extended: Iterated Extended Kalman Smoothing
In ClausDethlefsen/sspir: State Space Models in R

Description Usage Arguments Details Value Author(s) References See Also Examples

An iterative procedure for calculation of the conditional mean and variance of the latent process in non-Gaussian state space models. The method calculates an approximating Gaussian state space model.

extended( ss,
          maxiter = 50,
          epsilon = 1e-06,
          debug = FALSE
        )

`ss`	an object of class `SS`.
`maxiter`	a positive integer giving the maximum number of iterations to run.
`epsilon`	a (small) positive numeric giving the tolerance of the maximum relative differences of m and C between iterations.
`debug`	a logical. If `TRUE`, some extra information is printed.

This is the default method when using kfs on an object of class ssm when the family is not gaussian. The conditional mean and variance can be retrieved using getFit and are then stored in the attributes m and C, respectively.

The object ss with updated components m, C, loglik, iteration, ytilde, x$vtilde, mu. These describe the approximating Gaussian state space model.

Claus Dethlefsen and Søren Lundbye-Christensen.

Durbin J, Koopman SJ (2001). Time series analysis by state space methods. Oxford University Press.

ssm, kfilter, smoother.

data(mumps)
index <- 1:length(mumps) # use 'index' instead of time
model <- ssm( mumps ~ -1 + tvar(polytime(index,1)),
              family=poisson(link=log))
results <- getFit(model)
plot(mumps,type='l',ylab='Number of Cases',xlab='',axes=FALSE)
lines( exp(results$m[,1]), lwd=2)
## Alternatives:
## results2 <- extended(model$ss)
## results3 <- kfs(model) ## yields the same