BOD                 package:datasets                 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.

_U_s_a_g_e:

     BOD

_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:

     require(stats)
     # 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 )

