auxins                  package:drc                  R Documentation

_E_f_f_e_c_t _o_f _t_e_c_h_n_i_c_a_l _g_r_a_d_e _a_n_d _c_o_m_m_e_r_c_i_a_l_l_y _f_o_r_m_u_l_a_t_e_d _a_u_x_i_n _h_e_r_b_i_c_i_d_e_s

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

     MCPA, 2,4-D, mecorprop and dichorlprop were applied either as
     technical grades materials  (h = 1, 2, 3, 4) or as commercial
     formulations (herb = 5, 6, 7, 8).  Each experimental unit
     consisted of five 1-week old seedlings grown together in a pot of
     nutrient solution during 14 days.

_U_s_a_g_e:

     data(auxins)

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

     A data frame with 150 observations on the following 5 variables.

     '_r' a numeric vector

     '_h' a numeric vector

     '_w' a numeric vector

     '_y' a numeric vector

     '_d_o_s_e' a numeric vector

_D_e_t_a_i_l_s:

     Data are parts of a larger joint action experiment with various
     herbicides.

     The eight herbicide preparations are naturally grouped into four
     pairs: (1, 5), (2, 6), (3, 7), and (4, 8), and in each pair of
     herbicides should have the same active ingredients but different
     formulation constituents, which were assumed to be biologically
     inert. The data consist of the 150 observations y of dry weights,
     each observation being the weight of five plants grown in the same
     pot. All the eight herbicide preparations have essentially the
     same mode of action in the plant; they all act like the plant 
     auxins, which are plant regulators that affect cell enlongation an
     other  essential metabolic pathways. One of the objects of the
     experiment was to test if the response functions were identical
     except for a multiplicative factor in  the dose. This is a
     necessary, but not a sufficient, condition for a similar mode of
     action for the herbicides.

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

     Streibig, J. C. (1987). Joint action of root-absorbed mixtures of
     auxin  herbicides in Sinapis alba L. and barley (Hordeum vulgare
     L.)  _Weed Research_, *27*, 337-347.

_R_e_f_e_r_e_n_c_e_s:

     Rudemo, M., Ruppert, D., and Streibig, J. C. (1989). Random-Effect
     Models  in Nonlinear Regression with Applications to Bioassay.
     _Biometrics_, *45*, 349-362.

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

     ## Fitting model with varying lower limits
     auxins.m1 <- boxcox(drm(y ~ dose, h, 
     pmodels = data.frame(h, h, 1, h), fct = LL.4(), data = auxins), method = "anova")

     ## Fitting model with common lower limit
     auxins.m2 <- boxcox(drm(y ~ dose, h, 
     pmodels = data.frame(h, 1, 1, h), fct = LL.4(), data = auxins), method = "anova")

     ## Comparing the two models
     anova(auxins.m2, auxins.m1)

