permtest_tsdata_to_mvgc
Calculate null distribution for conditional time-domain MVGC from time series data, based on a permutation test
Syntax
FP = permtest_tsdata_to_mvgc(U,x,y,p,bsize,nsamps,regmode,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) 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 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, based on randomly permuting blocks of size bsize of the source variable Y [2]. p is the model order; for other parameters see tsdata_to_var and 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] 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_pwcgc | permtest_tsdata_to_smvgc | permtest_tsdata_to_spwcgc | tsdata_to_var | var_to_autocov | autocov_to_mvgc.