# profile.nls

0th

Percentile

##### Method for Profiling nls Objects

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

Keywords
models, regression, nonlinear
##### Usage
"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
par.vals
a matrix of parameter values for each fitted model.
tau
the profile t-statistics.

##### References

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

nls, profile, plot.profile.nls
library(stats)  # 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)