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

MVGC Toolbox v1.0. © Lionel Barnett and Anil K. Seth, 2012.
See file license.txt for licensing terms.