

SSasymp {nls}                                R Documentation

_A_s_y_m_p_t_o_t_i_c _R_e_g_r_e_s_s_i_o_n _M_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_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_s_a_g_e_:

     SSasymp(input, Asym, R0, lrc)

_A_r_g_u_m_e_n_t_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_a_l_u_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_u_t_h_o_r_(_s_)_:

     Jose Pinheiro and Douglas Bates

_S_e_e _A_l_s_o_:

     `nls', `selfStart'

_E_x_a_m_p_l_e_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)

