# 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 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:

##### References

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

##### See Also

##### Examples

`library(stats)`

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

*Documentation reproduced from package stats, version 3.6.0, License: Part of R 3.6.0*