stats (version 3.1.3)

profile.nls: Method for Profiling nls Objects

Description

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

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.

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.

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).

See Also

nls, profile, plot.profile.nls

Examples

Run this code

# 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)

Run the code above in your browser using DataCamp Workspace