mvn.sim.2.cps: Simulate multivariate normal data with 2 change points

In vccp: Vine Copula Change Point Detection in Multivariate Time Series

Description

This function simulates a multivariate normal data set with 2 change points in the network structure.

Usage

mvn.sim.2.cps(nobs, n_ser, seed = 101)

Arguments

nobs	A positive integer, which defines the length of the time series. It must be a multiple of 3 since change points occur at time points T/3 and 2T/3.
n_ser	A positive integer number indicating the dimensionality of the time series. n_ser must be equal to or larger than 8 as mvn.sim.2.cps generates 3 different network structures among 8 connected nodes. The remaining variables are generated as independent data.
seed	A positive integer with default value equal to 101. It is used to ensure reproducibility.

Value

A nobs * n_ser matrix with 2 change points at time points nobs/3+1 and nobs*2/3+1.

Examples

## Simulate MVN data with 2 change points
data <- mvn.sim.2.cps(180, 8, seed = 101)

vccp documentation built on May 29, 2021, 5:06 p.m.