build.mim               package:minet               R Documentation

_B_u_i_l_d _M_u_t_u_a_l _I_n_f_o_r_m_a_t_i_o_n _M_a_t_r_i_x

_D_e_s_c_r_i_p_t_i_o_n:

     'build.mim' takes the dataset as input and computes the  mutual
     information beetween all pair of variables according  to the
     mutual inforamtion estimator 'estimator'.  The results are saved
     in the mutual information matrix (MIM), a square  matrix whose
     (i,j) element is the mutual information between variables Xi and
     Xj.

_U_s_a_g_e:

     build.mim(data, estimator="mi.empirical")

_A_r_g_u_m_e_n_t_s:

    data: data.frame containing gene expression data or any dataset
          where columns contain variables/features and rows contain
          outcomes/samples.

estimator: The name of the mutual information estimator. The package
          implements four estimators :  "mi.empirical", "mi.mm",
          "mi.shrink", "mi.sg" (default:"mi.empirical") - see details. 
          These estimators require discrete data values - see
          'discretize'.

_D_e_t_a_i_l_s:


        *  "mi.empirical" : This estimator computes the entropy of the
           empirical probability distribution.

        *  "mi.mm" : This is the Miller-Madow asymptotic bias corrected
           empirical estimator.

        *  "mi.shrink" : This is a shrinkage estimate of the entropy of
           a Dirichlet probability distribution.

        *  "mi.sg" : This is the Schurmann-Grassberger estimate of the
           entropy of a Dirichlet probability distribution.

_V_a_l_u_e:

     'build.mim' returns the mutual information matrix.

_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.

     J. Beirlant, E. J. Dudewica, L. Gyofi, and E. van der Meulen.
     Nonparametric  entropy estimation : An overview. Journal of
     Statistics, 1997.

     Jean Hausser. Improving entropy estimation and the inference of
     genetic regulatory networks. Master thesis of the National
     Institute of Applied Sciences of Lyon, 2006.

_S_e_e _A_l_s_o:

     'clr',   'aracne',   'mrnet'

_E_x_a_m_p_l_e_s:

       data(syn.data)
       #mutual information estimator
       estimator="mi.empirical"
       #number of bins used to discretize
       nb.bins = sqrt(nrow(syn.data))
       mim <- build.mim(discretize(syn.data,nbins=nb.bins),estimator)

