plotDiscriminantFuzzyPattern       package:DFP       R Documentation

_P_l_o_t_s _t_h_e _D_i_s_c_r_i_m_i_n_a_n_t _F_u_z_z_y _P_a_t_t_e_r_n _o_f _t_h_e _r_e_l_e_v_a_n_t _g_e_n_e_s

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

     This function plots the _Discriminant Fuzzy Pattern_ of the
     relevant genes (in rows) for the sample  classes (in columns), as
     well as the impact factor which determines if a gene belongs to a
     _Fuzzy Pattern_  in a class (if its value is higher than the
     piVal).
      The relevant genes are those which are present in almost two
     different _Fuzzy Patterns_ with different linguistic labels.
      The plotting is made in both graphical and text mode.

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

     plotDiscriminantFuzzyPattern(dfp, overlapping = 2)

_A_r_g_u_m_e_n_t_s:

     dfp: A matrix with the fuzzy patterns and impact factors for the
          relevant genes. 

overlapping: Modifies the number of membership functions used in the
          discretization process.
            Possible values: 

             1.  Low, Medium, High.

             2.  Low, Low-Medium, Medium, Medium-High, High.

             3.  Low, Low-Medium, Low-Medium-High, Medium,
                Medium-High, High.

          'Default value = 2'. 

_V_a_l_u_e:

     A matrix with the discriminant genes in rows, along with the
     _Fuzzy Pattern_ for each class (in columns).
      This object contains an attribute ('ifs') which stores the
     _Impact Factors_ used to determine if a gene  belongs to a _Fuzzy
     Pattern_ in a class (if the value is higher than the piVal).

_A_u_t_h_o_r(_s):

     Rodrigo Alvarez-Gonzalez
      Daniel Glez-Pena
      Fernando Diaz
      Florentino Fdez-Riverola
      Maintainer: Rodrigo Alvarez-Gonzalez <rodrigo.djv@uvigo.es>

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

     F. Diaz; F. Fdez-Riverola; D. Glez-Pena; J.M. Corchado. Using
     Fuzzy Patterns for Gene Selection and Data Reduction on Microarray
     Data. 7th International Conference on Intelligent Data Engineering
     and Automated Learning: IDEAL 2006, (2006) pp. 1095-1102

