maximum number of iterations in the fitting algorithm. Default is 50.
tolerance
if the change in the scaled parameter estimates is less than this value, the
iteration is judged to have converged. The parameters are scaled by the
algorithm in the internal calculations. Roughly, you can think of this
tolerance as a relative convergence criterion in the parameters that should be
approximately invariant to rescaling.
Default is
sqrt(.Machine$double.eps), where
eps is the machine precision.
maxfcalls
maximum number of times the expression for the function should be evaluated.
Default is 200.
rel.tolerance
if the relative change in the objective function is less than this value,
the iteration is judged to have converged.
Default
max(10^-10, .Machine$double.eps^(2/3)).
f.tolerance
if the absolute value of the objective function is less than this value, the
iteration is judged to have converged. (Only relevant if the computed value at
the minimum could be a hard zero.) Default
10^(-20).
failure
what action should occur if the iteration fails?
The default is a warning.
If
failure is 0, no warning is issued.
If
failure is 2 or more, a fatal error occurs.
scale
vector of scale values, which is multiplied into the parameter vector
for purposes of testing convergence and for other internal calculations. The
algorithm does its own attempt at choosing a scale vector if none is provided.
minscale
the optimization algorithm tries at each iteration to make the function
decrease by stepping out along a suggested direction. If the function does not
decrease, the scale of the step is cut back.
If the scale drops below the
minscale value (relative to
an initial value of 1), the iteration is judged to have failed.
Default is
100 * .Machine$double.eps.
trace
logical value: if
TRUE special trace printing for the iterative
minimization is produced.
flags
the integer and double-precision arrays of internal parameters to the
minimization algorithm.
These are returned in the fitted object (see
ms.object).
The strong of heart can consult the documentation for the underlying
Fortran algorithm, in the PORT library, and change individual values.
Note that these block settings will be overridden by any specific
use of the other arguments above.
opt.parameters
the integer and double-precision arrays of internal parameters to the
minimization algorithm.
These are returned in the fitted object (see
ms.object).
The strong of heart can consult the documentation for the underlying
Fortran algorithm, in the PORT library, and change individual values.
Note that these block settings will be overridden by any specific
use of the other arguments above.
VALUE:
list of the control values, both specified and default.
NOTE:
There is no need to call
ms.control directly. The call to
ms can
specify any of these control values as the
control argument.
See the EXAMPLES section below.
Note also that the control parameter names may
notbe abbreviated.
REFERENCES:
A. T. & T. Bell Laboratories (1984).
PORT Mathematical Subroutine Library Manual.
Chambers, J. M., and Hastie, T. J. (eds) (1990).
Statistical Models in S,
Chapter 10, "Nonlinear Models", Pacific Grove, CA.