rcModels           package:preprocessCore           R Documentation

_F_i_t _r_o_w-_c_o_l_u_m_n _m_o_d_e_l _t_o _a _m_a_t_r_i_x

_D_e_s_c_r_i_p_t_i_o_n:

     These functions fit row-column effect models to matrices

_U_s_a_g_e:

     rcModelPLM(y)
     rcModelWPLM(y, w)
     rcModelMedianPolish(y)

_A_r_g_u_m_e_n_t_s:

       y: A numeric matrix

       w: A matrix or vector of weights. These should be non-negative.

_D_e_t_a_i_l_s:

     These functions fit row-column models to the specified input
     matrix. Specifically the model 

                       y_ij = r_i + c_j + e_ij

     with r_i and c_j as row and column effects respectively. Note that
     this functions treat the row effect as the parameter to be
     constrained using sum to zero (for 'rcModelPLM' and 'rcModelWPLM')
     or median of zero (for 'rcModelMedianPolish').

     The 'rcModelPLM' and 'rcModelWPLM' functions use a robust linear
     model procedure for fitting the model.

     The function 'rcModelMedianPolish' uses the median polish
     algorithm.

_V_a_l_u_e:

     A list with following items: 

Estimates: The parameter estimates. Stored in column effect then row
          effect order

 Weights: The final weights used

Residuals: The residuals

StdErrors: Standard error estimates. Stored in column effect then row
          effect order

_A_u_t_h_o_r(_s):

     B. M. Bolstad bmb@bmbolstad.com

_S_e_e _A_l_s_o:

_E_x_a_m_p_l_e_s:

     col.effects <- c(10,11,10.5,12,9.5)
     row.effects <- c(seq(-0.5,-0.1,by=0.1),seq(0.1,0.5,by=0.1))

     y <- outer(row.effects, col.effects,"+")
     w <- runif(50)

     rcModelPLM(y)
     rcModelWPLM(y, w)
     rcModelMedianPolish(y)

     y <- y + rnorm(50)

     rcModelPLM(y)
     rcModelWPLM(y, w)
     rcModelMedianPolish(y)