Fit a Profile Likelihood in a Spatial Regression Model

DESCRIPTION:

Optimization algorithm used to estimate spatial regression covariance parameters.

USAGE:

slm.nlminb(start, objective, scale=1, control=NULL, lower= -Inf, 
           upper=Inf, ...)  

REQUIRED ARGUMENTS:

start
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.

SEE ALSO:

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