# profile.nls: Method for Profiling nls Objects

## Description

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

.

## 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.

## Value

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

par.valsa matrix of parameter values for each fitted model.

tauthe profile t-statistics.

## 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.

## References

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

## Examples

# 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
## IGNORE_RDIFF_BEGIN
pr1$A
pr1$lrc
## IGNORE_RDIFF_END
# see also example(plot.profile.nls)
# }