MJ241                   package:mj                   R Documentation

_M_J_2_4_1 _S_p_l_i_t _P_l_o_t

_F_o_r_m_a_t:

     MJ241 data frame with 16 observations on 4 variables.

       [,1]  block    factor   block identifier
       [,2]  fert     factor   fertilizer level
       [,3]  variety  factor   variety level
       [,4]  yield    numeric  plot yield

_D_e_t_a_i_l_s:

_S_o_u_r_c_e:

_R_e_f_e_r_e_n_c_e_s:

_E_x_a_m_p_l_e_s:

     # attach library; get data
     #library( pda )
     data( MJ241 )

     # MJ 24.1 Split Plot
     MJ241$plot <- 4 * ( MJ241$block - 1 ) + MJ241$fert
     MJ241$fert <- factor( MJ241$fert )
     MJ241$block <- factor( MJ241$block )
     MJ241$variety <- factor( MJ241$variety )
     MJ241$plot <- factor( MJ241$plot )

     # split plot with block and block:fert as fixed effects
     MJ241.fit <- aov( yield ~ block * fert + variety + variety:fert,
        MJ241 )

     # split plot using block:fert as random effect
     # see Venables & Ripley (1994, sec. 6.7) or
     # Chambers & Hastie (1992, sec. 5.2.1)
     MJ241.bfit <- aov( yield ~ fert + variety + variety:fert
        + Error( block + block:fert ), MJ241 )
     print( summary( MJ241.bfit ))

     # split plot using LME (Splus on ALPHA computers only)
     # see Lindstrom & Bates (1988) JASA 83:1014-1022
     library( nlme )
     MJ241.lme <- lme( yield ~ fert + variety + variety:fert,
        random = ~ block + block:fert,
        cluster = ~ plot, data = MJ241,
        est.method = "RML", re.structure = "identity")
     print( summary( MJ241.lme ))
     print( fitted.values( MJ241.lme ))