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limo_eeg_tf.m
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limo_eeg_tf.m
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function limo_eeg_tf(varargin)
% Forked from limo_eeg to run time-frequncy analyses
% This mostly runs parallel to limo_eeg{3} and +, but adapted for 4D tf
% data - the LIMO.mat is created and updated via limo_eeg.m - if data are
% 4D then limo_eeg_tf is called.
%
% IMPUT limo_eeg_tf(4) to run the GLM with/without bootstrap and tfce
% limo_eeg_tf(5) make figures
% limo_eeg(6) contrasts
%
% Andrew X Stewart, Nov 2013
% Cyril Pernet February 2014
% ---------------------------
% Copyright (C) LIMO Team 2014
global LIMO
if ~isnumeric(cell2mat(varargin))
error('case number expected - see help')
end
switch varargin{1}
% case{1} - see limo_eeg = GUI
% case{2} - see limo_eeg = IMPORT
% case{3} - see limo_eeg = DESIGN MATRIX
case{4}
% NBOOT (updated if specified in LIMO.design)
% ------------------------------------------
nboot = 1000;
% ----------
% get the LIMO.mat
try
load('LIMO.mat');
catch
[file,dir_path,ind] = uigetfile('LIMO.mat','select a LIMO.mat file');
if ind ==0
return
else
cd (dir_path); load LIMO.mat;
end
end
cd(LIMO.dir);
% ---------------- univariate analysis ------------------
% --------------------------------------------------------
if strcmp(LIMO.design.type_of_analysis,'Mass-univariate')
% --------- load files created by limo_design_matrix ------------------
load Yr; % load a 4D Yr with tf data,
if sum(size(Yr) ~= LIMO.data.size4D)~=0; % then check it is so
errordlg('Is 4D data given to limo_design_matrix_tf?','LIMO.data.size4D'); return
end
Yr = limo_tf_4d_reshape(Yr); % reshape to 3D
if sum(size(Yr) ~= LIMO.data.size3D)~=0; % confirm now shaped 3D
errordlg('4D data are not reshaped correctly!','LIMO.data.size3D'); return
end
Yhat = zeros(LIMO.data.size3D);
Res = zeros(LIMO.data.size3D);
Betas = zeros(LIMO.data.size3D(1),LIMO.data.size3D(2),size(LIMO.design.X,2));
R2 = zeros(LIMO.data.size3D(1),LIMO.data.size3D(2),3);
% ------------- prepare weight matrix -------------------------------------
if strcmp(LIMO.design.method,'WLS') || strcmp(LIMO.design.method,'OLS')
W = ones(LIMO.data.size3D(1),LIMO.data.size4D(2),LIMO.data.size3D(3));
elseif strcmp(LIMO.design.method,'IRLS')
W = zeros(size(Yr));
end
% ------------ prepare condition/covariates -------------------
if LIMO.design.nb_conditions ~=0
tmp_Condition_effect = NaN(size(Yr,1),size(Yr,2),length(LIMO.design.nb_conditions),2);
end
if LIMO.design.nb_interactions ~=0
tmp_Interaction_effect = NaN(size(Yr,1),size(Yr,2),length(LIMO.design.nb_interactions),2);
end
if LIMO.design.nb_continuous ~=0
tmp_Covariate_effect = NaN(size(Yr,1),size(Yr,2),LIMO.design.nb_continuous,2);
end
% -------------- loop the analysis electrode per electrode
if size(Yr,1) == 1
array = 1;
else
array = find(~isnan(Yr(:,1,1))); % skip empty electrodes
end
if strcmp(LIMO.design.status,'to do')
update = 1;
X = LIMO.design.X;
for e = 1:size(array,1)
electrode = array(e); warning off;
fprintf('analyzing channel %g/%g \n',electrode,size(Yr,1));
if LIMO.Level == 2
Y = squeeze(Yr(electrode,:,:));
index = find(~isnan(Y(1,:)));
Y = Y(:,index);
LIMO.design.X = X(index,:);
if size(LIMO.design.X,1) <= size(LIMO.design.X,2)
fprintf('skipping channel %g not enough data \n',electrode);
else
model = limo_glm1(Y',LIMO); warning on;
end
if isempty(index)
index = [1:size(Y,2)];
end
else % level 1 we should not have any NaNs
