dlmLL

Function that computes the log likelihood of a state space model.

misc

Provides routines for Maximum likelihood,
Kalman filtering and smoothing, and Bayesian
analysis of Normal linear State Space models, also known as
Dynamic Linear Models.

pcp by Giovanni Petris

Bayesian and Likelihood Analysis of Dynamic Linear Models

Giovanni Petris

Wally Gilks

dlmLL function

<dl> <dt>y</dt>
<dd>a vector, matrix, or time series of data.</dd> <dt>mod</dt>
<dd>an object of class <code>"dlm"</code>, or a list with components
 <code>m0</code>, <code>C0</code>, 
 <code>FF</code>, <code>V</code>, <code>GG</code>, <code>W</code> defining the model
 and the parameters of the prior distribution.</dd>
 
 <dt>debug</dt>
<dd>if <code>debug=TRUE</code>, the function uses R code, otherwise
 it uses faster C code.</dd></dl>

Arguments

The observation variance <code>V</code> in <code>mod</code> must be nonsingular.

Warning

Giovanni Petris <a href="/link/GPetris%40uark.edu?package=dlm&version=1.1-6.1" data-mini-rdoc="dlm::GPetris@uark.edu">GPetris@uark.edu</a>

Author

Log likelihood evaluation for a state space model — dlmLL

<dl>

 <dt>y</dt>
<dd>a vector, matrix, or time series of data.</dd>

 <dt>mod</dt>
<dd>an object of class <code>"dlm"</code>, or a list with components
 <code>m0</code>, <code>C0</code>, 
 <code>FF</code>, <code>V</code>, <code>GG</code>, <code>W</code> defining the model
 and the parameters of the prior distribution.</dd>
 
 <dt>debug</dt>
<dd>if <code>debug=TRUE</code>, the function uses R code, otherwise
 it uses faster C code.</dd>

</dl>

Giovanni Petris <a href='mailto:GPetris@uark.edu'>GPetris@uark.edu</a>

dlmLL: Log likelihood evaluation for a state space model

Description

Usage

Value

Arguments

Warning

Author

Details

References

See Also

Examples