plot.fks | R Documentation |
Plotting method for objects of class fks
. This function
provides tools visualisation of the state vector of the Kalman smoother output
## S3 method for class 'fks' plot(x, CI = 0.95, ahatt.idx = 1:nrow(x$ahatt), ...)
x |
The output of |
CI |
The confidence interval in case |
ahatt.idx |
An vector giving the indexes of the predicted state variables
which shall be plotted if |
... |
Arguments passed to either |
The state variables are plotted. By the argument ahatt.idx
, the user can specify
which of the smoothed (a(t|n)) state variables will be drawn.
fks
## <---------------------------------------------------------------------------> ## Example 3: Local level model for the treering data ## <---------------------------------------------------------------------------> ## Transition equation: ## alpha[t+1] = alpha[t] + eta[t], eta[t] ~ N(0, HHt) ## Measurement equation: ## y[t] = alpha[t] + eps[t], eps[t] ~ N(0, GGt) y <- treering y[c(3, 10)] <- NA # NA values can be handled ## Set constant parameters: dt <- ct <- matrix(0) Zt <- Tt <- array(1,c(1,1,1)) a0 <- y[1] # Estimation of the first width P0 <- matrix(100) # Variance of 'a0' ## Estimate parameters: fit.fkf <- optim(c(HHt = var(y, na.rm = TRUE) * .5, GGt = var(y, na.rm = TRUE) * .5), fn = function(par, ...) -fkf(HHt = array(par[1],c(1,1,1)), GGt = array(par[2],c(1,1,1)), ...)$logLik, yt = rbind(y), a0 = a0, P0 = P0, dt = dt, ct = ct, Zt = Zt, Tt = Tt) ## Filter tree ring data with estimated parameters: fkf.obj <- fkf(a0, P0, dt, ct, Tt, Zt, HHt = array(fit.fkf$par[1],c(1,1,1)), GGt = array(fit.fkf$par[2],c(1,1,1)), yt = rbind(y)) fks.obj <- fks(fkf.obj) plot(fks.obj) lines(as.numeric(y),col="blue")
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