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 AIC criterion and returns the highest scoring one

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

     nemModelSelection(lambdas,D,inference="nem.greedy",models=NULL,type="mLL",para=NULL,hyperpara=NULL,Pe=NULL,Pmlocal=NULL,Pm=NULL,local.prior.size=length(unique(colnames(D))),local.prior.bias=1,triples.thrsh=0.5,delta=1,selEGenes=FALSE,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; or 'triples' for
          triple-based inference; or 'pairwise' for the pairwise
          heuristic; or 'ModuleNetwork' for the module based inference;
          or 'nem.greedy' for the greedy hillclimbing

  models: a list of adjacency matrices for model search. If NULL,
          'enumerate.models' is used for exhaustive enumeration of all
          possible models.

    type: 'mLL' or 'FULLmLL' or 'CONTmLL' or 'CONTmLLDens' or
          'CONTmLLRatio'

    para: vector of length two: false positive rate and false negative
          rate for non-binary data. Used by 'mLL()'

hyperpara: vector of length four: used by 'FULLmLL()' for binary data

      Pe: prior of effect reporter positions in the phenotypic
          hierarchy (same dimension as D)

      Pm: prior over models (n x n matrix)

 Pmlocal: local model prior for pairwise and triple learning. For
          pairwise learning generated by 'local.model.prior()'
          according to arguments 'local.prior.size' and
          'local.prior.bias'

local.prior.size: prior expected number of edges in the graph (for
          pairwise learning)

local.prior.bias: bias towards double-headed edges. Default: 1 (no
          bias; for pairwise learning)

triples.thrsh: threshold for model averaging to combine triple models
          for each edge

   delta: regularization parameter for automated E-gene subset
          selection (CONTmLLRatio only)

selEGenes: automated E-gene subset selection (includes tuning of delta
          for CONTmLLRatio)

 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 AIC criterion ('network.AIC').

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

   graph: the inferred directed graph (graphNEL object)

     mLL: marginal likelihood of final model

     pos: posterior over effect positions

  mappos: MAP estimate of effect positions

    type: as used in function call

    para: as used in function call

hyperpara: as used in function call

  lambda: as in function call

selected: selected E-gene subset

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

     Holger Froehlich

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

     'network.AIC', 'nem', 'score', 'nem.greedy', 'moduleNetwork',
     'triples.posterior', 'pairwise.posterior', 'local.model.prior',
     'enumerate.models'

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

        data("BoutrosRNAi2002")
        D <- BoutrosRNAiDiscrete[,9:16]
        p <- c(.13,.05)
        res <- nemModelSelection(c(0.1,1,10),D, para=p, Pm=matrix(0,ncol=4,nrow=4))   
        
        
        plot(res,main="highest scoring model")      

