nls.control | R Documentation |
Allow the user to set some characteristics of the nls
nonlinear least squares algorithm.
nls.control(maxiter = 50, tol = 1e-05, minFactor = 1/1024, printEval = FALSE, warnOnly = FALSE, scaleOffset = 0, nDcentral = FALSE)
maxiter |
A positive integer specifying the maximum number of iterations allowed. |
tol |
A positive numeric value specifying the tolerance level for the relative offset convergence criterion. |
minFactor |
A positive numeric value specifying the minimum step-size factor allowed on any step in the iteration. The increment is calculated with a Gauss-Newton algorithm and successively halved until the residual sum of squares has been decreased or until the step-size factor has been reduced below this limit. |
printEval |
a logical specifying whether the number of evaluations (steps in the gradient direction taken each iteration) is printed. |
warnOnly |
a logical specifying whether |
scaleOffset |
a constant to be added to the denominator of the relative
offset convergence criterion calculation to avoid a zero divide in the case
where the fit of a model to data is very close. The default value of
|
nDcentral |
only when numerical derivatives are used:
|
A list
with components
maxiter |
|
tol |
|
minFactor |
|
printEval |
|
warnOnly |
|
scaleOffset |
|
nDcentreal |
with meanings as explained under ‘Arguments’.
Douglas Bates and Saikat DebRoy; John C. Nash for part of the
scaleOffset
option.
Bates, D. M. and Watts, D. G. (1988), Nonlinear Regression Analysis and Its Applications, Wiley.
nls
nls.control(minFactor = 1/2048)
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