nls.control - R Documentation

`nls.control` Control the Iterations in nls

Description

Allow the user to set some characteristics of the nls nonlinear least squares algorithm.

Usage

nls.control(maxiter = 50, tol = 1e-05, minFactor = 1/1024,
            printEval = FALSE, warnOnly = FALSE, scaleOffset = 0,
            nDcentral = FALSE)

Arguments

`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 `nls()` should return instead of signalling an error in the case of termination before convergence. Termination before convergence happens upon completion of `maxiter` iterations, in the case of a singular gradient, and in the case that the step-size factor is reduced below `minFactor`.
`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 `0` keeps the legacy behaviour of `nls()`. A value such as `1` seems to work for problems of reasonable scale with very small residuals.
`nDcentral`	only when numerical derivatives are used: `logical` indicating if central differences should be employed, i.e., `numericDeriv(*, central=TRUE)` be used.

Value

A list with components

`maxiter`
`tol`
`minFactor`
`printEval`
`warnOnly`
`scaleOffset`
`nDcentreal`

with meanings as explained under ‘Arguments’.

Author(s)

Douglas Bates and Saikat DebRoy; John C. Nash for part of the scaleOffset option.

References

Bates, D. M. and Watts, D. G. (1988), Nonlinear Regression Analysis and Its Applications, Wiley.

Examples

nls.control(minFactor = 1/2048)

nls.control Control the Iterations in nls