IARinterpolation {iAR}

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

Eyheramendy S, Elorrieta F, Palma W (2018). “An irregular discrete time series model to identify residuals with autocorrelation in astronomical light curves.” Monthly Notices of the Royal Astronomical Society, 481(4), 4311–4322. ISSN 0035-8711, doi: 10.1093/mnras/sty2487, https://academic.oup.com/mnras/article-pdf/481/4/4311/25906473/sty2487.pdf.

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)