index = [1:size(Yr,3)];
model = limo_glm1(squeeze(Yr(electrode,:,:))',LIMO);
end
% update the LIMO.mat (do it only once)
if update == 1
LIMO.model.model_df = model.df;
if LIMO.design.nb_conditions ~=0
LIMO.model.conditions_df = model.conditions.df;
end
if LIMO.design.nb_interactions ~=0
LIMO.model.interactions_df = model.interactions.df;
end
if LIMO.design.nb_continuous ~=0
LIMO.model.continuous_df = model.continuous.df;
end
update = 0;
end
% update the files to be stored on the disk
if strcmp(LIMO.design.method,'IRLS')
W(electrode,:,:,index) = model.W';
else
W(electrode,:,index) = model.W';
end
fitted_data = LIMO.design.X*model.betas;
Yhat(electrode,:,index) = fitted_data';
Res(electrode,:,index) = squeeze(Yr(electrode,:,index)) - fitted_data'; clear fitted_data
R2(electrode,:,1) = model.R2_univariate;
R2(electrode,:,2) = model.F;
R2(electrode,:,3) = model.p;
Betas(electrode,:,:,1) = model.betas';
if prod(LIMO.design.nb_conditions) ~=0
if length(LIMO.design.nb_conditions) == 1
tmp_Condition_effect(electrode,:,1,1) = model.conditions.F;
tmp_Condition_effect(electrode,:,1,2) = model.conditions.p;
else
for i=1:length(LIMO.design.nb_conditions)
tmp_Condition_effect(electrode,:,i,1) = model.conditions.F(i,:);
tmp_Condition_effect(electrode,:,i,2) = model.conditions.p(i,:);
end
end
end
if LIMO.design.fullfactorial == 1
if length(LIMO.design.nb_interactions) == 1
tmp_Interaction_effect(electrode,:,1,1) = model.interactions.F;
tmp_Interaction_effect(electrode,:,1,2) = model.interactions.p;
else
for i=1:length(LIMO.design.nb_interactions)
tmp_Interaction_effect(electrode,:,i,1) = model.interactions.F(i,:);
tmp_Interaction_effect(electrode,:,i,2) = model.interactions.p(i,:);
end
end
end
if LIMO.design.nb_continuous ~=0
if LIMO.design.nb_continuous == 1
tmp_Covariate_effect(electrode,:,1,1) = model.continuous.F;
tmp_Covariate_effect(electrode,:,1,2) = model.continuous.p;
else
for i=1:LIMO.design.nb_continuous
tmp_Covariate_effect(electrode,:,i,1) = model.continuous.F(:,i);
tmp_Covariate_effect(electrode,:,i,2) = model.continuous.p(:,i);
end
end
end
end
% save data on the disk and clean out
LIMO.design.X = X;
LIMO.design.weights = W;
LIMO.design.status = 'done';
% Save all tf data as 4D elec x freqs x times x trials
disp('saving results to drive ..'); save LIMO LIMO;
Yhat = limo_tf_4d_reshape(Yhat); save Yhat Yhat -v7.3;
Res = limo_tf_4d_reshape(Res); save Res Res -v7.3;
Betas = limo_tf_4d_reshape(Betas); save Betas Betas -v7.3;
R2 = limo_tf_4d_reshape(R2); save R2 R2 -v7.3;
clear Yhat Res Betas R2
if prod(LIMO.design.nb_conditions) ~=0
for i=1:length(LIMO.design.nb_conditions)
name = sprintf('Condition_effect_%g',i);
if size(tmp_Condition_effect,1) == 1
tmp = squeeze(tmp_Condition_effect(1,:,i,:));
Condition_effect = NaN(1,size(tmp_Condition_effect,2),2);
Condition_effect(1,:,:) = tmp;
else
Condition_effect = squeeze(tmp_Condition_effect(:,:,i,:));
end
Condition_effect = limo_tf_4d_reshape(Condition_effect);
save(name,'Condition_effect','-v7.3')
end
clear Condition_effect tmp_Condition_effect
end
if LIMO.design.fullfactorial == 1
for i=1:length(LIMO.design.nb_interactions)
name = sprintf('Interaction_effect_%g',i);
if size(tmp_Interaction_effect,1) == 1
tmp = squeeze(tmp_Interaction_effect(1,:,i,:));
Interaction_effect = NaN(1,size(tmp_Interaction_effect,2),2);
Interaction_effect(1,:,:) = tmp;
else
Interaction_effect = squeeze(tmp_Interaction_effect(:,:,i,:));
end
Interaction_effect = limo_tf_4d_reshape(Interaction_effect);
