multipleVLGrangerFunc: multipleVLGrangerFunc

In VLTimeCausality: Variable-Lag Time Series Causality Inference Framework

Description

multipleVLGrangerFunc is a function that infers Variable-lag Granger Causality of all pairwises of m time series TS[,1],...TS[,m].

Usage

multipleVLGrangerFunc(
  TS,
  maxLag,
  alpha = 0.05,
  gamma = 0.3,
  autoLagflag = TRUE,
  causalFlag = 0,
  VLflag = TRUE,
  family = gaussian
)

Arguments

TS	is a numerical time series of effect where TS[t,k] is an element at time t of kth time series.
maxLag	is a maximum possible time delay. The default is 0.2*length(Y).
alpha	is a significance level of F-test to determine whether X Granger-causes Y. The default is 0.05.
gamma	is a parameter to determine whether X Granger-causes Y using BIC difference ratio. The default is 0.3.
autoLagflag	is a flag for enabling the automatic lag inference function. The default is true. If it is set to be true, then maxLag is set automatically using cross-correlation. Otherwise, if it is set to be false, then the function takes the maxLag value to infer Granger causality.
causalFlag	is a choice of criterion for inferring causality: causalFlag=0 for BIC difference ratio, causalFlag=1 for f-test, or causalFlag=2 for BIC.
VLflag	is a flag of Granger causality choice: either VLflag=TRUE for VL-Granger or VLflag=FALSE for Granger causality.
family	is a parameter of family of function for Generalized Linear Models function (glm). The default is gaussian.

Value

This function returns of a list of an adjacency matrix of causality where adjMat[i,j] is true if TS[,i] causes TS[,j].

Examples

## Generate simulation data
#TS <- MultipleSimulationVLtimeseries()
## Run the function
#out<-multipleVLGrangerFunc(TS)

VLTimeCausality documentation built on Jan. 24, 2022, 5:07 p.m.