SSasymp                package:stats                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 regression
     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 logarithm 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:

     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)