clr                  package:minet                  R Documentation

_C_o_n_t_e_x_t _L_i_k_e_l_i_h_o_o_d _o_r _R_e_l_a_t_e_d_n_e_s_s _N_e_t_w_o_r_k

_D_e_s_c_r_i_p_t_i_o_n:

     'clr' takes the mutual information matrix as input in order to
     return the infered network - see details.

_U_s_a_g_e:

     clr( 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:

     The CLR algorithm is an extension of relevance network. Instead of
      considering the mutual information I(Xi;Xj) between features Xi
     and Xj, it takes into account the score  sqrt(zi^2+zj^2), where 

             zi = max( 0, ( I(Xi;Xj)-mean(Xi) )/sd(Xi) )


       and mean(Xi) and sd(Xi) are, respectively,  the mean and the
     standard deviation of the empirical distribution  of the mutual
     information values I(Xi,Xk),  k=1,...,n.

_V_a_l_u_e:

     'clr' 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 comand plot( as( returned.matrix
     ,"graphNEL") )

_R_e_f_e_r_e_n_c_e_s:

     Jeremiah J. Faith, Boris Hayete, Joshua T. Thaden, Ilaria Mogno,
     Jamey Wierzbowski, Guillaume Cottarel, Simon Kasif, James J.
     Collins, and  Timothy S. Gardner. Large-scale mapping and
     validation of escherichia coli transcriptional regulation from a
     compendium of expression profiles. PLoS Biology, 2007.

_S_e_e _A_l_s_o:

     'build.mim',   'aracne',   'mrnet'

_E_x_a_m_p_l_e_s:

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

