GlobalAncova          package:GlobalAncova          R Documentation

_M_e_t_h_o_d_s _f_o_r _F_u_n_c_t_i_o_n _G_l_o_b_a_l_A_n_c_o_v_a

_D_e_s_c_r_i_p_t_i_o_n:

     There are three possible ways of using 'GlobalAncova'. The general
     way is to define formulas for the full and reduced model,
     respectively, where the formula terms correspond to variables in
     'model.dat'. An alternative is to specify the full model and the
     name of the model terms that shall be tested regarding
     differential expression. In order to make this layout compatible
     with the function call in the first version of the package there
     is also a method where simply a group variable (and possibly
     covariate information) has to be given. This is maybe the easiest
     usage in cases where no 'special' effects like e.g. interactions
     are of interest.

_M_e_t_h_o_d_s:

     _x_x = "_m_a_t_r_i_x", _f_o_r_m_u_l_a._f_u_l_l = "_f_o_r_m_u_l_a", _f_o_r_m_u_l_a._r_e_d = "_f_o_r_m_u_l_a", _m_o_d_e_l._d_a_t = "_A_N_Y", _g_r_o_u_p = "_m_i_s_s_i_n_g", _c_o_v_a_r_s = "_m_i_s_s_i_n_g", _t_e_s_t._t_e_r_m_s = "_m_i_s_s_i_n_g" 
          In this method, besides the expression matrix 'xx', model
          formulas for the full and reduced model and a data frame
          'model.dat' specifying corresponding model terms have to be
          given. Terms that are included in the full but not in the
          reduced model are those whose association with differential
          expression will be tested. The arguments 'group', 'covars'
          and 'test.terms' are '"missing"' since they are not needed
          for this method.

     _x_x = "_m_a_t_r_i_x", _f_o_r_m_u_l_a._f_u_l_l = "_f_o_r_m_u_l_a", _f_o_r_m_u_l_a._r_e_d = "_m_i_s_s_i_n_g", _m_o_d_e_l._d_a_t = "_A_N_Y", _g_r_o_u_p = "_m_i_s_s_i_n_g", _c_o_v_a_r_s = "_m_i_s_s_i_n_g", _t_e_s_t._t_e_r_m_s = "_c_h_a_r_a_c_t_e_r" 
          In this method, besides the expression matrix 'xx', a model
          formula for the full model and a data frame 'model.dat'
          specifying corresponding model terms are required. The
          character argument 'test.terms' names the terms of interest
          whose association with differential expression will be
          tested. The basic idea behind this method is that one can
          select single terms, possibly from the list of terms provided
          by previous 'GlobalAncova' output, and test them without
          having to specify each time a model formula for the reduced
          model. The arguments 'formula.red', 'group' and 'covars' are
          '"missing"' since they are not needed for this method.

     _x_x = "_m_a_t_r_i_x", _f_o_r_m_u_l_a._f_u_l_l = "_m_i_s_s_i_n_g", _f_o_r_m_u_l_a._r_e_d = "_m_i_s_s_i_n_g", _m_o_d_e_l._d_a_t = "_m_i_s_s_i_n_g", _g_r_o_u_p = "_A_N_Y", _c_o_v_a_r_s = "_A_N_Y", _t_e_s_t._t_e_r_m_s = "_m_i_s_s_i_n_g" 
          Besides the expression matrix 'xx' a clinical variable
          'group' is required. Covariate adjustment is possible via the
          argument 'covars' but more complex models have to be
          specified with the methods described above. This method
          emulates the function call in the first version of the
          package. The arguments 'formula.full', 'formula.red',
          'model.dat' and 'test.terms' are '"missing"' since they are
          not needed for this method.

