carrots              package:robustbase              R Documentation

_I_n_s_e_c_t _D_a_m_a_g_e_s _o_n _C_a_r_r_o_t_s

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

     The damage carrots data set from Phelps (1982) was used by
     McCullagh and Nelder (1989) in order to illustrate diagnostic
     techniques because of the presence of an outlier. In a soil
     experiment trial with three blocks, eight levels of insecticide
     were applied and the carrots were tested for insect damage.

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

     data(carrots)

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

     A data frame with 24 observations on the following 4 variables.

     _s_u_c_c_e_s_s integer giving the number of carrots with insect damage.

     _t_o_t_a_l integer giving the total number of carrots per experimental
          unit.

     _l_o_g_d_o_s_e a numeric vector giving log(dose) values (eight different
          levels only).

     _b_l_o_c_k factor with levels 'B1' to 'B3'

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

     Phelps, K. (1982). Use of the complementary log-log function to
     describe doseresponse relationships in insecticide evaluation
     field trials. 
      In R. Gilchrist (Ed.), _Lecture Notes in Statistics, No. 14.
     GLIM.82: Proceedings of the International Conference on
     Generalized Linear Models_; Springer-Verlag.

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

     McCullagh P. and Nelder, J. A. (1989) _Generalized Linear Models._
     London: Chapman and Hall.

     Eva Cantoni and Elvezio Ronchetti (2001); JASA,  and 
      Eva Cantoni (2004); JSS, see 'glmrob'

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

     data(carrots)
     str(carrots)
     plot(success/total ~ logdose, data = carrots, col = as.integer(block))
     coplot(success/total ~ logdose | block, data = carrots)

     ## Classical glm
     Cfit0 <- glm(cbind(success, total-success) ~ logdose + block,
                  data=carrots, family=binomial)
     summary(Cfit0)

     ## Robust Fit (see help(glmrob)) ....

