SSbiexp                package:stats                R Documentation

_B_i_e_x_p_o_n_e_n_t_i_a_l _m_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     This 'selfStart' model evaluates the biexponential model function
     and its gradient.  It has an 'initial' attribute that  creates
     initial estimates of the parameters 'A1', 'lrc1', 'A2', and
     'lrc2'.

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_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 logarithm of the
          rate constant of the first exponential.

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

    lrc2: a numeric parameter representing the natural logarithm of the
          rate constant of the second exponential.

_V_a_l_u_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, 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:

     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
     getInitial(conc ~ SSbiexp(time, A1, lrc1, A2, lrc2), data = Indo.1)
     ## Initial values are in fact the converged values
     fm1 <- nls(conc ~ SSbiexp(time, A1, lrc1, A2, lrc2), data = Indo.1)
     summary(fm1)

