mvgc_pval
p-values for sample MVGC based on theoretical asymptotic null distribution
Syntax
pval = mvgc_pval(x,p,m,N,nx,ny,nz,tstat)
Arguments
See also Common variable names and data structures.
input
x matrix of MVGC values p VAR model order m number of observations per trial N number of trials nx number of target ("to") variables ny number of source ("from") variables nz number of conditioning variables (default: 0) tstat statistic: 'F' or 'chi2' (default: 'F' if nx == 1, else 'chi2')
output
pval matrix of p-values
Description
Returns p-values pval for sample MVGCs in x, based on theoretical (asymptotic) null distribution. NaN s are ignored. See mvgc_cdf for details of other parameters.
Important: To test p-values for statistical significance you should correct for multiple null hypotheses; see routine significance.
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 ].
See also
mvgc_cdf | mvgc_cdfi | mvgc_confint | mvgc_cval | mvgc_demo | significance