# 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)

## 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`

.

## Value

A `list`

with exactly five components:

maxiter
tol
minFactor
printEval
warnOnly
with meanings as explained under Arguments.
## References

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

## Examples

# NOT RUN {
nls.control(minFactor = 1/2048)
# }