AvgDailyGain            package:SASmixed            R Documentation

_A_v_e_r_a_g_e _d_a_i_l_y _w_e_i_g_h_t _g_a_i_n _o_f _s_t_e_e_r_s _o_n _d_i_f_f_e_r_e_n_t _d_i_e_t_s

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

     The `AvgDailyGain' data frame has 32 rows and 6 columns.

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

     This data frame contains the following columns:

     _I_d the animal number

     _B_l_o_c_k an ordered factor indicating the barn in which the steer was
            housed.

     _T_r_e_a_t_m_e_n_t an ordered factor with levels `0' < `10' < `20' < `30'
            indicating the amount of medicated feed additive added to
            the base ration.

     _a_d_g a numeric vector of average daily weight gains over a period
            of 160 days.

     _I_n_i_t_W_t a numeric vector giving the initial weight of the animal

     _T_r_t the `Treatment' as a numeric variable

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

     Littel, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R.
     D. (1996), SAS System for Mixed Models, SAS Institute (Data Set
     5.3).

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

     library(SASmixed)
     data(AvgDailyGain)
     options(
       contrasts = c(unordered = "contr.SAS", ordered = "contr.poly"))
     ## plot of adg versus Treatment by Block
     coplot(adg ~ as.numeric(Treatment) | Block, data = AvgDailyGain,
            show = FALSE)
     fm1Adg <- lme( adg ~ InitWt * Treatment - 1, 
           data = AvgDailyGain, random = ~ 1 | Block)
     summary( fm1Adg ) # compare with output 5.1, p. 178
     VarCorr( fm1Adg ) # show the variance component estimates
     anova( fm1Adg )   # checking significance of terms
     fm2Adg <- update( fm1Adg, adg ~ InitWt + Treatment )  # common slope model
     summary( fm2Adg )
     VarCorr( fm2Adg )
     anova( fm2Adg )
     summary( update( fm1Adg, adg ~ InitWt + Treatment - 1 ) )

