dummy.coef               package:stats               R Documentation

_E_x_t_r_a_c_t _C_o_e_f_f_i_c_i_e_n_t_s _i_n _O_r_i_g_i_n_a_l _C_o_d_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     This extracts coefficients in terms of the original levels of the
     coefficients rather than the coded variables.

_U_s_a_g_e:

     dummy.coef(object, ...)

     ## S3 method for class 'lm':
     dummy.coef(object, use.na = FALSE, ...)

     ## S3 method for class 'aovlist':
     dummy.coef(object, use.na = FALSE, ...)

_A_r_g_u_m_e_n_t_s:

  object: a linear model fit.

  use.na: logical flag for coefficients in a singular model. If
          'use.na' is true, undetermined coefficients will be missing;
          if false they will get one possible value.

     ...: arguments passed to or from other methods.

_D_e_t_a_i_l_s:

     A fitted linear model has coefficients for the contrasts of the
     factor terms, usually one less in number than the number of
     levels.  This function re-expresses the coefficients in the
     original coding; as the coefficients will have been fitted in the
     reduced basis, any implied constraints (e.g., zero sum for
     'contr.helmert' or 'contr.sum' will be respected.  There will be
     little point in using 'dummy.coef' for 'contr.treatment'
     contrasts, as the missing coefficients are by definition zero.

     The method used has some limitations, and will give incomplete
     results for terms such as 'poly(x, 2))'.  However, it is adequate
     for its main purpose, 'aov' models.

_V_a_l_u_e:

     A list giving for each term the values of the coefficients. For a
     multistratum 'aov' model, such a list for each stratum.

_W_a_r_n_i_n_g:

     This function is intended for human inspection of the output: it
     should not be used for calculations.  Use coded variables for all
     calculations.

     The results differ from S for singular values, where S can be
     incorrect.

_S_e_e _A_l_s_o:

     'aov', 'model.tables'

_E_x_a_m_p_l_e_s:

     options(contrasts=c("contr.helmert", "contr.poly"))
     ## From Venables and Ripley (2002) p.165.
     N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0)
     P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0)
     K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0)
     yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5,
     55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0)

     npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P),
                       K=factor(K), yield=yield)
     npk.aov <- aov(yield ~ block + N*P*K, npk)
     dummy.coef(npk.aov)

     npk.aovE <- aov(yield ~  N*P*K + Error(block), npk)
     dummy.coef(npk.aovE)

