bootstrap_tsdata_to_mvgc

Calculate sampling distribution for conditional time-domain MVGC from time series data, based on a nonparametric bootstrap

Syntax

   FB = bootstrap_tsdata_to_mvgc(U,x,y,p,nsamps,acmaxlags,acdectol)

Arguments

See also Common variable names and data structures.

input

   U          multi-trial time series data
   x          vector of indices of target (causee) multi-variable
   y          vector of indices of source (causal) multi-variable
   p          model order (number of lags)
   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 Granger causalities (empirical distribution)

Description

Returns nsamps samples from the empirical sampling distribution of the time-domain MVGC from the variable Y (specified by the vector of indices y) to the variable X (specified by the vector of indices x), conditional on all other variables in 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.

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_pwcgc | bootstrap_tsdata_to_smvgc | bootstrap_tsdata_to_spwcgc | var_to_autocov | autocov_to_mvgc.