save(name,'Interaction_effect','-v7.3')
end
clear Interaction_effect tmp_Interaction_effect
end
if LIMO.design.nb_continuous ~=0
for i=1:LIMO.design.nb_continuous
name = sprintf('Covariate_effect_%g',i);
if size(tmp_Covariate_effect,1) == 1
tmp = squeeze(tmp_Covariate_effect(1,:,i,:));
Covariate_effect = NaN(1,size(tmp_Covariate_effect,2),2);
Covariate_effect(1,:,:) = tmp;
else
Covariate_effect = squeeze(tmp_Covariate_effect(:,:,i,:));
end
Covariate_effect = limo_tf_4d_reshape(Covariate_effect);
save(name,'Covariate_effect','-v7.3')
end
clear Covariate_effect tmp_Covariate_effect
end
clear file electrode filename model reg dir i W
end
% as above for bootstrap under H0
% -------------------------------
boot_go = 0;
if LIMO.design.bootstrap ~=0
if exist('H0','dir')
if strcmp(questdlg('H0 directory detected, overwrite?','data check','Yes','No','No'),'No');
if LIMO.design.tfce == 1
errordlg2('bootstrap skipped - attempting to continue with tfce');
else
return
end
else
boot_go = 1;
end
else
boot_go = 1;
end
else
clear Yr
end
if boot_go == 1
try
fprintf('\n %%%%%%%%%%%%%%%%%%%%%%%% \n Bootstrapping data with the GLM can take a while, be patient .. \n %%%%%%%%%%%%%%%%%%%%%%%% \n')
mkdir H0;
if LIMO.design.bootstrap > 599 || ~exist(nboot,'var')
nboot = LIMO.design.bootstrap;
end
if LIMO.Level == 2
boot_table = limo_create_boot_table(Yr,nboot);
else
boot_table = randi(size(Yr,3),size(Yr,3),nboot);
end
H0_Betas = NaN(size(Yr,1), size(Yr,2), size(LIMO.design.X,2), nboot);
H0_R2 = NaN(size(Yr,1), size(Yr,2), 3, nboot); % stores R, F and p values for each boot
if LIMO.design.nb_conditions ~= 0
tmp_H0_Conditions = NaN(size(Yr,1), size(Yr,2), length(LIMO.design.nb_continuous), 2, nboot);
end
if LIMO.design.nb_interactions ~=0
tmp_H0_Interaction_effect = NaN(size(Yr,1),size(Yr,2),length(LIMO.design.nb_interactions), 2, nboot);
end
if LIMO.design.nb_continuous ~= 0
tmp_H0_Covariates = NaN(size(Yr,1), size(Yr,2), LIMO.design.nb_continuous, 2, nboot);
end
warning off;
X = LIMO.design.X;
h = waitbar(0,'bootstraping data','name','% done');
for e = 1:size(array,1)
electrode = array(e);
waitbar(e/size(array,1))
fprintf('bootstrapping electrode %g \n',electrode);
if LIMO.Level == 2
Y = squeeze(Yr(electrode,:,:));
index = find(~isnan(Y(1,:)));
[rows,cols] = size(X(index,:));
if (rows >= cols)
model = limo_glm1_boot(Y(:,index)',X(index,:),LIMO.design.nb_conditions,LIMO.design.nb_interactions,LIMO.design.nb_continuous,LIMO.design.zscore,LIMO.design.method,boot_table{electrode});
end
else
model = limo_glm1_boot(squeeze(Yr(electrode,:,:))',LIMO,boot_table);
end
% update the files to be stored on the disk
H0_Betas(electrode,:,:,:) = model.Betas;
for B = 1:nboot % now loop because we use cells
H0_R2(electrode,:,1,B) = model.R2{B};
H0_R2(electrode,:,2,B) = model.F{B};
H0_R2(electrode,:,3,B) = model.p{B};
if prod(LIMO.design.nb_conditions) ~=0
if length(LIMO.design.nb_conditions) == 1
tmp_H0_Conditions(electrode,:,1,1,B) = model.conditions.F{B};
tmp_H0_Conditions(electrode,:,1,2,B) = model.conditions.p{B};
else
for i=1:length(LIMO.design.nb_conditions)
tmp_H0_Conditions(electrode,:,i,1,B) = model.conditions.F{B}(i,:);
tmp_H0_Conditions(electrode,:,i,2,B) = model.conditions.p{B}(i,:);
end
end
end
if LIMO.design.fullfactorial == 1
if length(LIMO.design.nb_interactions) == 1
tmp_H0_Interaction_effect(electrode,:,1,1,:) = model.interactions.F{B};
tmp_H0_Interaction_effect(electrode,:,1,2,:) = model.interactions.p{B};
else
