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Slepi/Code/Utilities/spm12/external/fieldtrip/connectivity/ft_connectivity_pdc.m
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ft_connectivity_pdc.m
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function [pdc, pdcvar, n] = ft_connectivity_pdc(input, varargin)
% FT_CONNECTIVITY_PDC computes partial directed coherence.
%
% Use as
% [p, v, n] = ft_connectivity_pdc(h, key1, value1, ...)
%
% Input arguments:
% H = spectral transfer matrix, Nrpt x Nchan x Nchan x Nfreq (x Ntime),
% Nrpt can be 1.
%
% additional options need to be specified as key-value pairs and are:
% 'hasjack' = 0 (default) is a boolean specifying whether the input
% contains leave-one-outs, required for correct variance
% estimate
% 'feedback' = string, determining verbosity (default = 'none'), see FT_PROGRESS
%
% Output arguments:
% p = partial directed coherence matrix Nchan x Nchan x Nfreq (x Ntime).
% If multiple observations in the input, the average is returned.
% v = variance of p across observations.
% n = number of observations.
%
% Typically, nrpt should be 1 (where the spectral transfer matrix is
% computed across observations. When nrpt>1 and hasjack is true the input
% is assumed to contain the leave-one-out estimates of H, thus a more
% reliable estimate of the relevant quantities.
% Copyright (C) 2009-2013, Jan-Mathijs Schoffelen
%
% This file is part of FieldTrip, see http://www.ru.nl/neuroimaging/fieldtrip
% for the documentation and details.
%
% FieldTrip is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% FieldTrip is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id: ft_connectivity_pdc.m 7538 2013-02-23 20:40:01Z jansch $
hasjack = ft_getopt(varargin, 'hasjack', 0);
feedback = ft_getopt(varargin, 'feedback', 'none');
% crossterms are described by chan_chan_therest
siz = [size(input) 1];
n = siz(1);
outsum = zeros(siz(2:end));
outssq = zeros(siz(2:end));
% computing pdc is easiest on the inverse of the transfer function
pdim = prod(siz(4:end));
tmpinput = reshape(input, [siz(1:3) pdim]);
ft_progress('init', feedback, 'inverting the transfer function...');
for k = 1:n
ft_progress(k/n, 'inverting the transfer function for replicate %d from %d\n', k, n);
tmp = reshape(tmpinput(k,:,:,:), [siz(2:3) pdim]);
for m = 1:pdim
tmp(:,:,m) = inv(tmp(:,:,m));
end
tmpinput(k,:,:,:) = tmp;
end
ft_progress('close');
input = reshape(tmpinput, siz);
ft_progress('init', feedback, 'computing metric...');
for j = 1:n
ft_progress(j/n, 'computing metric for replicate %d from %d\n', j, n);
invh = reshape(input(j,:,:,:,:), siz(2:end));
den = sum(abs(invh).^2,1);
tmppdc = abs(invh)./sqrt(repmat(den, [siz(2) 1 1 1 1]));
%if ~isempty(cfg.submethod), tmppdc = baseline(tmppdc, cfg.submethod, baselineindx); end
outsum = outsum + tmppdc;
outssq = outssq + tmppdc.^2;
end
ft_progress('close');
pdc = outsum./n;
if n>1,
if hasjack
bias = (n-1).^2;
else
bias = 1;
end
pdcvar = bias*(outssq - (outsum.^2)./n)./(n - 1);
else
pdcvar = [];
end
% this is to achieve the same convention for all connectivity metrics:
% row -> column
for k = 1:prod(siz(4:end))
pdc(:,:,k) = transpose(pdc(:,:,k));
if ~isempty(pdcvar)
pdcvar(:,:,k) = transpose(pdcvar(:,:,k));
end
end
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