network.AIC               package:nem               R Documentation

_A_I_C/_B_I_C _c_r_i_t_e_r_i_o_n _f_o_r _n_e_t_w_o_r_k _g_r_a_p_h

_D_e_s_c_r_i_p_t_i_o_n:

     Calclate AIC/BIC for a given network graph (should be transitively
     closed). The number of free parameters equals the number of
     unknown edges in the network graph.

_U_s_a_g_e:

             network.AIC(network,Pm=NULL,k=length(nodes(network$graph)),verbose=TRUE)

_A_r_g_u_m_e_n_t_s:

 network: a nem object (e.g. 'pairwise')

      Pm: prior over models (n x n matrix). If NULL, then a matrix of
          0s is assumed

       k: penalty per parameter in the AIC/BIC calculation. k = 2 for
          classical AIC

 verbose: print out the result

_D_e_t_a_i_l_s:

     For k = log(n) the BIC (Schwarz criterion) is computed. Usually
     this function is not called directly but from 'nemModelSelection'

_V_a_l_u_e:

     AIC/BIC value

_A_u_t_h_o_r(_s):

     Holger Froehlich

_S_e_e _A_l_s_o:

     'nemModelSelection'

_E_x_a_m_p_l_e_s:

        data("BoutrosRNAi2002") 
        D = BoutrosRNAiDiscrete[,9:16]
        control = set.default.parameters(unique(colnames(D)), para=c(0.13,0.05))
        res1 <- nem(D, control=control)
        network.AIC(res1)
        control$lambda=100 # enforce sparsity
        res2 <- nem(D,control=control)
        network.AIC(res2)

