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

\name{selfStart}
\title{Construct Self-starting Nonlinear Models}
\encoding{UTF-8}
\alias{selfStart}
\alias{selfStart.default}
\alias{selfStart.formula}
\description{
  Construct self-starting nonlinear models to be used in
  \code{\link{nls}}, etc.  Via function \code{initial} to compute
  approximate parameter values from data, such models are
  \dQuote{self-starting}, i.e., do not need a \code{start} argument in,
  e.g., \code{\link{nls}()}.
}
\usage{
selfStart(model, initial, parameters, template)
}
\arguments{
  \item{model}{a function object defining a nonlinear model or
    a nonlinear \code{\link{formula}} object of the form \code{~ expression}.}
 \item{initial}{a function object, taking arguments \code{mCall},
   \code{data}, and \code{LHS}, \emph{and} \code{...}, representing,
   respectively, a matched call to the function \code{model}, a data frame in
   which to interpret the variables in \code{mCall}, and the expression
   from the left-hand side of the model formula in the call to \code{nls}.
   This function should return initial values for the parameters in
   \code{model}.  The \code{...} is used by \code{\link{nls}()} to pass its
   \code{control} and \code{trace} arguments for the cases where
   \code{initial()} itself calls \code{nls()} as it does for the ten
   self-starting nonlinear models in \R's \pkg{stats} package.}
  \item{parameters}{a character vector specifying the terms on the right
    hand side of \code{model} for which initial estimates should be
    calculated.  Passed as the \code{namevec} argument to the
    \code{deriv} function.}
  \item{template}{an optional prototype for the calling sequence of the
    returned object, passed as the \code{function.arg} argument to the
    \code{deriv} function.  By default, a template is generated with the
    covariates in \code{model} coming first and the parameters in
    \code{model} coming last in the calling sequence.}
}
\details{
  \code{\link{nls}()} calls \code{\link{getInitial}} and the
  \code{initial} function for these self-starting models.

  This function is generic; methods functions can be written to handle
  specific classes of objects.
}
\value{
  a \code{\link{function}} object of class \code{"selfStart"}, for the
  \code{formula} method obtained by applying \code{\link{deriv}}
  to the right hand side of the \code{model} formula.  An
  \code{initial} attribute (defined by the \code{initial} argument) is
  added to the function to calculate starting estimates for the
  parameters in the model automatically.
}
\author{\enc{José}{Jose} Pinheiro and Douglas Bates}

\seealso{
  \code{\link{nls}}, \code{\link{getInitial}}.

  Each of the following are \code{"selfStart"} models (with examples)
  \code{\link{SSasymp}}, \code{\link{SSasympOff}}, \code{\link{SSasympOrig}},
  \code{\link{SSbiexp}}, \code{\link{SSfol}}, \code{\link{SSfpl}},
  \code{\link{SSgompertz}}, \code{\link{SSlogis}}, \code{\link{SSmicmen}},
  \code{\link{SSweibull}}.

  Further, package \CRANpkg{nlme}'s \code{\link[nlme]{nlsList}}.
}
\examples{
## self-starting logistic model

## The "initializer" (finds initial values for parameters from data):
initLogis <- function(mCall, data, LHS, ...) {
    xy <- sortedXyData(mCall[["x"]], LHS, data)
    if(nrow(xy) < 4)
        stop("too few distinct input values to fit a logistic model")
    z <- xy[["y"]]
    ## transform to proportion, i.e. in (0,1) :
    rng <- range(z); dz <- diff(rng)
    z <- (z - rng[1L] + 0.05 * dz)/(1.1 * dz)
    xy[["z"]] <- log(z/(1 - z))		# logit transformation
    aux <- coef(lm(x ~ z, xy))
    pars <- coef(nls(y ~ 1/(1 + exp((xmid - x)/scal)),
                     data = xy,
                     start = list(xmid = aux[[1L]], scal = aux[[2L]]),
                     algorithm = "plinear", ...))
    setNames(pars [c(".lin", "xmid", "scal")],
             mCall[c("Asym", "xmid", "scal")])
}

mySSlogis <- selfStart(~ Asym/(1 + exp((xmid - x)/scal)),
                       initial = initLogis,
                       parameters = c("Asym", "xmid", "scal"))
\dontdiff{
getInitial(weight ~ mySSlogis(Time, Asym, xmid, scal),
           data = subset(ChickWeight, Chick == 1))
}

# 'first.order.log.model' is a function object defining a first order
# compartment model
# 'first.order.log.initial' is a function object which calculates initial
# values for the parameters in 'first.order.log.model'
#
# self-starting first order compartment model
\dontrun{
SSfol <- selfStart(first.order.log.model, first.order.log.initial)
}

## Explore the self-starting models already available in R's  "stats":
pos.st <- which("package:stats" == search())
mSS <- apropos("^SS..", where = TRUE, ignore.case = FALSE)
(mSS <- unname(mSS[names(mSS) == pos.st]))
fSS <- sapply(mSS, get, pos = pos.st, mode = "function")
all(sapply(fSS, inherits, "selfStart"))  # -> TRUE

## Show the argument list of each self-starting function:
str(fSS, give.attr = FALSE)
}
\keyword{models}
