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.