autocov_to_spwcgc
Calculate pairwise-conditional frequency-domain MVGCs (spectral multivariate Granger causalites)
Syntax
[f,fres] = autocov_to_spwcgc(G,fres,useFFT)
Arguments
See also Common variable names and data structures.
input
G autocovariance sequence fres frequency resolution useFFT use FFT method for autocovariance transform (default: as for autocov_xform)
output
f spectral Granger causality matrix
Description
Returns the matrix f of pairwise-conditional frequency-domain (spectral) MVGCs
(where [ij] denotes omission of the ij-th variables) between all pairs of variables 
represented in G, for a stationary VAR process with autocovariance sequence G. The first index i of f is the target (causee) variable, the second j the source (causal) variable and the third indexes the frequency. See ref. [1] for details.
Spectral causality is calculated up to the Nyqvist frequency at a resolution fres. If fres is not supplied it is calculated optimally as the number of autocovariance lags. Call freqs = sfreqs(fres,fs), where fs is the sampling rate, to get a corresponding vector freqs of frequencies on [0,fs/2].
The useFFT flag specifies the algorithm used to transform the autocovariance sequence; see autocov_xform for details.
The caller should take note of any warnings issued by this function and test results with a call isbad|(f,false)|.
For details of the algorithm, see autocov_to_smvgc and [1].
References
[1] L. Barnett and A. K. Seth, The MVGC Multivariate Granger Causality Toolbox: A New Approach to Granger-causal Inference, J. Neurosci. Methods 223, 2014 [ preprint ].
See also
autocov_to_smvgc | autocov_to_pwcgc | autocov_to_var | var2trfun | autocov_xform | sfreqs | isbad