

   SSasymp {nls}                                R Documentation

   AAssyymmppttoottiicc RReeggrreessssiioonn MMooddeell

   DDeessccrriippttiioonn::

        This `selfStart' model evaluates the asymptotic regres-
        sion function and its gradient.  It has an `initial'
        attribute that will evaluate initial estimates of the
        parameters `Asym', `R0', and `lrc' for a given set of
        data.

   UUssaaggee::

        SSasymp(input, Asym, R0, lrc)

   AArrgguummeennttss::

      input: a numeric vector of values at which to evaluate
             the model.

       Asym: a numeric parameter representing the horizontal
             asymptote on the right side (very large values of
             `input').

         R0: a numeric parameter representing the response when
             `input' is zero.

        lrc: a numeric parameter representing the natural loga-
             rithm of the rate constant.

   VVaalluuee::

        a numeric vector of the same length as `input'.  It is
        the value of the expression
        `Asym+(R0-Asym)*exp(-exp(lrc)*input)'.  If all of the
        arguments `Asym', `R0', and `lrc' are names of objects,
        the gradient matrix with respect to these names is
        attached as an attribute named `gradient'.

   AAuutthhoorr((ss))::

        Jose Pinheiro and Douglas Bates

   SSeeee AAllssoo::

        `nls', `selfStart'

   EExxaammpplleess::

        library( nls )
        data( Loblolly )
        Lob.329 <- Loblolly[ Loblolly$Seed == "329", ]
        SSasymp( Lob.329$age, 100, -8.5, -3.2 )  # response only
        Asym <- 100
        resp0 <- -8.5
        lrc <- -3.2
        SSasymp( Lob.329$age, Asym, resp0, lrc ) # response and gradient
        getInitial(height ~ SSasymp( age, Asym, resp0, lrc), data = Lob.329)
        ## Initial values are in fact the converged values
        fm1 <- nls(height ~ SSasymp( age, Asym, resp0, lrc), data = Lob.329)
        summary(fm1)

