dlmSmooth {dlm} | R Documentation |
The function apply Kalman smoother to compute smoothed values of the state vectors, together with their variance/covariance matrices.
dlmSmooth(y, ...) ## Default S3 method: dlmSmooth(y, mod, ...) ## S3 method for class 'dlmFiltered' dlmSmooth(y, ..., debug = FALSE)
y |
an object used to select a method. |
... |
futher arguments passed to or from other methods. |
mod |
an object of class |
debug |
if |
The default method returns means and variances of the smoothing
distribution for a data vector (or matrix) y
and a model
mod
.
dlmSmooth.dlmFiltered
produces the same output based on a
dlmFiltered
object, typically one produced by a call to
dlmFilter
.
The calculations are based on the singular value decomposition (SVD) of the relevant matrices. Variance matrices are returned in terms of their SVD.
A list with components
s |
Time series (or matrix) of smoothed values of the state vectors. The series starts one time unit before the first observation. |
U.S |
See below. |
D.S |
Together with |
The observation variance V
in mod
must be nonsingular.
Giovanni Petris GPetris@uark.edu
Zhang, Y. and Li, X.R., Fixed-interval smoothing algorithm
based on singular value decomposition, Proceedings of the 1996
IEEE International Conference on Control Applications.
Giovanni Petris (2010), An R Package for Dynamic Linear
Models. Journal of Statistical Software, 36(12), 1-16.
http://www.jstatsoft.org/v36/i12/.
Petris, Petrone, and Campagnoli, Dynamic Linear Models with
R, Springer (2009).
See dlm
for a description of dlm objects,
dlmSvd2var
to obtain a variance matrix from its SVD,
dlmFilter
for Kalman filtering,
dlmMLE
for maximum likelihood estimation, and
dlmBSample
for drawing from the posterior distribution
of the state vectors.
s <- dlmSmooth(Nile, dlmModPoly(1, dV = 15100, dW = 1470)) plot(Nile, type ='o') lines(dropFirst(s$s), col = "red") ## Multivariate set.seed(2) tmp <- dlmRandom(3, 5, 20) obs <- tmp$y m <- tmp$mod rm(tmp) f <- dlmFilter(obs, m) s <- dlmSmooth(f) all.equal(s, dlmSmooth(obs, m))