run permutation testΒΆ

Matlab output: run_permutation_test

%% Permutation test example
%
% A simple example of running a permutation test to determine the
% signifance of classification accuracies
%
% #   For CoSMoMVPA's copyright information and license terms,   #
% #   see the COPYING file distributed with CoSMoMVPA.           #

%% Set the number of permutations
niter=1000;

%% Define dataset, classifier, partitioner
config=cosmo_config();
data_path=fullfile(config.tutorial_data_path,'ak6','s01');

data_fn=fullfile(data_path,'glm_T_stats_perrun.nii');
mask_fn=fullfile(data_path,'vt_mask.nii');
ds=cosmo_fmri_dataset(data_fn,'mask',mask_fn,...
                        'targets',repmat(1:6,1,10),...
                        'chunks',floor(((1:60)-1)/6)+1);


% remove constant features
ds=cosmo_remove_useless_data(ds);

% Only consider four classes (otherwise the classifier does extremily well)
ds=cosmo_slice(ds,ds.sa.targets<=4);

classifier=@cosmo_classify_nn;

% for more speed, just do odd-even partitioning
partitions=cosmo_oddeven_partitioner(ds);

%% compute classification accuracy of the original data
[pred, acc]=cosmo_crossvalidate(ds, classifier, partitions);

%% prepare for permutations
acc0=zeros(niter,1); % allocate space for permuted accuracies
ds0=ds; % make a copy of the dataset

%% for _niter_ iterations, reshuffle the labels and compute accuracy
% Use the helper function cosmo_randomize_targets
for k=1:niter
    ds0.sa.targets=cosmo_randomize_targets(ds);
    [foo, acc0(k)]=cosmo_crossvalidate(ds0, classifier, partitions);
end

p=sum(acc<acc0)/niter;
fprintf('%d permutations: accuracy=%.3f, p=%.4f\n', niter, acc, p);

bins=0:10/niter:1;
h=histc(acc0,bins);
bar(bins,h)
hold on
line([acc acc],[0,max(h)])
hold off
title(sprintf('acc=%.3f',acc))