Random DLM
Generate a random (constant or time-varying) object of class
"dlm", along with states and observations from it.
dlmRandom(m, p, nobs = 0, JFF, JV, JGG, JW)
m |
dimension of the observation vector. |
p |
dimension of the state vector. |
nobs |
number of states and observations to simulate from the model. |
JFF |
should the model have a time-varying |
JV |
should the model have a time-varying |
JGG |
should the model have a time-varying |
JW |
should the model have a time-varying |
The function generates randomly the system and observation matrices and
the variances of a DLM having the specified state and observation
dimension. The system matrix GG is guaranteed to have
eigenvalues strictly less than one, which implies that a constant DLM is
asymptotically stationary. The default behavior is to generate a
constant DLM. If JFF is TRUE then a model for
nobs observations in which all
the elements of FF are time-varying is generated. Similarly
with JV, JGG, and JW.
The function returns a list with the following components.
mod |
An object of class |
theta |
Matrix of simulated state vectors from the model. |
y |
Matrix of simulated observations from the model. |
If nobs is zero, only the mod component is returned.
Giovanni Petris GPetris@uark.edu
Anderson and Moore, Optimal filtering, Prentice-Hall (1979)
dlmRandom(1, 3, 5)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.