Extracting the Partial Signal Of a State Space Model
Function signal returns the signal of a state space model using only
subset of states.
signal(object, states = "all", filtered = FALSE)
object |
Object of class |
states |
Which states are combined? Either a numeric vector containing
the indices of the corresponding states, or a character vector defining the
types of the corresponding states. Possible choices are
|
filtered |
If |
signal |
Time series object of filtered signal Z[t]a[t] or smoothed signal Z[t]α[t] using only the defined states. |
variance |
Cov(Z[t]a[t]) or Cov(Z[t]α[t]) using only the defined states. For the covariance matrices of the filtered signal, only the non-diffuse part of P is used. |
model <- SSModel(log(drivers) ~ SSMtrend(1, NA) +
SSMseasonal(12, sea.type = 'trigonometric', Q = NA) +
log(PetrolPrice) + law,data = Seatbelts, H = NA)
ownupdatefn <- function(pars,model,...){
model$H[] <- exp(pars[1])
diag(model$Q[,,1]) <- exp(c(pars[2], rep(pars[3], 11)))
model
}
fit <- fitSSM(inits = log(c(var(log(Seatbelts[,'drivers'])), 0.001, 0.0001)),
model = model, updatefn = ownupdatefn, method = 'BFGS')
out <- KFS(fit$model, smoothing = c('state', 'mean'))
ts.plot(cbind(out$model$y, fitted(out)),lty = 1:2, col = 1:2,
main = 'Observations and smoothed signal with and without seasonal component')
lines(signal(out, states = c('regression', 'trend'))$signal, col = 4, lty = 1)
legend('bottomleft',
legend = c('Observations', 'Smoothed signal','Smoothed level'),
col = c(1, 2, 4), lty = c(1, 2, 1))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.