bcpa (version 1.3.2)

GetL: Obtain likelihood estimates of gappy Gaussian time series

Description

Obtain likelihood of gappy standardized Gaussian time series "x" sampled at times "t" given parameter "rho" (autocorrelation). Alternatively computes the characteristic time scale "tau".

Arguments

x

Time series

t

Sampling times

rho

Auto-correlation

tau

logical: Whether or not to compute characteristic time scale instead of rho.

Value

Returns the log-likelihood of the data.

See Also

Core function of BCPA, used directly in GetRho

Examples

# NOT RUN {
# simulate autocorrelated time series
  rho.true <- 0.8
  x.full <- arima.sim(1000, model=list(ar = rho.true))
  t.full <- 1:1000
  
# subsample time series
  keep <- sort(sample(1:1000, 200))
  x <- x.full[keep]
  t <- t.full[keep]
  plot(t,x, type="l")
  
# Obtain MLE of rho
  rhos <- seq(0,.99,.01)
  L <- sapply(rhos, function(r) GetL(x, t, r))
  rho.hat <- rhos[which.max(L)]
  plot(rhos, L, type = "l")
  abline(v = c(rho.true, rho.hat), lty=3:2, lwd=2)
  legend("bottomleft", legend=c("true value","MLE"), lty=3:2, lwd=2, 
         title = expression(rho))
         
# Why tau is better
  tau.true <- -1/log(rho.true)
  taus <- seq(1,10,.1)
  L <- sapply(taus, function(r) GetL(x, t, r, tau = TRUE))
  tau.hat <- taus[which.max(L)]

  plot(taus, L, type = "l")
  abline(v = c(tau.true, tau.hat), lty=3:2, lwd=2)
  legend("bottomleft", legend=c("true value","MLE"), lty=3:2, lwd=2, 
         title = expression(tau))
# }