# profile.nls

##### Method for Profiling nls Objects

Investigates the profile log-likelihood function for a fitted model of
class `"nls"`

.

- Keywords
- models, regression, nonlinear

##### Usage

```
# S3 method for nls
profile(fitted, which = 1:npar, maxpts = 100, alphamax = 0.01,
delta.t = cutoff/5, …)
```

##### Arguments

- fitted
the original fitted model object.

- which
the original model parameters which should be profiled. This can be a numeric or character vector. By default, all non-linear parameters are profiled.

- maxpts
maximum number of points to be used for profiling each parameter.

- alphamax
highest significance level allowed for the profile t-statistics.

- delta.t
suggested change on the scale of the profile t-statistics. Default value chosen to allow profiling at about 10 parameter values.

- …
further arguments passed to or from other methods.

##### Details

The profile t-statistics is defined as the square root of change in sum-of-squares divided by residual standard error with an appropriate sign.

##### Value

A list with an element for each parameter being profiled. The elements are data-frames with two variables

a matrix of parameter values for each fitted model.

the profile t-statistics.

##### References

Bates, D. M. and Watts, D. G. (1988), *Nonlinear Regression Analysis
and Its Applications*, Wiley (chapter 6).

##### See Also

##### Examples

`library(stats)`

```
# NOT RUN {
# obtain the fitted object
fm1 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
# get the profile for the fitted model: default level is too extreme
pr1 <- profile(fm1, alpha = 0.05)
# profiled values for the two parameters
pr1$A
pr1$lrc
# see also example(plot.profile.nls)
# }
```

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