# IARinterpolation: Interpolation from IAR model In iAR: Irregularly Observed Autoregressive Models

 IARinterpolation R Documentation

## Interpolation from IAR model

### Description

Interpolation of missing values from models fitted by `IARkalman`

### Usage

```IARinterpolation(
x,
y,
st,
delta = 0,
yini = 0,
zero.mean = TRUE,
standardized = TRUE
)
```

### Arguments

 `x` A given phi coefficient of the IAR model. `y` Array with the time series observations. `st` Array with the irregular observational times. `delta` Array with the measurements error standard deviations. `yini` a single value, initial value for the estimation of the missing value of the time series. `zero.mean` logical; if TRUE, the array y has zero mean; if FALSE, y has a mean different from zero. `standardized` logical; if TRUE, the array y is standardized; if FALSE, y contains the raw time series.

### Value

A list with the following components:

• fitted Estimation of a missing value of the IAR process.

• ll Value of the negative log likelihood evaluated in the fitted missing values.

### References

\insertRef

Eyheramendy_2018iAR

`gentime`, `IARsample`, `IARkalman`

### Examples

```set.seed(6714)
st<-gentime(n=100)
y<-IARsample(phi=0.99,st=st,n=100)
y<-y\$series
phi=IARkalman(y=y,st=st)\$phi
print(phi)
napos=10
y0=y
y[napos]=NA
xest=phi
yest=IARinterpolation(xest,y=y,st=st)
yest\$fitted
mse=(y0[napos]-yest\$fitted)^2
print(mse)
plot(st,y,type='l',xlim=c(st[napos-5],st[napos+5]))
points(st,y,pch=20)
points(st[napos],yest\$fitted,col="red",pch=20)
```

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