nls.control
Control the Iteration in nls()
Description
Provides a convenient way to set the control argument of nls().
Usage
nls.control(maxiter = 50, tolerance = 1e-05, minFactor = 1/1024, printEval = FALSE, warnOnly = FALSE)
Arguments
| maxiter |
the maximum number of iterations during fitting. The default is 50.
|
| tolerance |
the tolerance for the relative offset convergence criterion in the algorithm.
The default is 1e-05.
Note that the convergence test used in nls() is strictly relative:
it wants the residual vector to be close to orthogonal to the tangent space of the model.
Therefore if the solution to a problem turns out to be a perfect
fit (unlikely except in artificial examples), convergence is not
guaranteed to be recognized by the algorithm.
|
| minFactor |
a small negative power of two. If the Gauss-Newton step results in an
increase in the sum of squared residuals, the step size is repeated
halved until the sum of squares decreases, or until the relative step
size is smaller than minFactor. The default is 1/1024.
|
| printEval |
a logical flag. If TRUE, prints information about the iterations and partial steps.
The default is FALSE.
|
| warnOnly |
a logical flag. If TRUE, if nls() terminates without reaching convergence,
it returns the current estimates with a warning, instead of signalling an error. The
default is FALSE.
|
Details
Use the control argument to the function nls() to set
several values to control the optimization algorithm. This function
may be used to set them instead of using the list function. The advantge
is that nls.control ensures that you spell (or abbreviate) the variable
names correctly.
Value
returns a list containing components for each of the possible arguments,
either the value supplied by the user or the default.
References
Chambers, J. M., and Hastie, T. J. (eds) (1990).
Statistical Models in S,
Chapter 10, "Nonlinear Models", Pacific Grove, CA.
See Also
Examples
# Make some fake data
d <- data.frame(X=1:20, Y=round(log2(1:20),1)+1)
# Fit a poor model to the data
nls(Y ~ cbind(1, X^p), start=list(p=0.5), data=d, algorithm="plinear",
trace=TRUE, control=nls.control(printEval=TRUE, minFactor=2^-24, warnOnly=TRUE))