + n (32)=total_9B_H(i); + n (33)=total_10B_H(i); + n (34)=total_11B_H(i); + n (35)=total_12B_H(i); + + n (36)=total_9G_W(i); + n (37)=total_10G_W(i); + n (38)=total_11G_W(i); + n (39)=total_12G_W(i); + n (40)=total_9B_W(i); + n (41)=total_10B_W(i); + n (42)=total_11B_W(i); + n (43)=total_12B_W(i); + + n (44)=total_9G_B(i); + n (45)=total_10G_B(i); + n (46)=total_11G_B(i); + n (47)=total_12G_B(i); + n (48)=total_9B_B(i); + n (49)=total_10B_B(i); + n (50)=total_11B_B(i); + n (51)=total_12B_B(i); + + n (52)=total_9G_O(i); + n (53)=total_10G_O(i); + n (54)=total_11G_O(i); + n (55)=total_12G_O(i); + n (56)=total_9B_O(i); + n (57)=total_10B_O(i); + n (58)=total_11B_O(i); + n (59)=total_12B_O(i); + + + n_mat(:,i)=n_mat(:,i)+ n; + end + + index_yesgirls{i}=intersect(index_yes{i},index_girls{i}); + index_yesboys{i}=intersect(index_yes{i},index_boys{i}); + yes_girls(i)=nansum(w(index_yesgirls{i})); + yes_boys(i)=nansum(w(index_yesboys{i})); + total_yes(i)=nansum(w(index_yes{i})); + + yes_W(i)=nansum(w(intersect(index_yes{i}, index_W{i}))); + yes_B(i)=nansum(w(intersect(index_yes{i}, index_B{i}))); + yes_H(i)=nansum(w(intersect(index_yes{i}, index_H{i}))); + yes_O(i)=nansum(w(intersect(index_yes{i}, index_O{i}))); + yes_WG(i)=nansum(w(intersect(index_yesgirls{i},index_W{i}))); + yes_BG(i)=nansum(w(intersect(index_yesgirls{i},index_B{i}))); + yes_HG(i)=nansum(w(intersect(index_yesgirls{i},index_H{i}))); + yes_OG(i)=nansum(w(intersect(index_yesgirls{i},index_O{i}))); + yes_WB(i)=nansum(w(intersect(index_yesboys{i},index_W{i}))); + yes_BB(i)=nansum(w(intersect(index_yesboys{i},index_B{i}))); + yes_HB(i)=nansum(w(intersect(index_yesboys{i},index_H{i}))); + yes_OB(i)=nansum(w(intersect(index_yesboys{i},index_O{i}))); + yes_9(i)=nansum(w(intersect(index_yes{i},index_9{i}))); + yes_10(i)=nansum(w(intersect(index_yes{i},index_10{i}))); + yes_11(i)=nansum(w(intersect(index_yes{i},index_11{i}))); + yes_12(i)=nansum(w(intersect(index_yes{i},index_12{i}))); + yes_9b(i)=nansum(w(intersect(index_yesboys{i},index_9{i}))); + yes_10b(i)=nansum(w(intersect(index_yesboys{i},index_10{i}))); + yes_11b(i)=nansum(w(intersect(index_yesboys{i},index_11{i}))); + yes_12b(i)=nansum(w(intersect(index_yesboys{i},index_12{i}))); + yes_9g(i)=nansum(w(intersect(index_yesgirls{i},index_9{i}))); + yes_10g(i)=nansum(w(intersect(index_yesgirls{i},index_10{i}))); + yes_11g(i)=nansum(w(intersect(index_yesgirls{i},index_11{i}))); + yes_12g(i)=nansum(w(intersect(index_yesgirls{i},index_12{i}))); + + + yes_9WB(i)=nansum(w(intersect(index_yesboys{i},index_W9{i}))); + yes_10WB(i)=nansum(w(intersect(index