litter               package:multcomp               R Documentation

_L_i_t_t_e_r _W_e_i_g_h_t_s _D_a_t_a _S_e_t

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

     Dose response of litter weights in rats.

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

     data("litter")

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

     This data frame contains the following variables

     _d_o_s_e dosages at four levels: '0', '5', '50',  '500'.

     _g_e_s_t_t_i_m_e gestation time as covariate.

     _n_u_m_b_e_r number of animals in litter as covariate.

     _w_e_i_g_h_t response variable: average post-birth weights  in the
          entire litter.

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

     Pregnant mice were divided into four groups and the compound in
     four different doses was administered during pregnancy. Their
     litters were evaluated for birth weights.

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

     P. H. Westfall, R. D. Tobias, D. Rom, R. D. Wolfinger, Y. Hochberg
     (1999). _Multiple Comparisons and Multiple Tests Using the SAS
     System_. Cary, NC: SAS Institute Inc., page 109.

     P. H. Westfall (1997). Multiple Testing of General Contrasts Using
     Logical Constraints and Correlations. _Journal of the American
     Statistical Association_, *92*(437), 299-306.

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

       ### fit ANCOVA model to data
       amod <- aov(weight ~ dose + gesttime + number, data = litter)

       ### define matrix of linear hypotheses for `dose'
       doselev <- as.integer(levels(litter$dose))
       K <- rbind(contrMat(table(litter$dose), "Tukey"),
                  otrend = c(-1.5, -0.5, 0.5, 1.5),
                  atrend = doselev - mean(doselev),
                  ltrend = log(1:4) - mean(log(1:4)))

       ### set up multiple comparison object
       Kht <- glht(amod, linfct = mcp(dose = K), alternative = "less")

       ### cf. Westfall (1997, Table 2)
       summary(Kht, test = univariate())
       summary(Kht, test = adjusted("bonferroni"))
       summary(Kht, test = adjusted("Shaffer"))
       summary(Kht, test = adjusted("Westfall"))
       summary(Kht, test = adjusted("single-step"))

