IARinterpolation: Interpolation from IAR model

In iAR: Irregularly Observed Autoregressive Models

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

See Also

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.