_yesboys{i},index_W10{i}))); + yes_11WB(i)=nansum(w(intersect(index_yesboys{i},index_W11{i}))); + yes_12WB(i)=nansum(w(intersect(index_yesboys{i},index_W12{i}))); + yes_9WG(i)=nansum(w(intersect(index_yesgirls{i},index_W9{i}))); + yes_10WG(i)=nansum(w(intersect(index_yesgirls{i},index_W10{i}))); + yes_11WG(i)=nansum(w(intersect(index_yesgirls{i},index_W11{i}))); + yes_12WG(i)=nansum(w(intersect(index_yesgirls{i},index_W12{i}))); + + yes_9W(i)=nansum(w(intersect(index_yes{i},index_W9{i}))); + yes_10W(i)=nansum(w(intersect(index_yes{i},(index_W10{i})))); + yes_11W(i)=nansum(w(intersect(index_yes{i},(index_W11{i})))); + yes_12W(i)=nansum(w(intersect(index_yes{i},(index_W12{i})))); + + yes_9B(i)=nansum(w(intersect(index_yes{i},(index_B9{i})))); + yes_10B(i)=nansum(w(intersect(index_yes{i},(index_B10{i})))); + yes_11B(i)=nansum(w(intersect(index_yes{i},(index_B11{i})))); + yes_12B(i)=nansum(w(intersect(index_yes{i},(index_B12{i})))); + + yes_9H(i)=nansum(w(intersect(index_yes{i},(index_H9{i})))); + yes_10H(i)=nansum(w(intersect(index_yes{i},(index_H10{i})))); + yes_11H(i)=nansum(w(intersect(index_yes{i},(index_H11{i})))); + yes_12H(i)=nansum(w(intersect(index_yes{i},(index_H12{i})))); + + yes_9O(i)=nansum(w(intersect(index_yes{i},(index_O9{i})))); + yes_10O(i)=nansum(w(intersect(index_yes{i},(index_O10{i})))); + yes_11O(i)=nansum(w(intersect(index_yes{i},(index_O11{i})))); + yes_12O(i)=nansum(w(intersect(index_yes{i},(index_O12{i})))); + + yes_9BB(i)=nansum(w(intersect(index_yesboys{i},index_B9{i}))); + yes_10BB(i)=nansum(w(intersect(index_yesboys{i},index_B10{i}))); + yes_11BB(i)=nansum(w(intersect(index_yesboys{i},index_B11{i}))); + yes_12BB(i)=nansum(w(intersect(index_yesboys{i},index_B12{i}))); + yes_9BG(i)=nansum(w(intersect(index_yesgirls{i},index_B9{i}))); + yes_10BG(i)=nansum(w(intersect(index_yesgirls{i},index_B10{i}))); + yes_11BG(i)=nansum(w(intersect(index_yesgirls{i},index_B11{i}))); + yes_12BG(i)=nansum(w(intersect(index_yesgirls{i},index_B12{i}))); + + yes_9HB(i)=nansum(w(intersect(index_yesboys{i},index_H9{i}))); + yes_10HB(i)=nansum(w(intersect(index_yesboys{i},index_H10{i}))); + yes_11HB(i)=nansum(w(intersect(index_yesboys{i},index_H11{i}))); + yes_12HB(i)=nansum(w(intersect(index_yesboys{i},index_H12{i}))); + yes_9HG(i)=nansum(w(intersect(index_yesgirls{i},index_H9{i}))); + yes_10HG(i)=nansum(w(intersect(index_yesgirls{i},index_H10{i}))); + yes_11HG(i)=nansum(w(intersect(index_yesgirls{i},index_H11{i}))); + yes_12HG(i)=nansum(w(intersect(index_yesgirls{i},index_H12{i}))); + + yes_9OB(i)=nansum(w(intersect(index_yesboys{i},index_