p-vector of initial values for the parameters (
NAs not allowed).
objective
an S-PLUS function that returns the value of minus the
spatial regression profile likelihood function to be minimized. This
function must be of the form f(x,), where x is
the vector of parameters over which the minimization takes place.
Users can accumulate information through attributes of the value of
objective. If the attributes include any additional arguments of
objective, the next call to objective will use the new values of
those arguments.
OPTIONAL ARGUMENTS:
scale
either a single positive value or a positive numeric vector (with length
equal to the number of parameters) to be used to
scale the parameter vector. Although scale can have a great effect on
the performance of the algorithm, it is not known how to choose it
optimally. The default is unscaled :
scale = 1.
control
a list of parameters by which the user can control various aspects of
the minimization. For details, see the help file for
nlminb.control.
lower,upper
either a single numeric value or a vector (with length equal to the
number of parameters) giving lower or upper bounds for the parameter
values. The absence of a bound may be indicated by either
NA or
NULL, or by -
Inf and
Inf. The default is unconstrained minimization:
lower = -Inf, upper = Inf.
VALUE:
a list with the following values:
parameters
final values of the parameters over which the optimization takes
place.
objective
the final value of the objective.
message
a statement of the reason for termination.
grad.norm
the final norm of the objective gradient. If there are active bounds,
then components corresponding to active bounds are excluded from
the norm calculation. If the number of active bounds is equal to the
number of parameters,
NA will be returned.
iterations
the total number of iterations before termination.
f.evals
the total number of residual evaluations before termination.
g.evals
the total number of jacobian evaluations before termination.
scale
the final value of the scale vector.
aux
the final value of the function attributes.
DETAILS:
nlminb is based on the Fortran functions dmnfb, dmngb, and
dmnhb. See the
nlminb help information for additional details.
Unlike
nlminb,
slm.nlminb only allows for the specification of
the criterion function: finite difference gradients and Hessians
are always used. Because a profile likelihood for the spatial regression
models is used, there should be very few covariance parameters, and
the use of finite difference derivatives will usually not be a
problem. In general, the user will not need to call routine
slm.nlminb
directly.