In the vignette for the R package dlm: link on page 12, the author runs the function
dlmSmooth to smooth the data and the function returns an object which is assigned to
gasSmooth$s is described in the package help as:
Time series (or matrix) of smoothed values of the state vectors. The series starts one time unit before the first observation.
Based on the fitted model, we can compute the smoothing estimates of the states. This can be used to obtain a decomposition of the data into a smooth trend plus a stochastic seasonal component, subject to measurement error.
and then run:
gasSmooth <- dlmSmooth(lGas, mod = dlmGas) x <- cbind(lGas, dropFirst(gasSmooth$s[,c(1,3)])) colnames(x) <- c("Gas", "Trend", "Seasonal")
The author in the vignette selects to plot columns 1 and 3 of this timeseries matrix, but how do they know that these states represent the smoothed trend and the seasonal trend?