src/library/stats/man/nls.control.Rd

% File src/library/stats/man/nls.control.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2020 R Core Team
% Distributed under GPL 2 or later

\name{nls.control}
\alias{nls.control}
\title{Control the Iterations in nls}
\description{
  Allow the user to set some characteristics of the \code{\link{nls}}
  nonlinear least squares algorithm.
}
\usage{
nls.control(maxiter = 50, tol = 1e-05, minFactor = 1/1024,
            printEval = FALSE, warnOnly = FALSE, scaleOffset = 0,
            nDcentral = FALSE)
}
\arguments{
  \item{maxiter}{A positive integer specifying the maximum number of
    iterations allowed.}
  \item{tol}{A positive numeric value specifying the tolerance level for
    the relative offset convergence criterion.}
  \item{minFactor}{A positive numeric value specifying the minimum
    step-size factor allowed on any step in the iteration.  The
    increment is calculated with a Gauss-Newton algorithm and
    successively halved until the residual sum of squares has been
    decreased or until the step-size factor has been reduced below this
    limit.}
  \item{printEval}{a logical specifying whether the number of evaluations
    (steps in the gradient direction taken each iteration) is printed.}
  \item{warnOnly}{a logical specifying whether \code{\link{nls}()} should
    return instead of signalling an error in the case of termination
    before convergence.
    Termination before convergence happens upon completion of \code{maxiter}
    iterations, in the case of a singular gradient, and in the case that the
    step-size factor is reduced below \code{minFactor}.}
  \item{scaleOffset}{a constant to be added to the denominator of the relative
    offset convergence criterion calculation to avoid a zero divide in the case
    where the fit of a model to data is very close.  The default value of
    \code{0} keeps the legacy behaviour of \code{nls()}.  A value such as
    \code{1} seems to work for problems of reasonable scale with very small
    residuals.}
  \item{nDcentral}{only when \emph{numerical} derivatives are used:
    \code{\link{logical}} indicating if \emph{central} differences
    should be employed, i.e., \code{\link{numericDeriv}(*, central=TRUE)}
    be used.}
}
\value{
  A \code{\link{list}} with components
  \item{maxiter}{}
  \item{tol}{}
  \item{minFactor}{}
  \item{printEval}{}
  \item{warnOnly}{}
  \item{scaleOffset}{}
  \item{nDcentreal}{}
  with meanings as explained under \sQuote{Arguments}.
}
\references{
  Bates, D. M. and Watts, D. G. (1988),
  \emph{Nonlinear Regression Analysis and Its Applications}, Wiley.
}
\author{Douglas Bates and Saikat DebRoy; John C. Nash for part of the
  \code{scaleOffset} option.}
\seealso{
  \code{\link{nls}}
}
\examples{
nls.control(minFactor = 1/2048)
}
\keyword{nonlinear}
\keyword{regression}
\keyword{models}