dlmodeler.build: Build a DLM
In dlmodeler: Generalized Dynamic Linear Modeler

Description Usage Arguments Details Value Author(s) See Also Examples

Builds a DLM with the supplied design matrices, or an "empty" DLM of the specified dimensions.

dlmodeler.build(a0 = NULL, P0 = NULL, P0inf = NULL,
                Tt = NULL, Rt = NULL, Qt = NULL,
                Zt = NULL, Ht = NULL,
                dimensions = NULL,
                name = 'noname', components = list())

`a0`	initial state vector.
`P0`	initial state covariance matrix.
`P0inf`	diffuse part of `P0`, matrix of zeros and ones.
`Tt`	state transition matrix.
`Rt`	state disturbance selection matrix.
`Qt`	state disturbance covariance matrix.
`Zt`	observation design matrix.
`Ht`	observation disturbance covariance matrix.
`dimensions`	vector of dimensions (m,r,d).
`name`	an optional name to be given to the resulting DLM.
`components`	optional list of components.

A DLM can be constructed either by specifying all the elements a0, P0, P0inf,Tt, Rt, Qt, Zt and Ht or by simply giving the dimensions m, r and d (in which case the DLM is created with zero-filled elements of the appropriate dimension).

See dlmodeler for information about the state-space representation adopted in this package.

This function is called by the helper functions referenced below.

An object of class dlmodeler representing the model.

Cyrille Szymanski <cnszym@gmail.com>

dlmodeler, dlmodeler.check, dlmodeler.build.polynomial, dlmodeler.build.dseasonal, dlmodeler.build.tseasonal, dlmodeler.build.structural, dlmodeler.build.arima, dlmodeler.build.regression

## Not run: 
require(dlmodeler)

# a stochastic level+trend DLM
mod <- dlmodeler.build(
		a0 = c(0,0), # initial state: (level, trend)
		P0 = diag(c(0,0)), # initial state variance set to...
		P0inf = diag(2), # ...use exact diffuse initialization
		matrix(c(1,0,1,1),2,2), # state transition matrix
		diag(c(1,1)), # state disturbance selection matrix
		diag(c(.5,.05)), # state disturbance variance matrix
		matrix(c(1,0),1,2), # observation design matrix
		matrix(1,1,1) # observation disturbance variance matrix
)
# print the model
mod
# check if it is valid
dlmodeler.check(mod)$status

# an empty DLM with 4 state variables (3 of which are stocastic)
# and bi-variate observations
mod <- dlmodeler.build(dimensions=c(4,3,2))
# print the model
mod
# check if it is valid
dlmodeler.check(mod)$status

## End(Not run)