nls.control {stats}  R Documentation 
Allow the user to set some characteristics of the nls
nonlinear least squares algorithm.
nls.control(maxiter = 50, tol = 1e05, 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 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 
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.control(minFactor = 1/2048)