for i=1:length(LIMO.design.nb_interactions)
tmp_H0_Interaction_effect(electrode,:,i,1,:) = model.interactions.F{B}(i,:);
tmp_H0_Interaction_effect(electrode,:,i,2,:) = model.interactions.p{B}(i,:);
end
end
end
if LIMO.design.nb_continuous ~=0
if LIMO.design.nb_continuous == 1
tmp_H0_Covariates(electrode,:,1,1,B) = model.continuous.F{B};
tmp_H0_Covariates(electrode,:,1,2,B) = model.continuous.p{B};
else
for i=1:LIMO.design.nb_continuous
tmp_H0_Covariates(electrode,:,i,1,B) = model.continuous.F{B}(:,i);
tmp_H0_Covariates(electrode,:,i,2,B) = model.continuous.p{B}(:,i);
end
end
end
end
end
close(h); warning on;
clear electrode model;
% save data on the disk and clear out
disp('saving H0 data to disk ... ');
save(['H0' filesep 'boot_table'],'boot_table')
H0_Betas = limo_tf_5d_reshape(H0_Betas);
save(['H0' filesep 'H0_Betas'],'H0_Betas','-v7.3')
clear H0_Betas
H0_R2 = limo_tf_5d_reshape(H0_R2);
save(['H0' filesep 'H0_R2'], 'H0_R2', '-v7.3');
clear H0_R2
if prod(LIMO.design.nb_conditions) ~=0
for i=1:length(LIMO.design.nb_conditions)
name = sprintf('H0_Condition_effect_%g',i);
H0_Condition_effect = squeeze(tmp_H0_Conditions(:,:,i,:,:));
H0_Condition_effect = limo_tf_5d_reshape(H0_Condition_effect);
save(['H0' filesep name],'H0_Condition_effect','-v7.3');
clear H0_Condition_effect
end
clear tmp_H0_Conditions
end
if LIMO.design.fullfactorial == 1
for i=1:length(LIMO.design.nb_interactions)
name = sprintf('H0_Interaction_effect_%g',i);
H0_Interaction_effect = squeeze(tmp_H0_Interaction_effect(:,:,i,:,:));
H0_Interaction_effect = limo_tf_5d_reshape(H0_Interaction_effect);
save(['H0' filesep name],'H0_Interaction_effect','-v7.3');
clear H0_Interaction_effect
end
clear tmp_H0_Interaction_effect
end
if LIMO.design.nb_continuous ~=0
for i=1:LIMO.design.nb_continuous
name = sprintf('H0_Covariate_effect_%g',i);
H0_Covariate_effect = squeeze(tmp_H0_Covariates(:,:,i,:,:));
H0_Covariate_effect = limo_tf_5d_reshape(H0_Covariate_effect);
save(['H0' filesep name],'H0_Covariate_effect','-v7.3');
clear H0_Covariate_effect
end
clear tmp_H0_Covariates
end
disp(' ');
catch boot_error
disp('an error occured while attempting to bootstrap or save the data')
fprintf('%s \n',boot_error.message); return
end
cd LIMO.dir
end
% TFCE if requested
% --------------
if LIMO.design.tfce == 1
if isfield(LIMO.data,'neighbouring_matrix') && LIMO.design.bootstrap ~=0
if exist('TFCE','dir')
if strcmp(questdlg('TFCE directory detected, overwrite?','data check','Yes','No','No'),'No');
return
end
end
fprintf('\n %%%%%%%%%%%%%%%%%%%%%%%% \n Computing TFCE for GLM takes a while, be patient .. \n %%%%%%%%%%%%%%%%%%%%%%%% \n')
mkdir TFCE;
% R2
load R2.mat; fprintf('Creating R2 TFCE scores \n');
if size(R2,1) == 1
tfce_score(1,:,:) = limo_tfce(2, squeeze(R2(:,:,:,2)),[]);
else
tfce_score = limo_tfce(3, squeeze(R2(:,:,:,2)),LIMO.data.neighbouring_matrix);
end
save(['H0' filesep 'tfce_R2'],'tfce_score'); clear R2;
cd('H0'); fprintf('Thresholding H0_R2 using TFCE \n'); load H0_R2;
if size(H0_R2,1) == 1
if exist('parfor','file')
tfce_H0_score = NaN(1,size(H0_R2,2),size(H0_R2,3),LIMO.design.bootstrap);
parfor b=1:nboot
tfce_H0_score(1,:,:,b) = limo_tfce(2,squeeze(H0_R2(:,:,:,2,b)),LIMO.data.neighbouring_matrix,0);
end
else
tfce_H0_score(1,:,:,:) = limo_tfce(2, squeeze(H0_R2(:,:,:,2,:)),[]);
end
else
if exist('parfor','file')
tfce_H0_score = NaN(size(H0_R2,1),size(H0_R2,2),size(H0_R2,3),LIMO.design.bootstrap);
parfor b=1:nboot
