Alfalfa                 package:nlme                 R Documentation

_S_p_l_i_t-_P_l_o_t _E_x_p_e_r_i_m_e_n_t _o_n _V_a_r_i_e_t_i_e_s _o_f _A_l_f_a_l_f_a

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

     The `Alfalfa' data frame has 72 rows and 4 columns.

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

     This data frame contains the following columns:

     _V_a_r_i_e_t_y a factor with levels `Cossack', `Ladak', and  `Ranger' 

     _D_a_t_e a factor with levels `None'  `S1'  `S20'  `O7' 

     _B_l_o_c_k a factor with levels `1'  `2'  `3'  `4'  `5'  `6' 

     _Y_i_e_l_d a numeric vector

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

     These data are described in Snedecor and Cochran (1980) as an
     example of a split-plot design. The treatment structure used in
     the experiment was a 3times4 full factorial, with three varieties
     of alfalfa and four dates of third cutting in 1943. The
     experimental units were arranged into six blocks, each subdivided
     into four plots. The varieties of alfalfa (Cossac, Ladak, and
     Ranger) were assigned randomly to the blocks and the dates of
     third cutting (None, S1-September 1, S20-September 20, and
     O7-October 7) were randomly assigned to the plots.  All four dates
     were used on each block.

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

     Pinheiro, J. C. and Bates, D. M. (2000), Mixed-Effects Models in S
     and S-PLUS, Springer, New York.  (Appendix A.1)

     Snedecor, G. W. and Cochran, W. G. (1980), Statistical Methods
     (7th ed), Iowa State University Press, Ames, IA

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

     data(Alfalfa)

