BOD                   package:nls                   R Documentation

_B_i_o_c_h_e_m_i_c_a_l _O_x_y_g_e_n _D_e_m_a_n_d

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

     The `BOD' data frame has 6 rows and 2 columns giving the
     biochemical oxygen demand versus time in an evaluation of water
     quality.

_F_o_r_m_a_t:

     This data frame contains the following columns:

     _T_i_m_e A numeric vector giving the time of the measurement
            (days).

     _d_e_m_a_n_d A numeric vector giving the biochemical oxygen
            demand (mg/l).

_S_o_u_r_c_e:

     Bates, D.M. and Watts, D.G. (1988), Nonlinear Regression Analysis
     and Its Applications, Wiley, Appendix A1.4.

     Originally from Marske (1967), Biochemical Oxygen Demand Data
     Interpretation Using Sum of Squares Surface M.Sc. Thesis,
     University of Wisconsin - Madison.

_E_x_a_m_p_l_e_s:

     library(nls)
     data(BOD)
     # simplest form of fitting a first-order model to these data
     fm1 <- nls(demand ~ A*(1-exp(-exp(lrc)*Time)), data = BOD,
        start = c(A = 20, lrc = log(.35)))
     coef(fm1)
     print(fm1)
     # using the plinear algorithm
     fm2 <- nls(demand ~ (1-exp(-exp(lrc)*Time)), data = BOD,
        start = c(lrc = log(.35)), algorithm = "plinear", trace = TRUE)
     # using a self-starting model
     fm3 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
     summary( fm3 )

