glymet                  package:drc                  R Documentation

_G_l_y_p_h_o_s_a_t_e _a_n_d _m_e_t_s_u_l_f_u_r_o_n-_m_e_t_h_y_l _t_e_s_t_e_d _o_n _a_l_g_a_e.

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

     The dataset has 7 mixtures, 8 dilutions, two replicates and 5
     common control controls.  Four observations are missing, giving a
     total of 113 observations.

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

     data(glymet)

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

     A data frame with 113 observations on the following 3 variables.

     '_d_o_s_e' a numeric vector of dose values

     '_p_c_t' a numeric vector denoting the grouping according to the
          mixtures percentages

     '_r_g_r' a numeric vector of response values (relative growth rates)

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

     The dataset is analysed in Soerensen et al (2007).  The
     concentration addition model can be entertained for this dataset.

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

     The dataset is kindly provided by Nina Cedergreen, Department of
     Agricultural Sciences,  Royal Veterinary and Agricultural
     University, Denmark.

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

     Soerensen, H. and Cedergreen, N. and Skovgaard, I. M. and
     Streibig, J. C. (2007)  An isobole-based statistical model and
     test for synergism/antagonism in binary mixture toxicity
     experiments,  _Environmental and Ecological Statistics_, *14*,
     383-397.

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

     ## Fitting the model with freely varying ED50 values
     glymet.free <- drm(rgr~dose, pct, data = glymet, 
     fct = LL.3(), pmodels = list(~factor(pct) , ~1, ~factor(pct))) 

     ## Lack-of-fit test
     modelFit(glymet.free)  # acceptable
     summary(glymet.free)

     ## Plotting isobole structure
     isobole(glymet.free, exchange=0.01)

     ## Fitting the concentration addition model
     glymet.ca <- mixture(glymet.free, model = "CA")

     ## Comparing to model with freely varying e parameter
     anova(glymet.ca, glymet.free)  # borderline accepted

     ## Plotting isobole based on concentration addition
     isobole(glymet.free, glymet.ca, exchange = 0.01)  # acceptable fit

     ## Fitting the Hewlett model
     glymet.hew <- mixture(glymet.free, model = "Hewlett")

     ### Comparing to model with freely varying e parameter
     anova(glymet.ca, glymet.hew)  
     # borderline accepted
     # the Hewlett model offers no improvement over concentration addition

     ## Plotting isobole based on the Hewlett model
     isobole(glymet.free, glymet.hew, exchange = 0.01)  
     # no improvement over concentration addition

