estMaxLik {dse1} | R Documentation |
Maximum likelihood estimation.
estMaxLik(obj1, obj2=NULL, ...) ## S3 method for class 'TSmodel': estMaxLik(obj1, obj2, algorithm="optim", algorithm.args=list(method="BFGS", upper=Inf, lower=-Inf, hessian=TRUE), ...) ## S3 method for class 'TSestModel': estMaxLik(obj1, obj2=TSdata(obj1), ...) ## S3 method for class 'TSdata': estMaxLik(obj1, obj2, ...)
obj1 |
an object of class TSmodel, TSdata or TSestModel |
obj2 |
TSdata or a TSmodel to be fitted with obj1. |
algorithm |
the algorithm ('optim', 'nlm' or 'nlmin') to use for maximization. |
algorithm.args |
arguments for the optimization algorithm. |
... |
arguments passed on to other methods. |
One of obj1
or obj2
should specify a TSmodel
and
the other TSdata
. If obj1
is a TSestModel
and
obj2
is NULL, then the data is extracted from obj1
.
The TSmodel
object is used to specify both the initial parameter
values and the model structure (the placement of the parameters
in the various arrays of the TSmodel). Estimation attempts to minimimize the
negative log likelihood (as returned by l
) of the given model
structure by adjusting the
parameter values. A TSmodel
can also have constant values in
some array elements, and these are not changed.
The value returned is an object of class TSestModel with additional
elements est$converged
, which is TRUE or FALSE indicating convergence,
est$converceCode
, which is the code returned by the estimation algorithm,
and est$results
, which are detailed results returned by the estimation
algorithm. The hessian and gradient in results could potentially
be used for restarting in the case of non-convergence, but that has not
yet been implemented.
optim
nlm
estVARXls
bft
TSmodel
l
true.model <- ARMA(A=c(1, 0.5), B=1) est.model <- estMaxLik(true.model, simulate(true.model)) summary(est.model) est.model tfplot(est.model)