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cluster_analysis.m
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cluster_analysis.m
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function [p, cluster_bin] = cluster_analysis(input,inputA, inputB, flag, one_vs_two_sided)
% input: matrix of size # subjects x # time points, already averaged across
% trials; either define "input", if there is only 1 group to be tested against
% zero, or define both "inputA" and "inputB", if two groups are to be compared
% against each other
% flag = 1 for single-group tests or repeated-measures tests (2 groups); flag = 2 for two independent
% groups;
% one_vs_two_sided = 1 for one-tailed and = 2 for two-tailed tests;
% for one-tailed tests, the condition with the higher hypothesized value
% has to be defined as inputA
% flag = 3 for trial permutation (disabled, since not yet debugged). matrix of size # subjects x # trials x #
% time points
if one_vs_two_sided == 1
t_thresh = 1.64;
%t_thresh = 1.28;
elseif one_vs_two_sided == 2
t_thresh = 1.96;
%t_thresh = 1.64;
end
nb_permuts = 10000;
if flag == 1
if isempty(input)
input = inputA - inputB;
end
nb_subj = size(input,1);
nb_comps = power(2,nb_subj);
nb_greater = 0;
[test_stat_orig, cluster_bin, IX_max_cluster, cluster, signed_bin_t_thresh] = test_stat(input, [], [], flag, t_thresh, one_vs_two_sided);
for i = 1:nb_comps
binp = ones(nb_subj,1);
binp_part = -1 *round((dec2bin(i-1) == '1') -0.5);
binp(nb_subj-numel(binp_part)+1:nb_subj) = binp_part;
binp = repmat(binp,[1 size(input,2)]);
[test_stat_perm, ~, ~, ~, ~] = test_stat(binp .* input,[],[],flag, t_thresh, one_vs_two_sided);
nb_greater = nb_greater + (test_stat_perm > test_stat_orig);
end;
p = nb_greater/nb_comps;
elseif flag == 2
input = [inputA; inputB];
[diff_AB, cluster_bin, IX_max_cluster, cluster, signed_bin_t_thresh] = test_stat([], inputA, inputB, flag, t_thresh, one_vs_two_sided);
n_A = size(inputA,1);
n_B = size(inputB,1);
n_tot = n_A + n_B;
get_pot_resamps = nchoosek([1:size(input,1)],n_A);
A_resamp = [];
B_resamp = [];
diff_AB_permut = [];
for o = 1:nb_permuts
j = ceil(rand(1,1)*size(get_pot_resamps,1));
A_resamp(o,:,:) = input(get_pot_resamps(j,:),:);
keep_B = [1:size(input,1)];
for k = 1:size(A_resamp,2)
deleteIX = min(find(keep_B == get_pot_resamps(j,k)));
keep_B = [keep_B(1:deleteIX-1),keep_B(deleteIX+1:end)];
end
B_resamp(o,:,:) = input(keep_B,:);
[diff_AB_permut(o), ~, ~, ~, ~] = test_stat([], squeeze(A_resamp(o,:,:)), squeeze(B_resamp(o,:,:)), flag, t_thresh, one_vs_two_sided);
end
diff_AB_permut = diff_AB_permut;
p = length(find(diff_AB_permut >= diff_AB))/nb_permuts; %%% größer als, nicht größer gleich!!!!!
% elseif flag == 3
% rand('twister',sum(100*clock))
%
% diff_AB = test_stat([], squeeze(nanmean(inputA,2)), squeeze(nanmean(inputB,2)), flag, t_thresh, one_vs_two_sided)
%
% diff_AB_permut = [];
% for o = 1:nb_permuts
% r = 0;
%
% A_resamp = []; B_resamp = [];
% for nbsubj = 1:length(find(isnan(inputA(:,1,1))==0))
%
% inputA_proc = inputA(nbsubj,:,:);
% inputA_proc = inputA_proc(1,~isnan(inputA_proc(1,:,1)),:);
% inputB_proc = inputB(nbsubj,:,:);
% inputB_proc = inputB_proc(1,~isnan(inputB_proc(1,:,1)),:);
% input_proc = cat(2,inputA_proc, inputB_proc);
% n_A = size(inputA_proc,2);
% n_B = size(inputB_proc,2);
%
% resamp = [];
% while r < n_A
% pot_num = ceil(rand(1,1)*(n_A+n_B));
% if ~ismember(pot_num,resamp)
% resamp = [resamp, pot_num];
% r = r + 1;
% end
% end
% A_resamp(nbsubj,:) = squeeze(nanmean(input_proc(:,resamp,:),2));
% keep_B = [1:n_A + n_B];
% for k = 1:length(resamp)
% deleteIX = min(find(keep_B == resamp(k)));
% keep_B = [keep_B(1:deleteIX-1),keep_B(deleteIX+1:end)];
% end
% B_resamp(nbsubj,:) = squeeze(nanmean(input_proc(:,keep_B,:),2));
% end
% diff_AB_permut(o) = test_stat([], A_resamp, B_resamp, flag, t_thresh, one_vs_two_sided);
%
% end
%
% p = length(find(diff_AB_permut >= diff_AB))/nb_permuts; %%% größer als, nicht größer gleich!!!!!
% p
end
%%
function [max_cluster, max_cluster_bin, IX_max_cluster, cluster, signed_bin_t_thresh] = test_stat(input, inputA, inputB, flag, t_thresh, one_vs_two_sided)
if flag == 1
[~,~,~, stats] = ttest(input);
tval = stats.tstat;
elseif flag == 2
[~,~,~, stats] = ttest2(inputA,inputB);
tval = stats.tstat;
elseif flag == 3
[~,~,~, stats] = ttest(inputA-inputB);
tval = stats.tstat;
end
if one_vs_two_sided == 1
bin_t_thresh = tval > t_thresh;
elseif one_vs_two_sided == 2
bin_t_thresh = abs(tval) > t_thresh;
end
cluster = [];
bin_t_thresh = bin_t_thresh;
signed_bin_t_thresh = bin_t_thresh.*sign(tval);
for i = 1:length(bin_t_thresh)
if abs(signed_bin_t_thresh(i)) == 1
if i == 1
cluster(i) = 1;
elseif and(signed_bin_t_thresh(i-1) ~= 0,signed_bin_t_thresh(i-1) == signed_bin_t_thresh(i))
cluster(i) = cluster(i-1);
elseif and(signed_bin_t_thresh(i-1) ~= 0,signed_bin_t_thresh(i-1) ~= signed_bin_t_thresh(i))
cluster(i) = cluster(i-1) + 1;
elseif signed_bin_t_thresh(i-1) == 0
cluster(i) = max(cluster) + 1;
end
else
cluster(i) = 0;
end
end
count_cluster = max(cluster);
sum_t_clusters = [];
for i = 1:count_cluster
if one_vs_two_sided == 1
sum_t_clusters(i) = nansum(tval(cluster==i));
if sum_t_clusters(i) < 0
sum_t_clusters(i) = 0;
end
elseif one_vs_two_sided == 2
sum_t_clusters(i) = abs(nansum(tval(cluster==i)));
end
end
[max_cluster, IX_max_cluster] = max(sum_t_clusters);
if ~isempty(IX_max_cluster)
max_cluster_bin = cluster == IX_max_cluster;
else
max_cluster_bin = zeros(size(cluster,1),size(cluster,2));
end
if isempty(max_cluster)
max_cluster = 0;
end