dlm: dlm objects

In dlm: Bayesian and Likelihood Analysis of Dynamic Linear Models

dlm objects

Description

The function dlm is used to create Dynamic Linear Model objects. as.dlm and is.dlm coerce an object to a Dynamic Linear Model object and test whether an object is a Dynamic Linear Model.

Usage

dlm(...)
as.dlm(obj)
is.dlm(obj)

Arguments

...

list with named elements m0, C0, FF, V, GG, W and, optionally, JFF, JV, JGG, JW, and X. The first six are the usual vector and matrices that define a time-invariant DLM. The remaining elements are used for time-varying DLM. X, if present, should be a matrix. If JFF is not NULL, then it must be a matrix of the same dimension of FF, with the (i,j) element being zero if FF[i,j] is time-invariant, and a positive integer k otherwise. In this case the (i,j) element of FF at time t will be X[t,k]. A similar interpretation holds for JV, JGG, and JW. ... may have additional components, that are not used by dlm. The named components may also be passed to the function as individual arguments.

obj

an arbitrary R object.

Details

The function dlm is used to create Dynamic Linear Model objects. These are lists with the named elements described above and with class of "dlm".

Class "dlm" has a number of methods. In particular, consistent DLM can be added together to produce another DLM.

Value

For dlm, an object of class "dlm".

Author(s)

Giovanni Petris [e-mail]

References

Giovanni Petris (2010), An R Package for Dynamic Linear Models. Journal of Statistical Software, 36(12), 1-16. https://www.jstatsoft.org/v36/i12/.
Petris, Petrone, and Campagnoli, Dynamic Linear Models with R, Springer (2009).
West and Harrison, Bayesian forecasting and dynamic models (2nd ed.), Springer (1997).

See Also

dlmModReg, dlmModPoly, dlmModARMA, dlmModSeas, to create particular objects of class "dlm".

Examples

## Linear regression as a DLM
x <- matrix(rnorm(10),nc=2)
mod <- dlmModReg(x)
is.dlm(mod)

## Adding dlm's
dlmModPoly() + dlmModSeas(4) # linear trend plus quarterly seasonal component

dlm documentation built on Nov. 28, 2022, 5:11 p.m.