Internal function: Generic method for estimation of LNRE model parameters. Based on the class of its first argument, the method dispatches to a suitable implementation of the estimation procedure.
Unless you are a developer working on the zipfR source code,
you are probably looking for the lnre manpage.
estimate.model(model, spc, param.names,
method, cost.function, m.max=15,
runs=3, debug=FALSE, ...)LNRE model object of the appropriate class (a subclass of
lnre). All parameters of the LNRE model that are not listed
in param.names must have been initialized to their
prespecified values in the model object.
an observed frequency spectrum, i.e. an object of class
spc. The values of the missing parameters will be estimated
from this frequency spectrum.
a character vector giving the names of parameters for which values have to be estimated ("missing" parameters)
name of the minimization algorithm used for parameter
estimation (see lnre for details)
cost function to be minimized (see
lnre for details). NB: this is a direct
reference to the function object rather than just the name of the
cost function. Look-up of the appropriate cost function
implementation is performed in the lnre constructor.
number of spectrum elements that will be used to compute
the cost function (passed on to cost.function)
number of parameter optimization runs with random initialization. Parameters from the run that achieves the smallest value of the cost function will be selected. Some method implementations may not support multiple optimization runs.
if TRUE, some debugging and progress information
will be printed during the estimation procedure
additional arguments are passed on and may be used by some implementations
A modified version of model, where the missing parameters
listed in param.names have been estimated from the observed
frequency spectrum spc. In addition, goodness-of-fit
information is added to the object.
By default, estimate.model dispatches to a generic
implementation of the estimation procedure that can be used with all
types of LNRE models (estimate.model.lnre).
This generic implementation can be overridden for specific LNRE
models, e.g. to calculate better init values or improve the estimation
procedure in some other way. To provide a custom implementation for
Zipf-Mandelbrot models (of class lnre.zm), for instance, it is
sufficient to define the corresponding method implementation
estimate.model.lnre.zm. If no custom implementation is
provided but the user has selected the Custom method (which is
the default), estimate.model falls back on Nelder-Mead
for multi-dimensional minimization and NLM for one-dimensional
minimization (where Nelder-Mead is considered to be unreliable).
Parmeter estimation is performed by minimization of the cost function
passed in the cost.function argument (see lnre
for details). Depending on the method argument, a range of
different minimization algorithms can be used (see lnre
for a complete listing). The minimization algorithm always operates
on transformed parameter values, making use of the
transform utility provided by LNRE models (see
lnre.details for more information about utility
functions). All parameters are initialized to 0 in the transformed
scale, which should translate to sensible starting points.
Note that the estimate.model implementations do not
perform any error checking. It is the responsibility of the caller
to make sure that the arguments are sensible and complete. In
particular, all model parameters that will not be estimated (i.e. are
not listed in param.names) must have been initialized to
their prespecified values in the model passed to the function.
The user-level function for estimating LNRE models is
lnre. Its manpage also lists available cost functions
and minimization algorithms.
The internal structure of lnre objects (representing LNRE
models) is described on the lnre.details manpage, which
also outlines the necessary steps for implementing a new LNRE model.
The minimization algorithms used are described in detail on the
nlm and optim manpages from R's standard
library.