Weights               package:SASmixed               R Documentation

_D_a_t_a _f_r_o_m _a _w_e_i_g_h_t-_l_i_f_t_i_n_g _p_r_o_g_r_a_m

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

     The `Weights' data frame has 399 rows and 5 columns.

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

     This data frame contains the following columns:

     _s_t_r_e_n_g_t_h a numeric vector

     _S_u_b_j_e_c_t a factor with levels `1' to `21'

     _P_r_o_g_r_a_m a factor with levels `CONT' (continuous repetitions and
            weights), `RI' (repetitions increasing) and `WI' (weights
            increasing)

     _S_u_b_j an ordered factor indicating the subject on which the
            measurement is made

     _T_i_m_e a numeric vector indicating the time of the measurement

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

_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
     3.2(a)).

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

     library(SASmixed)
     options(
       contrasts = c(unordered = "contr.SAS", ordered = "contr.poly"))
     data(Weights)
     fm1Weight <- lme( strength ~ Program * Time,
                       data = Weights, random = ~ 1 | Subj)
     summary( fm1Weight )               # compare with output 3.1, p. 91
     VarCorr( fm1Weight )
     anova( fm1Weight )
     fm2Weight <- update( fm1Weight, random = ~ Time | Subj )
     anova( fm1Weight, fm2Weight )
     summary( fm2Weight )
     VarCorr( fm2Weight )
     intervals( fm2Weight )

     fm3Weight <- update( fm2Weight, correlation = corAR1())
     anova( fm2Weight, fm3Weight )
     fm4Weight <- update( fm3Weight, strength ~ Program * (Time + I(Time^2)),
                         random = ~Time|Subj)
     summary( fm4Weight )
     VarCorr( fm4Weight )
     anova( fm4Weight )
     intervals( fm4Weight )

