l.SS {dse1} | R Documentation |
Evaluate a state space TSmodel.
l(obj1, obj2, sampleT=NULL, predictT=NULL, error.weights=0, return.state=FALSE, return.track=FALSE, result=NULL, compiled=.DSECOMPILED, warn=TRUE, return.debug.info=FALSE, ...)
obj1 |
An 'SS' 'TSmodel' object. |
obj2 |
A TSdata object. |
sampleT |
an integer indicating the last data point to use for one step ahead filter estimation. If NULL all available data is used. |
predictT |
an integer indicating how far past the end of the sample predictions should be made. For models with an input, input data must be provided up to predictT. Output data is necessary only to sampleT. If NULL predictT is set to sampleT. |
error.weights |
a vector of weights to be applied to the squared prediction errors. |
return.state |
if TRUE the element filter$state
containing E[z(t)|y(t-1), u(t)] is returned as part of the
result. This can be a fairly large matrix. |
return.track |
if TRUE the element filter$track containing
the expectation of the tracking error given y(t-1) and u(t) is
returned as part of the result. This can be an very large array. |
result |
if result is not specified an object of class
TSestModel is returned. Otherwise, the specified element
of TSestModel$estimates is returned. |
compiled |
if TRUE the compiled version of the code is used. Otherwise the S/R version is used. |
warn |
if FALSE then certain warning messages are turned off. |
return.debug.info |
logical indicating if additional debugging information should be returned. |
... |
(further arguments, currently disregarded). |
This function is called by the function l() when the argument to l is a state space model. Using l() is usually preferable to calling l.SS directly. l.SS calls a compiled program unless compiled=FALSE. The compiled version is much faster than the S version.
Output data must be at least as long as sampleT. If sampleT is not supplied it is taken to be periods(data).
Input data must be at least as long as predictT. predictT must be at least as large as sampleT. If predictT is not supplied it is taken to be sampleT.
If error.weights
is greater than zero then weighted prediction
errors are calculated up to the horizon indicated
by the length of error.weights. The weights are applied to the squared
error at each period ahead.
Usually an object of class TSestModel (see TSestModel), but see result above.
SS
l
l.ARMA
TSmodel
TSestModel.object
smoother
if(is.R()) data("eg1.DSE.data.diff", package="dse1") model <- toSS(TSmodel(estVARXls(eg1.DSE.data.diff))) lmodel <- l.SS(model,eg1.DSE.data.diff)