diffGranger: Directed conditional granger causality

In FIAR: Functional Integration Analysis in R

Description

Compute the difference conditional granger causality of multivariate timeseries.

Usage

diffGranger(data, nx = 1, ny = 1, order=1, perm = FALSE, bs = 100)

Arguments

data	object containing all observations (rows) and variables (columns) that are being considered. The variables should be ordered as follows: First the variables that are supposed to granger cause a set of other variables (>=1). Then the set of variables (>=1) that are Granger caused by the first set of variables. Finally, a set of variables to condition on(>=1).
nx	The number of variables (>=1) that Granger cause a set of other variables (default = 1), conditioned on a third set of variables (>=1).
ny	The number of variables (>=1) that are Granger caused by the first nx variables (default = 1), conditioned on a third set of variables (>=1).
order	Autoregressive order (>=1) of timeseries. Can be computed using ARorder().
perm	Logical. If perm = FALSE (default), only the Granger causality measure is produced. If perm = TRUE, the Granger test is computed and a permutation test is performed to do inference.
bs	Number of permutation samples. Only works when perm = TRUE. Default=100

Details

The total linear dependence between X and Y can be divided in three components: a directed influence from X to Y, a directed influence from Y to X and an undirected instantaneous influence between them. The difference granger causality from X to Y computed in the function diff.granger() subtracts the conditional granger causality from Y to X from the conditional granger causality from X to Y. This can be used as a measure of how much stronger (weaker) one directed influence is compared to the opposite directed influence.

Value

Partial Granger causality measure F1 plus p -value (Only when perm=TRUE).

Author(s)

Bjorn Roelstraete

References

Roebroeck, A., Formisano, E., Goebel, R. (2005). Mapping directed influence over the brain using Granger causality. NeuroImage 230-242.

See Also

condGranger, pdiffGranger

Examples

# Example data with 5 regions x, y, z, q, w
head(grangerdata)

# Calculate AR() order of the data
ARorder(grangerdata, max=10)
 
# Compute difference conditional granger causality of region x to regions y o
# and z, conditional on regions q and w
F <- diffGranger(grangerdata, nx=1, ny=2, order=3)

# Compute F and bootstrap H0 distribution
F <- diffGranger(grangerdata, nx=1, ny=2, order=3, perm=TRUE, bs=50)

FIAR documentation built on June 5, 2018, 5:03 p.m.