BIARfit: Fitted Values of BIAR model

In iAR: Irregularly Observed Autoregressive Models

Fitted Values of BIAR model

Description

Fit a BIAR model to a bivariate irregularly observed time series.

Usage

BIARfit(phiValues, y1, y2, t, yerr1, yerr2, zeroMean = TRUE)

Arguments

phiValues	An array with the parameters of the BIAR model. The elements of the array are, in order, the autocorrelation and the cross correlation parameter of the BIAR model.
y1	Array with the observations of the first time series of the BIAR process.
y2	Array with the observations of the second time series of the BIAR process.
t	Array with the irregular observational times.
yerr1	Array with the measurements error standard deviations of the first time series of the BIAR process.
yerr2	Array with the measurements error standard deviations of the second time series of the BIAR process.
zeroMean	logical; if TRUE, the array y has zero mean; if FALSE, y has a mean different from zero.

Value

A list with the following components:

• rho Estimated value of the contemporary correlation coefficient.

• innov.var Estimated value of the innovation variance.

• fitted Fitted values of the BIAR model.

• fitted.state Fitted state values of the BIAR model.

• Lambda Lambda value estimated by the BIAR model at the last time point.

• Theta Theta array estimated by the BIAR model at the last time point.

• Sighat Covariance matrix estimated by the BIAR model at the last time point.

• Qt Covariance matrix of the state equation estimated by the BIAR model at the last time point.

References

\insertRef

Elorrieta_2021iAR

See Also

gentime, BIARsample, BIARphikalman, BIARkalman

Examples

n=80
set.seed(6714)
st<-gentime(n)
x=BIARsample(n=n,phiR=0.9,phiI=0.3,st=st,rho=0.9)
y=x$y
y1=y/apply(y,1,sd)
yerr1=rep(0,n)
yerr2=rep(0,n)
biar=BIARkalman(y1=y1[1,],y2=y1[2,],t=st,delta1 = yerr1,delta2=yerr2)
biar
predbiar=BIARfit(phiValues=c(biar$phiR,biar$phiI),y1=y1[1,],y2=y1[2,],t=st,yerr1
 = rep(0,length(y[1,])),yerr2=rep(0,length(y[1,])))
rho=predbiar$rho
print(rho)
yhat=predbiar$fitted

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