

   BBiieexxppoonneennttiiaall mmooddeell

        SSbiexp(input, A1, lrc1, A2, lrc2)

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

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

         A1: a numeric parameter representing the multiplier of
             the first exponential.

       lrc1: a numeric parameter representing the natural loga-
             rithm of the rate constant of the first exponen-
             tial.

         A2: a numeric parameter representing the multiplier of
             the second exponential.

       lrc2: a numeric parameter representing the natural loga-
             rithm of the rate constant of the second exponen-
             tial.

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

        This `selfStart' model evaluates the biexponential
        model function and its gradient.  It has an `initial'
        attribute that will evaluate initial estimates of the
        parameters `A1', `lrc1', `A2', and `lrc2' for a given
        set of data.

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

        a numeric vector of the same length as `input'.  It is
        the value of the expression
        `A1*exp(-exp(lrc1)*input)+A2*exp(-exp(lrc2)*input)'.
        If all of the arguments `A1', `lrc1', `A2', and `lrc2'
        are names of objects, as opposed to expressions or
        explicit numerical values, 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( lme )
        data( Indometh )
        Indo.1 <- Indometh[Indometh$Subject == 1, ]
        SSbiexp( Indo.1$time, 3, 1, 0.6, -1.3 )  # response only
        A1 <- 3
        lrc1 <- 1
        A2 <- 0.6
        lrc2 <- -1.3
        SSbiexp( Indo.1$time, A1, lrc1, A2, lrc2 ) # response and gradient