tfce_H0_score(:,:,:,b) = limo_tfce(3,squeeze(H0_R2(:,:,:,2,b)),LIMO.data.neighbouring_matrix,0);
end
else
tfce_H0_score = limo_tfce(3, squeeze(H0_R2(:,:,:,2,:)),LIMO.data.neighbouring_matrix);
end
end
save('tfce_H0_R2','tfce_H0_score'); clear H0_R2; cd ..;
% conditions
if prod(LIMO.design.nb_conditions) ~=0
for i=1:length(LIMO.design.nb_conditions)
name = sprintf('Condition_effect_%g.mat',i); load(name);
cd('TFCE'); fprintf('Creating Condition %g TFCE scores \n',i)
if size(Condition_effect,1) == 1
tfce_score(1,:,:) = limo_tfce(2, squeeze(Condition_effect(:,:,:,1)),[]);
else
tfce_score = limo_tfce(3, squeeze(Condition_effect(:,:,:,1)),LIMO.data.neighbouring_matrix);
end
full_name = sprintf('tfce_%s',name); save(full_name,'tfce_score');
clear Condition_effect tfce_score; cd ..
end
cd('H0'); fprintf('Creating H0 Condition(s) TFCE scores \n');
for i=1:length(LIMO.design.nb_conditions)
name = sprintf('H0_Condition_effect_%g.mat',i); load(name);
if size(H0_Condition_effect,1) == 1
if exist('parfor','file')
tfce_H0_score = NaN(1,size(H0_Condition_effect,2),size(H0_Condition_effect,3),LIMO.design.bootstrap);
parfor b=1:nboot
tfce_H0_score(:,:,:,b) = limo_tfce(2,squeeze(H0_Condition_effect(:,:,:,1,b)),LIMO.data.neighbouring_matrix,0);
end
else
tfce_H0_score(2,:,:,:) = limo_tfce(2,squeeze(H0_Condition_effect(:,:,:,1,:)),[]);
end
else
if exist('parfor','file')
tfce_H0_score = NaN(size(H0_Condition_effect,1),size(H0_Condition_effect,2),size(H0_Condition_effect,3),LIMO.design.bootstrap);
parfor b=1:nboot
tfce_H0_score(:,:,:,b) = limo_tfce(3,squeeze(H0_Condition_effect(:,:,:,1,b)),LIMO.data.neighbouring_matrix,0);
end
else
tfce_H0_score = limo_tfce(3,squeeze(H0_Condition_effect(:,:,:,1,:)),LIMO.data.neighbouring_matrix);
end
end
full_name = sprintf('tfce_%s',name); save(full_name,'tfce_H0_score');
clear H0_Condition_effect tfce_H0_score;
end
cd ..
end
% interactions
if LIMO.design.fullfactorial == 1
for i=1:length(LIMO.design.fullfactorial)
name = sprintf('Interaction_effect_%g.mat',i); load(name);
cd('TFCE'); fprintf('Creating Interaction %g TFCE scores \n',i)
if size(Interaction_effect,1) == 1
tfce_score(1,:) = limo_tfce(2,squeeze(Interaction_effect(:,:,:,1)),[]);
else
tfce_score = limo_tfce(3,squeeze(Interaction_effect(:,:,:,1)),LIMO.data.neighbouring_matrix);
end
full_name = sprintf('tfce_%s',name); save(full_name,'tfce_score');
clear Interaction_effect tfce_score; cd ..
end
cd('H0'); fprintf('Creating H0 Interaction(s) TFCE scores \n');
for i=1:length(LIMO.design.fullfactorial)
name = sprintf('H0_Interaction_effect_%g.mat',i); load(name);
if size(H0_Interaction_effect,1) == 1
if exist('parfor','file')
tfce_H0_score = NaN(1,size(H0_Interaction_effect,2),size(H0_Interaction_effect,3),LIMO.design.bootstrap);
parfor b=1:nboot
tfce_H0_score(:,:,:,b) = limo_tfce(2,squeeze(H0_Interaction_effect(:,:,:,1,b)),LIMO.data.neighbouring_matrix,0);
end
else
tfce_H0_score(1,:,:) = limo_tfce(2,squeeze(H0_Interaction_effect(:,:,:,1,:)),[]);
end
else
if exist('parfor','file')
tfce_H0_score = NaN(size(H0_Interaction_effect,1),size(H0_Interaction_effect,2),size(H0_Interaction_effect,3),LIMO.design.bootstrap);
parfor b=1:nboot
tfce_H0_score(:,:,:,b) = limo_tfce(3,squeeze(H0_Interaction_effect(:,:,:,1,b)),LIMO.data.neighbouring_matrix,0);
end
else
tfce_H0_score = limo_tfce(3,squeeze(H0_Interaction_effect(:,:,:,1,:)),LIMO.data.neighbouring_matrix);
end
end
full_name = sprintf('tfce_%s',name); save(full_name,'tfce_H0_score');
clear H0_Interaction_effect tfce_H0_score;
end
cd ..
