deguelin                 package:drc                 R Documentation

_D_e_g_u_e_l_i_n _a_p_p_l_i_e_d _t_o _c_h_r_y_s_a_n_t_h_e_m_u_m _a_p_h_i_s

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

     Quantal assay data from an experiment where the insectide deguelin
     was applied to _Macrosiphoniella sanborni_.

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

     data(deguelin)

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

     A data frame with 6 observations on the following 4 variables.

     '_d_o_s_e' a numeric vector of doses applied

     '_l_o_g_1_0_d_o_s_e' a numeric vector of logarithm-transformed doses

     '_r' a numeric vector contained number of dead insects

     '_n' a numeric vector contained the total number of insects

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

     The log-logistic model provides an inadequate fit.

     The dataset is used in Nottingham and Birch (2000) to illustrate a
     semiparametric approach to dose-response modelling.

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

     Morgan, B. J. T. (1992) _Analysis of Quantal Response Data_,
     London: Chapman & Hall/CRC (Table 3.9, p. 117).

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

     Notttingham, Q. J. and Birch, J. B. (2000) A semiparametric
     approach to analysing dose-response data, _Statist. Med._, *19*,
     389-404.

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

     ## Log-logistic fit
     deguelin.m1 <- drm(r/n~dose, weights=n, data=deguelin, fct=LL.2(), type="binomial")
     modelFit(deguelin.m1)
     summary(deguelin.m1)

     ## Loess fit
     deguelin.m2 <- loess(r/n~dose, data=deguelin, degree=1)

     ## Plot of data with fits superimposed
     plot(deguelin.m1, ylim=c(0.2,1))
     lines(1:60, predict(deguelin.m2, newdata=data.frame(dose=1:60)), col = 2, lty = 2)

     lines(1:60, 0.95*predict(deguelin.m2, 
     newdata=data.frame(dose=1:60))+0.05*predict(deguelin.m1, newdata=data.frame(dose=1:60), se = FALSE),
     col = 3, lty=3)

