permtest_tsdata_to_pwcgc

Calculate null distribution for pairwise-conditional time-domain MVGCs from time series data, based on a permutation test

Syntax

  FP = permtest_tsdata_to_pwcgc(U,p,bsize,nsamps,regmode,acmaxlags,acdectol)

Arguments

See also Common variable names and data structures.

input

   U          multi-trial time series data
   p          model order (number of lags)
   bsize      permutation block size (default: use model order)
   nsamps     number of permutations
   regmode    regression mode (default as for 'tsdata_to_var')
   acmaxlags  maximum autocovariance lags  (default as for 'var_to_autocov')
   acdectol   autocovariance decay tolerance (default as for 'var_to_autocov')

output

   FP         permutation test Granger causalities (null distribution)

Description

Returns nsamps samples from the empirical null distribution of the pairwise-conditional time-domain MVGCs from the time series data U, based on randomly permuting blocks of size bsize of the source variable [2]. p is the model order; for other parameters see tsdata_to_var and var_to_autocov.

The first dimension of the returned matrix FP indexes samples, the second indexes the target (causee) variable and the third the source (causal) variable.

For usage in significance testing, see mvgc_demo_permtest.

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] M. J. Anderson and J. Robinson, Permutation tests for linear models, Aust. N. Z. J. Stat. 43(1), 2001.

See also

mvgc_demo_permtest | permtest_tsdata_to_mvgc | permtest_tsdata_to_smvgc | permtest_tsdata_to_spwcgc | tsdata_to_var | var_to_autocov | autocov_to_pwcgc.