mvgc_confint
Confidence intervals for sample MVGC based on theoretical asymptotic distribution
Syntax
[xup,xlo] = mvgc_confint(alpha,x,p,m,N,nx,ny,nz,tstat)
Arguments
See also Common variable names and data structures.
input
alpha significance level (scalar) 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
xup matrix of upper confidence bounds xlo matrix of lower confidence bounds
Description
Return upper and lower confidence bounds [xup,xlo] at significance level alpha for sample MVGCs in x, based on theoretical (asymptotic) distributions. NaN s are ignored. See mvgc_cdfi for details of other parameters.
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_pval | mvgc_cval | mvgc_demo_confint