end
% covariates / continuous regressors
if LIMO.design.nb_continuous ~=0
for i=1:LIMO.design.nb_continuous
name = sprintf('Covariate_effect_%g.mat',i); load(name);
cd('TFCE'); fprintf('Creating Covariate %g TFCE scores \n',i);
if size(Covariate_effect,1) == 1
tfce_score(1,:,:) = limo_tfce(2,squeeze(Covariate_effect(:,:,:,1)),[]);
else
tfce_score = limo_tfce(3,squeeze(Covariate_effect(:,:,:,1)),LIMO.data.neighbouring_matrix);
end
full_name = sprintf('tfce_%s',name); save(full_name,'tfce_score');
clear Covariate_effect tfce_score; cd ..
end
cd('H0'); fprintf('Creating H0 Covariate(s) TFCE scores \n');
for i=1:LIMO.design.nb_continuous
name = sprintf('H0_Covariate_effect_%g.mat',i); load(name);
if size(H0_Covariate_effect,1) ==1
if exist('parfor','file')
tfce_H0_score = NaN(1,size(H0_Covariate_effect,2),size(H0_Covariate_effect,3),LIMO.design.bootstrap);
parfor b=1:nboot
tfce_H0_score(:,:,:,b) = limo_tfce(2,squeeze(H0_Covariate_effect(:,:,:,1,b)),LIMO.data.neighbouring_matrix,0);
end
else
tfce_H0_score(1,:,:,:) = limo_tfce(2,squeeze(H0_Covariate_effect(:,:,:,1,:)),[]);
end
else
if exist('parfor','file')
tfce_H0_score = NaN(size(H0_Covariate_effect,1),size(H0_Covariate_effect,2),size(H0_Covariate_effect,3),LIMO.design.bootstrap);
parfor b=1:nboot
tfce_H0_score(:,:,:,b) = limo_tfce(3,squeeze(H0_Covariate_effect(:,:,:,1,b)),LIMO.data.neighbouring_matrix,0);
end
else
tfce_H0_score = limo_tfce(3,squeeze(H0_Covariate_effect(:,:,:,1,:)),LIMO.data.neighbouring_matrix);
end
end
full_name = sprintf('tfce_%s',name); save(full_name,'tfce_H0_score');
clear H0_Covariate_effect tfce_H0_score
end
cd ..
end
elseif ~isfield(LIMO.data,'neighbouring_matrix')
disp('No TFCE performed, neighbourhood matrix missing')
elseif LIMO.design.bootstrap ==0
disp('No TFCE performed, since there was no bootstraps computed')
end
end
% ---------------- multivariate analysis ------------------
% --------------------------------------------------------
elseif strcmp(LIMO.design.type_of_analysis,'Multivariate')
disp('multivariate in time*freq space not supported')
% update = 1;
%
% % --------- load files created by limo_design_matrix ------------------
% load Yr; load Yhat; load Res; load Betas;
%
% % ------------- prepare weight matrice -------------------------------------
% if strcmp(LIMO.design.method,'WLS') || strcmp(LIMO.design.method,'OLS')
% W = ones(size(Yr,1),size(Yr,3));
% elseif strcmp(LIMO.design.method,'IRLS')
% W = ones(size(Yr));
% end
%
% % -------------- loop the analysis time frames per time frames
%
% if strcmp(LIMO.design.status,'to do')
%
% % 1st get weights based on time
% if strcmp(LIMO.design.method,'WLS')
% fprintf('getting trial weights \n')
% array = find(~isnan(Yr(:,1,1))); % skip empty electrodes
% for e = 1:size(Yr,1)
% electrode = array(e); [Betas,W(e,:)] = limo_WLS(LIMO.design.X,squeeze(Yr(electrode,:,:))');
% end
% LIMO.design.weights = W;
% end
%
% % 2nd run the multivative analysis
% for t = 1:size(Yr,2)
% fprintf('analyzing time frame %g/%g \n',t,size(Yr,2));
% model = limo_glmm1(squeeze(Yr(:,t,:))',LIMO); warning off;
%
% % update the LIMO.mat
% if update == 1
% if LIMO.design.nb_conditions ~=0
