bootstrap_tsdata_to_spwcgc
Calculate sampling distribution for pairwise-conditional frequency-domain MVGCs from time series data, based on a nonparametric bootstrap
Syntax
fB = bootstrap_tsdata_to_spwcgc(U,p,fres,nsamps,acmaxlags,acdectol)
Arguments
See also Common variable names and data structures.
input
U multi-trial time series data p model order (number of lags) fres frequency resolution (default: automatic) nsamps number of bootstrap samples acmaxlags maximum autocovariance lags (default as for 'var_to_autocov') acdectol autocovariance decay tolerance (default as for 'var_to_autocov')
output
fB bootstrap spectral Granger causalities (empirical distribution)
Description
Returns nsamps samples from the empirical sampling distribution of the pairwise-conditional frequency-domain MVGCs from the time series data U. The bootstrap randomly samples (with replacement) residuals of the full autoregression of U on its own lags; the subsampled residuals are then added back to the corresponding predictors to form surrogate time series [2]. p is the model order; for other parameters see var_to_autocov.
The first dimension of the returned matrix fP indexes samples, the second indexes the target (causee) variable, the third the source (causal) variable and the fourth frequency.
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].
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 ].
[2] D. A. Freedman, Bootstrapping regression models, Ann. Stats. 9(6), 1981.
See also
mvgc_demo_bootstrap | bootstrap_tsdata_to_mvgc | bootstrap_tsdata_to_pwcgc | bootstrap_tsdata_to_smvgc | var_to_autocov | autocov_to_spwcgc | sfreqs.