nemModelSelection            package:nem            R Documentation

_M_o_d_e_l _s_e_l_e_c_t_i_o_n _f_o_r _n_e_s_t_e_d _e_f_f_e_c_t _m_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     Infers models with different regularization constants, compares
     them via the BIC or AIC criterion and returns the highest scoring
     one

_U_s_a_g_e:

     nemModelSelection(lambdas,D,inference="nem.greedy",models=NULL,control=set.default.parameters(unique(colnames(D))),verbose=TRUE,...)

_A_r_g_u_m_e_n_t_s:

 lambdas: vector of regularization constants

       D: data matrix with experiments in the columns (binary or
          continious)

inference: 'search' to use exhaustive enumeration, 'triples' for
          triple-based inference, 'pairwise' for the pairwise
          heuristic, 'ModuleNetwork' for the module based inference,
          'nem.greedy' for greedy hillclimbing, 'nem.greedyMAP' for
          alternating MAP optimization using log odds or log p-value
          densities

  models: a list of adjacency matrices for model search. If NULL, an
          exhaustive enumeration of all possible models is performed.

 control: list of parameters: see 'set.default.parameters'

 verbose: do you want to see progression statements? Default: TRUE

     ...: other arguments to pass to function 'nem' or 'network.AIC'

_D_e_t_a_i_l_s:

     'nemModelSelection' internally calls 'nem' to infer a model with a
     given regularization constant. The comparison between models is
     based on the BIC or AIC criterion, depending on the parameters
     passed to 'network.AIC'.

_V_a_l_u_e:

     nem object

_A_u_t_h_o_r(_s):

     Holger Froehlich

_S_e_e _A_l_s_o:

     'set.default.parameters', 'nem', 'network.AIC'

_E_x_a_m_p_l_e_s:

        data("BoutrosRNAi2002")
        D <- BoutrosRNAiDiscrete[,9:16]   
        hyper = set.default.parameters(unique(colnames(D)), para=c(0.13, 0.05), Pm=diag(4))
        res <- nemModelSelection(c(0.1,1,10), D, control=hyper)      
        
        plot.nem(res,main="highest scoring model")      

