Soils                  package:car                  R Documentation

_S_o_i_l _C_o_m_p_o_s_i_t_i_o_n_s _o_f _P_h_y_s_i_c_a_l _a_n_d _C_h_e_m_i_c_a_l _C_h_a_r_a_c_t_e_r_i_s_t_i_c_s

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

     Soil characteristics were measured on samples from three types of
     contours (Top, Slope, and Depression) and at four depths (0-10cm,
     10-30cm, 30-60cm, and 60-90cm).  The area was divided into 4 
     blocks, in a randomized block design.

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

     data(Soils)

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

     A data frame with 48 observations on the following 14 variables. 
     There are 3 factors and 9 response variables.

     '_G_r_o_u_p' a factor with 12 levels, corresponding to the combinations
          of 'Contour' and 'Depth' 

     '_C_o_n_t_o_u_r' a factor with 3 levels: 'Depression' 'Slope' 'Top'

     '_D_e_p_t_h' a factor with 4 levels: '0-10' '10-30' '30-60' '60-90'

     '_G_p' a factor with 12 levels, giving abbreviations for the groups:
           'D0' 'D1' 'D3' 'D6' 'S0' 'S1' 'S3' 'S6' 'T0' 'T1' 'T3' 'T6'

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

     '_p_H' soil pH

     '_N' total nitrogen in %

     '_D_e_n_s' bulk density in gm/cm$^3$ 

     '_P' total phosphorous in ppm

     '_C_a' calcium in me/100 gm.

     '_M_g' magnesium in me/100 gm.

     '_K' phosphorous in me/100 gm.

     '_N_a' sodium in me/100 gm.

     '_C_o_n_d_u_c' conductivity

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

     These data provide good examples of MANOVA and canonical
     discriminant analysis in a somewhat complex multivariate setting. 
     They may be treated as a one-way design (ignoring 'Block'), by
     using either 'Group' or 'Gp' as the factor, or a two-way
     randomized block design using 'Block', 'Contour' and 'Depth'
     (quantitative, so orthogonal polynomial contrasts are useful).

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

     Horton, I. F.,Russell, J. S., and Moore, A. W. (1968)
     Multivariate-covariance and canonical analysis:  A method for
     selecting the most effective discriminators in a multivariate
     situation. _Biometrics_ *24*, 845-858. <URL:
     http://www.stat.lsu.edu/faculty/moser/exst7037/soils.sas>

_R_e_f_e_r_e_n_c_e_s:

     Khattree, R., and Naik, D. N. (2000) _Multivariate Data Reduction
     and Discrimination with SAS Software._ SAS Institute.

     Friendly, M. (in press) Data ellipses, HE plots and reduced-rank
     displays for multivariate linear models: SAS software and
     examples. _Journal of Statistical Software_.

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

     Soils

