# BIAR.fit: Fitted Values of BIAR model In iAR: Irregularly Observed Autoregressive Models

## Description

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

## Usage

 `1` ```BIAR.fit(x, y1, y2, t, yerr1, yerr2, zero.mean = "TRUE") ```

## Arguments

 `x` 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. `zero.mean` 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.

`gentime`, `BIAR.sample`, `BIAR.phi.kalman`, `BIAR.kalman`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```n=80 set.seed(6714) st<-gentime(n) x=BIAR.sample(n=n,phi.R=0.9,phi.I=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=BIAR.kalman(y1=y1[1,],y2=y1[2,],t=st,delta1 = yerr1,delta2=yerr2) biar predbiar=BIAR.fit(x=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 ```