Dyestuff                package:lme4                R Documentation

_Y_i_e_l_d _o_f _d_y_e_s_t_u_f_f _b_y _b_a_t_c_h

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

     The 'Dyestuff' data frame provides the yield of dyestuff
     (Naphthalene Black 12B) from 5 different preparations from each of
     6 different batchs of an intermediate product (H-acid).  The
     'Dyestuff2' data were generated data in the same structure but
     with a large residual variance relative to the batch variance.

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

     data(Dyestuff)

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

     Data frames, each with 30 observations on the following 2
     variables.

     '_B_a_t_c_h' a factor indicating the batch of the intermediate product
          from which the preparation was created.

     '_Y_i_e_l_d' the yield of dyestuff from the preparation (grams of
          standard color).

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

     The 'Dyestuff' data are described in Davies and Goldsmith (1972)
     as coming from "an investigation to find out how much the
     variation from batch to batch in the quality of an intermediate
     product (H-acid) contributes to the variation in the yield of the
     dyestuff (Naphthalene Black 12B) made from it.  In the experiment
     six samples of the intermediate, representing different batches of
     works manufacture, were obtained, and five preparations of the
     dyestuff were made in the laboratory from each sample.  The
     equivalent yield of each preparation as grams of standard colour
     was determined by dye-trial."

     The 'Dyestuff2' data are described in Box and Tiao (1973) as
     illustrating " the case where between-batches mean square is less
     than the within-batches mean square.  These data had to be
     constructed for although examples of this sort undoubtably occur
     in practice, they seem to be rarely published."

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

     O.L. Davies and P.L. Goldsmith (eds), _Statistical Methods in
     Research and Production, 4th ed._, Oliver and Boyd, (1972),
     section 6.4

     G.E.P. Box and G.C. Tiao, _Bayesian Inference in Statistical
     Analysis_, Addison-Wesley, (1973), section 5.1.2

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

     str(Dyestuff)
     dotplot(reorder(Batch, Yield) ~ Yield, Dyestuff,
             ylab = "Batch", jitter.y = TRUE, aspect = 0.3,
             type = c("p", "a"))
     dotplot(reorder(Batch, Yield) ~ Yield, Dyestuff2,
             ylab = "Batch", jitter.y = TRUE, aspect = 0.3,
             type = c("p", "a"))
     (fm1 <- lmer(Yield ~ 1|Batch, Dyestuff))
     (fm2 <- lmer(Yield ~ 1|Batch, Dyestuff2))

