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 = 1e05, 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 stepsize factor allowed on any step in the iteration. The increment is calculated with a GaussNewton algorithm and successively halved until the residual sum of squares has been decreased or until the stepsize 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 ofmaxiter
iterations, in the case of a singular gradient, and in the case that the stepsize 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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