O9{i}))); + yes_10OB(i)=nansum(w(intersect(index_yesboys{i},index_O10{i}))); + yes_11OB(i)=nansum(w(intersect(index_yesboys{i},index_O11{i}))); + yes_12OB(i)=nansum(w(intersect(index_yesboys{i},index_O12{i}))); + yes_9OG(i)=nansum(w(intersect(index_yesgirls{i},index_O9{i}))); + yes_10OG(i)=nansum(w(intersect(index_yesgirls{i},index_O10{i}))); + yes_11OG(i)=nansum(w(intersect(index_yesgirls{i},index_O11{i}))); + yes_12OG(i)=nansum(w(intersect(index_yesgirls{i},index_O12{i}))); + + x(1)=total_yes(i); + x(2)=yes_girls(i); + x(3)=yes_boys(i); + x(4)=yes_WG(i); + x(5)=yes_WB(i); + x(6)=yes_BG(i); + x(7)=yes_BB(i); + x(8)=yes_HG(i); + x(9)=yes_HB(i); + x(10)=yes_OG(i); + x(11)=yes_OB(i); + x(12)=yes_W(i); + x(13)=yes_B(i); + x(14)=yes_H(i); + x(15)=yes_O(i); + x(16)=yes_9(i); + x(17)=yes_10(i); + x(18)=yes_11(i); + x(19)=yes_12(i); + x(20)=yes_9g(i); + x(21)=yes_9b(i); + x(22)=yes_10g(i); + x(23)=yes_10b(i); + x(24)=yes_11g(i); + x(25)=yes_11b(i); + x(26)=yes_12g(i); + x(27)=yes_12b(i); + + x(28)=yes_9HG(i); + x(29)=yes_10HG(i); + x(30)=yes_11HG(i); + x(31)=yes_12HG(i); + x(32)=yes_9HB(i); + x(33)=yes_10HB(i); + x(34)=yes_11HB(i); + x(35)=yes_12HB(i); + + x(36)=yes_9WG(i); + x(37)=yes_10WG(i); + x(38)=yes_11WG(i); + x(39)=yes_12WG(i); + x(40)=yes_9WB(i); + x(41)=yes_10WB(i); + x(42)=yes_11WB(i); + x(43)=yes_12WB(i); + + x(44)=yes_9BG(i); + x(45)=yes_10BG(i); + x(46)=yes_11BG(i); + x(47)=yes_12BG(i); + x(48)=yes_9BB(i); + x(49)=yes_10BB(i); + x(50)=yes_11BB(i); + x(51)=yes_12BB(i); + + x(52)=yes_9OG(i); + x(53)=yes_10OG(i); + x(54)=yes_11OG(i); + x(55)=yes_12OG(i); + x(56)=yes_9OB(i); + x(57)=yes_10OB(i); + x(58)=yes_11OB(i); + x(59)=yes_12OB(i); + + if (j>1) + x_mat(:,1,i)=x_mat(:,1,i)+x; + end + if (j>1 && j<5) %1-20 times + x_mat(:,2, i)=x_mat(:,2, i)+x; + elseif (j>4); + x_mat(:,3, i)=x_mat(:,3, i)+x; + end + + end +end + +%%confidence interval + +lower_mat=double.empty; +upper_mat=double.empty; +plot_mat=double.empty; +z=1.96; +for k=1:59 + count=2; + for i=1:r + n=n_mat(k,i); + n_CI=n_CI_mat(k,i); + for j=1:3 + x=x_mat(k,j,i); %x_mat=zeros(59,5,r); + p=x/n_CI; %x is the number of subjects saying "yes", n is the total subjects + P=x/n; + upper=((P+z*sqrt(p*(1-p)/n_CI))*100); + lower=((P-z*sqrt(p*(1-p)/n_CI))*100); + lower_mat(k,count-1)=lower; + upper_mat(k,count-1)=upper; + upper=sprintf('%0.1f',round(upper*10)/10); + lower=sprintf('%0.1f',round(lower*10)/10); + P=P*100; + plot_mat(k,count-1)=P; + p_num=sprintf('%0.1f', round(P*10)/10); + conf_mat{k+1,count}=[p_num ' [' lower ', ' upper ']']; + N_MAT{k+1,count}=x; + count=count+1; + end + end +end