validate                package:minet                R Documentation

_I_n_f_e_r_e_n_c_e _V_a_l_i_d_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     'validate' compares the infered network to the true underlying
     network for several threshold values  and appends the resulting
     confusion matrices to the returned object.

_U_s_a_g_e:

     validate( inet, tnet, steps=50 )

_A_r_g_u_m_e_n_t_s:

    inet: This is the infered network, a data.frame or matrix obtained
          by one of the functions minet, aracne, clr or mrnet .

    tnet: The true underlying network. This network must have the same
          size and variable names as 'inet'.

   steps: The number of threshold values to be used in the validation
          process - see 'details'.

_D_e_t_a_i_l_s:

     For each of the 'steps' threshold values T, the edges whose weight
     are (strictly)  below T are eliminated. All the other edges will
     have a weight 1.  Thus for each threshold, we obtain a boolean
     network from the infered network. This  network is compared to the
     true underlying network, 'tnet', in order to compute a  confusion
     (adjacency) matrix. All the confusion matrices, obtained with
     different threshold values, are appended to the  returned object.
     In the end the 'validate' function returns a data.frame containing
     'steps' confusion matrices.

_V_a_l_u_e:

     'validate' returns a data.frame whith four columns named 'thrsh,
     tp, fp, fn'. These values are  computed for each of the 'steps'
     thresholds. Thus each row of the returned object contains the
     confusion matrix for a different threshold.

_S_e_e _A_l_s_o:

     'minet',   'vis.res'

_E_x_a_m_p_l_e_s:

     data(syn.data)
     data(syn.net)
     inf.net <- mrnet(build.mim(discretize(syn.data)))
     table <- validate( inf.net, syn.net, steps=100 )

