mutualInfo              package:bioDist              R Documentation

_M_u_t_u_a_l _I_n_f_o_r_m_a_t_i_o_n

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

     Calculate mutual information via binning

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

     mutualInfo(x, ...)
     MIdist(x, ...)

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

       x: an n by p matrix or ExpressionSet; if x is an ExpressionSet,
          the function uses its 'exprs' slot

     ...: arguments passed to 'mutualInfo' and 'MIdist':

     _n_b_i_n number of bins to calculate discrete probabilities, default
          is 10 

     _d_i_a_g if TRUE, diagonal of the distance matrix will be displayed,
          default is FALSE 

     _u_p_p_e_r if TRUE, upper triangle of the distance matrix will be
          displayed, default is FALSE  

_D_e_t_a_i_l_s:

     For 'mutualInfo' each row of 'x' is divided into  'nbin' groups
     and then the mutual information is computed, treating  the data as
     if they were discrete.

     For 'MIdist' we use the transformation proposed by Joe (1989),
     delta* = (1 - exp(-2 delta))^.5 where delta is the mutual
     information. The 'MIdist' is  then 1-delta*. Joe argues that this
     measure is then  similar to Kendall's tau, 'tau.dist'.

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

     An object of class 'dist' which contains the pairwise distances.

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

     Robert Gentleman

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

     H. Joe, Relative Entropy Measures of Multivariate Dependence,
     JASA, 1989, 157-164.

_S_e_e _A_l_s_o:

     'dist', 'KLdist.matrix', 'cor.dist', 'KLD.matrix'

_E_x_a_m_p_l_e_s:

      x <- matrix(rnorm(100),nrow=5)
      mutualInfo(x, nbin=3)

