nls.control
Control the Iterations in nls
Allow the user to set some characteristics of the nls
nonlinear least squares algorithm.
- Keywords
- models, regression, nonlinear
Usage
nls.control(maxiter = 50, tol = 1e-05, minFactor = 1/1024, printEval = FALSE, warnOnly = 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 ofmaxiteriterations, in the case of a singular gradient, and in the case that the step-size factor is reduced belowminFactor.
Value
-
A
- maxiter
- tol
- minFactor
- printEval
- warnOnly with meanings as explained under ‘Arguments’.
list with exactly five components:
References
Bates, D. M. and Watts, D. G. (1988), Nonlinear Regression Analysis and Its Applications, Wiley.
See Also
Examples
library(stats)
nls.control(minFactor = 1/2048)
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