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reference:ft_connectivity_pdc [2018/08/23 14:43] (current)
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 +=====  FT_CONNECTIVITY_PDC =====
 +Note that this reference documentation is identical to the help that is displayed in MATLAB when you type "help ft_connectivity_pdc"​.
 +  <a href=/​reference/​ft_connectivity_pdc><​font color=green>​FT_CONNECTIVITY_PDC</​font></​a>​ computes partial directed coherence. This function implements
 +  the metrices described in Baccala et al., Biological Cybernetics 2001, 84(6),
 +  463-74. and in Baccala et al., 15th Int.Conf.on DSP 2007, 163-66.
 +  The implemented algorithm has been tested against the implementation in the
 +  SIFT-toolbox. It yields numerically identical results to what is known there as
 +  '​nPDC'​ (for PDC) and '​GPDC'​ for generalized pdc.
 +  Use as
 +    [p, v, n] = ft_connectivity_pdc(h,​ key1, value1, ...)
 +  The input argument H should be a spectral transfer matrix organized as
 +    Nrpt x Nchan x Nchan x Nfreq (x Ntime),
 +  where Nrpt can be 1.
 +  Additional optional input arguments come as key-value pairs:
 +    '​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 <a href=/​reference/​ft_progress><​font color=green>​FT_PROGRESS</​font></​a>​
 +    '​invfun' ​  = '​inv'​ (default) or '​pinv',​ the function used to invert the
 +                 ​transfer matrix to obtain the fourier transform of the
 +                 MVAR coefficients. Use '​pinv'​ if the data are
 +                 ​poorly-conditioned.
 +    '​noisecov'​ = matrix containing the covariance of the residuals of the
 +                 MVAR model. If this matrix is defined, the function
 +                 ​returns the generalized partial directed coherence.
 +  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&​gt;​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.
 +  See also <a href=/​reference/​ft_connectivityanalysis><​font color=green>​FT_CONNECTIVITYANALYSIS</​font></​a>​