

SSfpl {nls}                                  R Documentation

_F_o_u_r_-_p_a_r_a_m_e_t_e_r _L_o_g_i_s_t_i_c _M_o_d_e_l

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

     This `selfStart' model evaluates the four-parameter
     logistic function and its gradient.  It has an `ini-
     tial' attribute that will evaluate initial estimates of
     the parameters `A', `B', `xmid', and `scal' for a given
     set of data.

_U_s_a_g_e_:

     SSfpl(input, A, B, xmid, scal)

_A_r_g_u_m_e_n_t_s_:

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

       A: a numeric parameter representing the horizontal
          asymptote on the left side (very small values of
          `input').

       B: a numeric parameter representing the horizontal
          asymptote on the right side (very large values of
          `input').

    xmid: a numeric parameter representing the `input' value
          at the inflection point of the curve.  The value
          of `SSfpl' will be midway between `A' and `B' at
          `xmid'.

    scal: a numeric scale parameter on the `input' axis.

_V_a_l_u_e_:

     a numeric vector of the same length as `input'.  It is
     the value of the expression `A+(B-A)/(1+exp((xmid-
     input)/scal))'.  If all of the arguments `A', `B',
     `xmid', and `scal' 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( ChickWeight )
     Chick.1 <- ChickWeight[ChickWeight$Chick == 1, ]
     SSfpl( Chick.1$Time, 13, 368, 14, 6 )  # response only
     A <- 13; B <- 368; xmid <- 14; scal <- 6
     SSfpl( Chick.1$Time, A, B, xmid, scal ) # response and gradient
     getInitial(weight ~ SSfpl(Time, A, B, xmid, scal), data = Chick.1)
     ## Initial values are in fact the converged values
     fm1 <- nls(weight ~ SSfpl(Time, A, B, xmid, scal), data = Chick.1)
     summary(fm1)

