nls.control {stats}

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.

See Also

nls

Examples

nls.control(minFactor = 1/2048)