dataRWAR: Generating data function with the deCAFS model

In vrunge/ARRWestim: Estimation of the parameters for the ARRW (Autoregressive AR(1) and Random Walk) model for time-series

Description

Generate a Realization from the deCAFS model (see references for further details).

y_t = μ_t + ε_t

where

μ_t = μ_{t-1} + η_t + δ_t, \quad η_t \sim N(0, σ_η^2), \ δ_t \ \in R

and

ε_t = φ ε_{t-1} + ν_t \quad ν_t \sim N(0, σ_ν^2)

Usage

dataRWAR(
  N = 1000,
  sdEta = 1,
  sdNu = 1,
  phi = 0.5,
  type = c("none", "up", "updown", "rand1"),
  nbSeg = 20,
  jumpSize = 1,
  seed = 42
)

Arguments

N	number of data points
sdEta	standard deviation in Random Walk
sdNu	standard deviation in AR(1)
phi	AR(1) autocorrelation parameter
type	possible scenarios for the jump structure
nbSeg	number of segments
jumpSize	max size of the jumps
seed	random number generator seed (default is 42)

Value

A list containing:

y

the data sequence,

signal

the underlying signal without the superimposed AR(1) noise,

changepoints

the changepoint indices

Examples

myData <- dataRWAR(1000, sdEta = 0.1, sdNu = 0.1, type = "rand1",  nbSeg = 10, seed = 86)

vrunge/ARRWestim documentation built on Feb. 20, 2021, 9:35 a.m.