% LIMO.model.conditions_df = [model.conditions.Roy.df' model.conditions.Roy.dfe' model.conditions.Pillai.df' model.conditions.Pillai.dfe'];
% end
% if LIMO.design.nb_interactions ~=0
% LIMO.model.interactions_df = [model.interactions.Roy.df' model.interactions.Roy.dfe' model.interactions.Pillai.df' model.interactions.Pillai.dfe' ];
% end
% if LIMO.design.nb_continuous ~=0
% LIMO.model.continuous_df = [model.continuous.Roy.df model.continuous.Roy.dfe];
% end
% update = 0;
% end
%
% % update the files to be stored on the disk
% fitted_data = LIMO.design.X*model.betas;
% Yhat(:,t,:) = fitted_data';
% Res(:,t,:) = squeeze(Yr(:,t,:)) - fitted_data'; clear fitted_data
% R2{t} = model.R2;
% Betas(:,t,:) = model.betas';
%
% if prod(LIMO.design.nb_conditions) ~=0
% if length(LIMO.design.nb_conditions) == 1
% tmp_Condition_effect{t} = model.conditions;
% else
% for i=1:length(LIMO.design.nb_conditions)
% tmp_Condition_effect{t}(i).EV = model.conditions.EV(i,:);
% tmp_Condition_effect{t}(i).Roy.F = model.conditions.Roy.F(i);
% tmp_Condition_effect{t}(i).Roy.p = model.conditions.Roy.p(i);
% tmp_Condition_effect{t}(i).Pillai.F = model.conditions.Pillai.F(i);
% tmp_Condition_effect{t}(i).Pillai.p = model.conditions.Pillai.p(i);
% end
% end
% end
%
% if LIMO.design.fullfactorial == 1
% if length(LIMO.design.nb_interactions) == 1
% tmp_Interaction_effect{t} = model.interactions;
% else
% for i=1:length(LIMO.design.nb_interactions)
% tmp_Interaction_effect{t}(i).EV = model.conditions.EV(i,:);
% tmp_Interaction_effect{t}(i).Roy.F = model.conditions.Roy.F(i);
% tmp_Interaction_effect{t}(i).Roy.p = model.conditions.Roy.p(i);
% tmp_Interaction_effect{t}(i).Pillai.F = model.conditions.Pillai.F(i);
% tmp_Interaction_effect{t}(i).Pillai.p = model.conditions.Pillai.p(i);
% end
% end
% end
%
% if LIMO.design.nb_continuous ~=0
% if LIMO.design.nb_continuous == 1
% tmp_Covariate_effect{t} = model.continuous;
% else
% for i=1:LIMO.design.nb_continuous
% tmp_Covariate_effect{t}(i).EV = model.conditions.EV(i,:);
% tmp_Covariate_effect{t}(i).Roy.F = model.conditions.Roy.F(i);
% tmp_Covariate_effect{t}(i).Roy.p = model.conditions.Roy.p(i);
% tmp_Covariate_effect{t}(i).Pillai.F = model.conditions.Pillai.F(i);
% tmp_Covariate_effect{t}(i).Pillai.p = model.conditions.Pillai.p(i);
% end
% end
% end
% end
%
% % save data on the disk and clean out
% LIMO.design.weights = W;
% LIMO.design.status = 'done';
% save LIMO LIMO; save Yhat Yhat;
% save Res Res; save Betas Betas;
% clear Yhat Res Betas
%
% % R2 data
% name = sprintf('R2_EV',i); R2_EV = NaN(size(Yr,1),size(Yr,2));
% for t=1:size(Yr,2); R2_EV(:,t) = real(R2{t}.EV); end
% save(name,'R2_EV','-v7.3')
% name = sprintf('R2'); tmp = NaN(size(Yr,2),5);
% for t=1:size(Yr,2); tmp(t,:) = [R2{t}.V R2{t}.Roy.F R2{t}.Roy.p R2{t}.Pillai.F R2{t}.Pillai.p]; end
% R2 = tmp; save(name,'R2','-v7.3')
%
% % condition effects
% if prod(LIMO.design.nb_conditions) ~=0
% for i=1:length(LIMO.design.nb_conditions)
% name = sprintf('Condition_effect_%g_EV',i);
% if length(LIMO.design.nb_conditions) == 1
% for t=1:size(Yr,2); Condition_effect_EV(:,t) = real(tmp_Condition_effect{t}.EV); end
% save(name,'Condition_effect_EV','-v7.3')
