BIARsample: Simulate from a BIAR Model

In iAR: Irregularly Observed Autoregressive Models

Simulate from a BIAR Model

Description

Simulates a BIAR Time Series Model

Usage

BIARsample(n, st, phiR, phiI, delta1 = 0, delta2 = 0, rho = 0)

Arguments

n	Length of the output bivariate time series. A strictly positive integer.
st	Array with observational times.
phiR	Autocorrelation coefficient of BIAR model. A value between -1 and 1.
phiI	Crosscorrelation coefficient of BIAR model. A value between -1 and 1.
delta1	Array with the measurements error standard deviations of the first time series of the bivariate process.
delta2	Array with the measurements error standard deviations of the second time series of the bivariate process.
rho	Contemporary correlation coefficient of BIAR model. A value between -1 and 1.

Details

The chosen phiR and phiI values must satisfy the condition $|phiR + i phiI| < 1$.

Value

A list with the following components:

• y Matrix with the simulated BIAR process.

• t Array with observation times.

• Sigma Covariance matrix of the process.

References

\insertRef

Elorrieta_2021iAR

See Also

gentime

Examples

n=300
set.seed(6714)
st<-gentime(n)
x=BIARsample(n=n,phiR=0.9,phiI=0.3,st=st)
plot(st,x$y[1,],type='l')
plot(st,x$y[2,],type='l')
x=BIARsample(n=n,phiR=-0.9,phiI=-0.3,st=st)
plot(st,x$y[1,],type='l')
plot(st,x$y[2,],type='l')

iAR documentation built on Nov. 25, 2022, 1:06 a.m.