\Header{nls}{Nonlinear Least Squares}
\alias{AIC.nls}{nls}
\alias{anova.nls}{nls}
\alias{anovalist.nls}{nls}
\alias{coef.nls}{nls}
\alias{deviance.nls}{nls}
\alias{df.residual.nls}{nls}
\alias{fitted.nls}{nls}
\alias{logLik.nls}{nls}
\alias{print.nls}{nls}
\alias{print.summary.nls}{nls}
\alias{residuals.nls}{nls}
\alias{summary.nls}{nls}
\alias{weights.nls}{nls}
\keyword{nonlinear}{nls}
\keyword{regression}{nls}
\keyword{models}{nls}
\begin{Description}\relax
Determine the nonlinear least squares estimates of the nonlinear model
parameters and return a class \code{nls} object.\end{Description}
\begin{Usage}
\begin{verbatim}
nls(formula, data = parent.frame(), start, control = nls.control(),
    algorithm = "default", trace = FALSE, subset,
    weights, na.action)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{formula}] a nonlinear model formula including variables and parameters
\item[\code{data}] an optional data frame in which to evaluate the variables in
\code{formula}
\item[\code{start}] a named list or named numeric vector of starting estimates
\item[\code{control}] an optional list of control settings.  See
\code{nls.control} for the names of the settable control values and
their effect.
\item[\code{algorithm}] character string specifying the algorithm to use.
The default algorithm is a Gauss-Newton algorithm. The other 
alternative is "plinear", the Golub-Pereyra algorithm for
partially linear least-squares models.
\item[\code{trace}] logical value indicating if a trace of the iteration
progress should be printed.  Default is \code{FALSE}.  If
\code{TRUE} the residual sum-of-squares and the parameter values
are printed at the conclusion of each iteration.  When the
\code{"plinear"} algorithm is used, the conditional estimates of
the linear parameters are printed after the nonlinear parameters.
\item[\code{subset}] an optional vector specifying a subset of observations
to be used in the fitting process.
\item[\code{weights}] an optional numeric vector of (fixed) weights.  When
present, the objective function is weighted least squares. \emph{not
yet implemented}
\item[\code{na.action}] a function which indicates what should happen
when the data contain \code{NA}s.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\bold{Do not use \code{nls} on artificial "zero-residual" data.}

The \code{nls} function uses a relative-offset convergence criterion
that compares the numerical imprecision at the current parameter
estimates to the residual sum-of-squares.  This performs well on data of
the form \deqn{y=f(x,\theta)+\epsilon}{y = f(x, theta) + eps} (with
\code{var(eps) > 0}).  It
fails to indicate convergence on data of the form
\deqn{y=f(x,\theta)}{y = f(x, theta)} because the criterion amounts to
comparing two components of the round-off error.  If you wish to test
\code{nls} on artificial data please add a noise component, as shown
in the example below.

An \code{nls} object is a type of fitted model object.  It has methods
for the generic functions \code{coef}, \code{formula}, \code{resid},
\code{print}, \code{summary}, \code{AIC}, and \code{fitted}.\end{Details}
\begin{Value}
A list of
\begin{ldescription}
\item[\code{m}] an \code{nlsModel} object incorporating the model
\item[\code{data}] the expression that was passed to \code{nls} as the data
argument.  The actual data values are present in the environment of
the \code{m} component.
\end{ldescription}
\end{Value}
\begin{Author}\relax
Douglas M. Bates and Saikat DebRoy\end{Author}
\begin{References}\relax
Bates, D.M. and Watts, D.G. (1988)
\emph{Nonlinear Regression Analysis and Its Applications},
Wiley\end{References}
\begin{SeeAlso}\relax
\code{\Link{nlsModel}}\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
data( DNase )
DNase1 <- DNase[ DNase$Run == 1, ]
## using a selfStart model
fm1DNase1 <- nls( density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1 )
summary( fm1DNase1 )
## using conditional linearity
fm2DNase1 <- nls( density ~ 1/(1 + exp(( xmid - log(conc) )/scal ) ),
                  data = DNase1,
                  start = list( xmid = 0, scal = 1 ),
                  alg = "plinear", trace = TRUE )
summary( fm2DNase1 )
## without conditional linearity
fm3DNase1 <- nls( density ~ Asym/(1 + exp(( xmid - log(conc) )/scal ) ),
                  data = DNase1,
                  start = list( Asym = 3, xmid = 0, scal = 1 ),
                  trace = TRUE )
summary( fm3DNase1 )
## weighted nonlinear regression
data(Puromycin)
Treated <- Puromycin[Puromycin$state == "treated", ]
weighted.MM <- function(resp, conc, Vm, K)
{
    ## Purpose: exactly as white book p.451 -- RHS for nls()
    ##  Weighted version of Michaelis-Menten model
    ## -------------------------------------------------------------------------
    ## Arguments: `y', `x' and the two parameters (see book)
    ## -------------------------------------------------------------------------
    ## Author: Martin Maechler, Date: 23 Mar 2001, 18:48

    pred <- (Vm * conc)/(K + conc)
    (resp - pred) / sqrt(pred)
}

Pur.wt <- nls( ~ weighted.MM(rate, conc, Vm, K), data = Treated,
              start = list(Vm = 200, K = 0.1),
              trace = TRUE)
\end{ExampleCode}
\end{Examples}

