mrnet                 package:minet                 R Documentation

_M_a_x_i_m_u_m _R_e_l_e_v_a_n_c_e _M_i_n_i_m_u_m _R_e_d_u_n_d_a_n_c_y

_D_e_s_c_r_i_p_t_i_o_n:

     'mrnet' takes the mutual information matrix as input in order to
     infer the network using  the maximum relevance/minimum redundancy
     feature selection method - see details.

_U_s_a_g_e:

     mrnet( mim )

_A_r_g_u_m_e_n_t_s:

     mim: A square matrix whose i,j th element is the mutual
          information  between variables X_i and X_j - see 'build.mim'.

_D_e_t_a_i_l_s:

     Consider a supervised learning task, where the output is denoted
     by Y and V is the set of input variables. The method ranks the set
      V of inputs according to a score that is the difference between 
     the mutual information with the output variable Y (maximum
     relevance)  and the average mutual information with the previously
     ranked variables (minimum redundancy). The greedy search starts by
     selecting the variable Xi having the highest mutual information
     with the target Y. The second selected variable Xj  will be the
     one that maximizes I(Xj;Y)-I(Xj;Xi).  In the following steps,
     given a set S of selected variables, the criterion  updates S by
     choosing the variable Xk that maximizes  I(Xk;Y) - mean(I(Xk;Xi)),
     Xi in S.
      The MRNET approach consists in repeating this selection procedure
     for  each target variable by putting Y=Xi and  V = X\{Xi},
     i=1,...,n where X is the set of outcomes of all variables. The
     weight of each pair Xi,Xj will be the maximum score between the
     one  computed when Xi is the output and the one computed when Xj
     is  the output.

_V_a_l_u_e:

     'mrnet' returns a matrix which is the weighted adjacency matrix of
     the network. In order to display the network, load the package
     Rgraphviz and use the following command: 
      plot( as( returned.matrix ,"graphNEL") )

_A_u_t_h_o_r(_s):

     Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi

_R_e_f_e_r_e_n_c_e_s:

     Patrick E. Meyer, Kevin Kontos, Frederic Lafitte, and Gianluca
     Bontempi.  Information-theoretic inference of large
     transcriptional regulatory networks. EURASIP Journal on
     Bioinformatics and Systems Biology, 2007.

_S_e_e _A_l_s_o:

     'build.mim',   'clr',   'aracne'

_E_x_a_m_p_l_e_s:

     data(syn.data)
     mim <- build.mim(discretize(syn.data))
     net <- mrnet(mim)

