mvgc_cdfi

Sample MVGC thoretical asymptotic inverse cumulative distribution function

Syntax

   x = mvgc_cdfi(P,X,p,m,N,nx,ny,nz,tstat)

Arguments

See also Common variable names and data structures.

input

   P          vector of MVGC cumulative distribution probabilities
   X          vector of actual 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

   x          vector of MVGC values

Description

Return theoretical sample MVGC asymptotic inverse cumulative distribution function for actual MVGCs in vector X, evaluated at probabilities in vector P. To calculate the critical MVGC value for a significance level alpha, assume null hypothesis H₀: X = 0 and set P = 1-alpha (see mvgc_cval). For confidence intervals at level alpha, set X to the estimated MVGC and set P = alpha for lower bound and P = 1-alpha for upper bound (see mvgc_confint).

The theoretical distribution is specified by the tstat parameter, which may be 'F' for Granger's F-test (default if nx == 1) or 'chi2' for Geweke's χ² test (default if nx > 1). For a multivariate predictee (i.e. nx > 1) only the χ² test is suitable [1].

For more details see mvgc_cdf.

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_pval | mvgc_confint | mvgc_cval

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