% name = sprintf('Condition_effect_%g',i);
% for t=1:size(Yr,2); Condition_effect(t,:) = [tmp_Condition_effect{t}.Roy.F tmp_Condition_effect{t}.Roy.p tmp_Condition_effect{t}.Pillai.F tmp_Condition_effect{t}.Pillai.p]; end
% save(name,'Condition_effect','-v7.3')
% else
% for t=1:size(Yr,2); Condition_effect_EV(:,t) = real(tmp_Condition_effect{t}(i).EV); end
% save(name,'Condition_effect_EV','-v7.3')
% name = sprintf('Condition_effect_%g',i);
% for t=1:size(Yr,2); Condition_effect(t,:) = [tmp_Condition_effect{t}(i).Roy.F tmp_Condition_effect{t}(i).Roy.p tmp_Condition_effect{t}(i).Pillai.F tmp_Condition_effect{t}(i).Pillai.p]; end
% save(name,'Condition_effect','-v7.3')
% end
% end
% clear Condition_effect Condition_effect_EV tmp_Condition_effect
% end
%
% % interaction effects
% if LIMO.design.fullfactorial == 1
% for i=1:length(LIMO.design.nb_interactions)
% name = sprintf('Interaction_effect_%g_EV',i);
% if length(LIMO.design.nb_interactions) == 1
% for t=1:size(Yr,2); Interaction_effect_EV(:,t) = real(tmp_Interaction_effect{t}.EV); end
% save(name,'Interaction_effect_EV','-v7.3')
% name = sprintf('Interaction_effect_%g',i);
% for t=1:size(Yr,2); Interaction_effect(t,:) = [tmp_Interaction_effect{t}.Roy.F tmp_Interaction_effect{t}.Roy.p tmp_Interaction_effect{t}.Pillai.F tmp_Interaction_effect{t}.Pillai.p]; end
% save(name,'Interaction_effect','-v7.3')
% else
% for t=1:size(Yr,2); Interaction_effect_EV(:,t) = real(tmp_Interaction_effect{t}(i).EV); end
% save(name,'Interaction_effect_EV','-v7.3')
% name = sprintf('Interaction_effect_%g',i);
% for t=1:size(Yr,2); Interaction_effect(t,:) = [tmp_Interaction_effect{t}(i).Roy.F tmp_Interaction_effect{t}(i).Roy.p tmp_Interaction_effect{t}(i).Pillai.F tmp_Interaction_effect{t}(i).Pillai.p]; end
% save(name,'Interaction_effectV','-v7.3')
% end
% end
% clear Interaction_effect Interaction_effect_EV tmp_Interaction_effect
% end
%
% if LIMO.design.nb_continuous ~=0
% for i=1:LIMO.design.nb_continuous
% name = sprintf('Covariate_effect_%g_EV',i);
% if LIMO.design.nb_continuous == 1
% for t=1:size(Yr,2); Covariate_effect_EV(:,t) = real(tmp_Covariate_effect{t}.EV); end
% save(name,'Covariate_effect_EV','-v7.3')
% name = sprintf('Covariate_effect_%g',i);
% for t=1:size(Yr,2); Covariate_effect(t,:) = [tmp_Covariate_effect{t}.Roy.F tmp_Covariate_effect{t}.Roy.p tmp_Covariate_effect{t}.Pillai.F tmp_Covariate_effect{t}.Pillai.p]; end
% save(name,'Covariate_effect','-v7.3')
% else
% for t=1:size(Yr,2); Covariate_effect_EV(:,t) = real(tmp_Covariate_effect{t}(i).EV); end
% save(name,'Covariate_effect_EV','-v7.3')
% name = sprintf('Covariate_effect_%g',i);
% for t=1:size(Yr,2); Covariate_effect(t,:) = [tmp_Covariate_effect{t}(i).Roy.F tmp_Covariate_effect{t}(i).Roy.p tmp_Covariate_effect{t}(i).Pillai.F tmp_Covariate_effect{t}(i).Pillai.p]; end
% save(name,'Covariate_effect','-v7.3')
% end
% end
% clear Covariate_effect Covariate_effect_EV tmp_Covariate_effect
% end
% clear file electrode filename model reg dir i W
% end
%
%
% % if bootsrrap
% if LIMO.design.bootstrap == 1
%
% end
%
% % TFCE if requested
% if LIMO.design.tfce == 1
% end
end
warning on;
end