nem                   package:nem                   R Documentation

_N_e_s_t_e_d _E_f_f_e_c_t_s _M_o_d_e_l_s - _m_a_i_n _f_u_n_c_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     The main function to perform model learning from data

_U_s_a_g_e:

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

     ## S3 method for class 'nem':
     print(x, ...)

_A_r_g_u_m_e_n_t_s:

       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

       x: nem object

     ...: other arguments to pass

_D_e_t_a_i_l_s:

     If parameter Pm != NULL and parameter lambda == 0, a Bayesian
     approach to include prior knowledge is used. Alternatively, the
     regularization parameter lambda can be tuned in a model selection
     step via the function 'nemModelSelection' using the BIC criterion.
      If automated subset selection of effect reporters is used and
     parameter type == CONTmLLMAP, the regularization parameter delta
     is tuned via the AIC model selection criterion. Otherwise, an
     iterative algorithm is executed, which in an alternating
     optimization scheme reconstructs a network given the current set
     of effect reporters and then selects the effect reporters having
     the highest likelihood under the given network. The procedure is
     run until convergence.

     The function 'plot.nem' plots the inferred phenotypic hierarchy as
     a directed graph, the likelihood distribution of the models (only
     for exhaustive search) or the posterior position of the effected
     genes.

_V_a_l_u_e:

   graph: the inferred directed graph (graphNEL object)

     mLL: log posterior marginal likelihood of final model

     pos: posterior over effect positions

  mappos: MAP estimate of effect positions

selected: selected E-gene subset

LLperGene: likelihood per selected E-gene

 control: hyperparameter as in function call

_A_u_t_h_o_r(_s):

     Holger Froehlich <URL: http:/www.dkfz.de/mga2/people/froehlich>,
     Florian Markowetz <URL: http://genomics.princeton.edu/~florian>

_S_e_e _A_l_s_o:

     'set.default.parameters', 'nemModelSelection', 'nem.jackknife',
     'nem.bootstrap', 'nem.consensus', 'local.model.prior', 'plot.nem'

_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,inference="search", control=control)
        res2 <- nem(D,inference="pairwise", control=control)
        res3 <- nem(D,inference="triples", control=control)
        res4 <- nem(D,inference="ModuleNetwork", control=control)
        res5 <- nem(D,inference="nem.greedy", control=control)        
        res6 = nem(BoutrosRNAiLods, inference="nem.greedyMAP", control=control)
        

        par(mfrow=c(2,3))
        plot.nem(res1,main="exhaustive search")
        plot.nem(res2,main="pairs")
        plot.nem(res3,main="triples")
        plot.nem(res4,main="module network")
        plot.nem(res5,main="greedy hillclimber")      
        plot.nem(res6,main="alternating MAP optimization")

