MultipleSimulationVLtimeseries: MultipleSimulationVLtimeseries

In VLTimeCausality: Variable-Lag Time Series Causality Inference Framework

MultipleSimulationVLtimeseries

Description

MultipleSimulationVLtimeseries is a support function for generating a set of time series TS[,1],...TS[,10]. TS[,1],TS[,2],TS[,3] are causes X time series that are generated independently. The rest of time series are Y time series that are effects of some causes TS[,1],TS[,2],TS[,3]. TS[,1] causes TS[,4],TS[,7],TS[,8], and TS[,10]. TS[,2] causes TS[,5],TS[,7],TS[,9], and TS[,10]. TS[,3] causes TS[,6],TS[,8],TS[,9], and TS[,10].

Usage

MultipleSimulationVLtimeseries(
  n = 200,
  lag = 5,
  YstFixInx = 110,
  YfnFixInx = 170,
  XpointFixInx = 100,
  arimaFlag = TRUE,
  seedVal = -1
)

Arguments

n	is length of time series.
lag	is a time lag between X and Y s.t. Y[t] is approximately X[t-lag].
YstFixInx	is the starting point of variable lag part.
YfnFixInx	is the end point of variable lag part.
XpointFixInx	is a point in X s.t. Y[YstFixInx:YfnFixInx]= X[XpointFixInx] .
arimaFlag	is ARMA model flag. If it is true, then X is generated by ARMA model. If it is false, then X is generated by sampling of the standard normal distribution.
seedVal	is a seed parameter for generating random noise.

Value

This function returns a list of time series TS.

Examples

# Generate simulation data
TS <- MultipleSimulationVLtimeseries()

VLTimeCausality documentation built on May 29, 2024, 2:44 a.m.