SSgompertz               package:stats               R Documentation

_G_o_m_p_e_r_t_z _G_r_o_w_t_h _M_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     This 'selfStart' model evaluates the Gompertz growth model and its
     gradient.  It has an 'initial' attribute that creates initial
     estimates of the parameters 'Asym', 'b2', and 'b3'.

_U_s_a_g_e:

     SSgompertz(x, Asym, b2, b3)

_A_r_g_u_m_e_n_t_s:

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

    Asym: a numeric parameter representing the asymptote.

      b2: a numeric parameter related to the value of the function at
          'x = 0'

      b3: a numeric parameter related to the scale the 'x' axis.

_V_a_l_u_e:

     a numeric vector of the same length as 'input'.  It is the value
     of the expression 'Asym*exp(-b2*b3^x)'.  If all of the arguments
     'Asym', 'b2', and 'b3' 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):

     Douglas Bates

_S_e_e _A_l_s_o:

     'nls', 'selfStart'

_E_x_a_m_p_l_e_s:

     DNase.1 <- subset(DNase, Run == 1)
     SSlogis(log(DNase.1$conc), 4.5, 2.3, 0.7)  # response only
     Asym <- 4.5; b2 <- 2.3; b3 <- 0.7
     SSgompertz(log(DNase.1$conc), Asym, b2, b3 ) # response and gradient
     getInitial(density ~ SSgompertz(log(conc), Asym, b2, b3),
                data = DNase.1)
     ## Initial values are in fact the converged values
     fm1 <- nls(density ~ SSgompertz(log(conc), Asym, b2, b3),
                data = DNase.1)
     summary(fm1)

