nem.jackknife              package:nem              R Documentation

_J_a_c_k_k_n_i_f_e _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:

     Assesses the statistical stability of a network via a jackknife
     procedure: Each S-gene is left out once and the network
     reconstructed on the remaining ones. The relative frequency of
     each edge to appear in n-1 jackknife samples is returned.

_U_s_a_g_e:

     nem.jackknife(D, thresh=0.5, inference="nem.greedy",models=NULL,control=set.default.parameters(unique(colnames(D))), verbose=TRUE)

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

_A_r_g_u_m_e_n_t_s:

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

  thresh: only edges appearing with a higher frequency than "thresh"
          are returned

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:

     Calls 'nem' or 'nemModelSelection' internally, depending on
     whether or not parameter lambda is a vector and parameter Pm !=
     NULL.

_V_a_l_u_e:

     nem object with edge weights being the jackknife probabilities

_A_u_t_h_o_r(_s):

     Holger Froehlich

_S_e_e _A_l_s_o:

     'nem.bootstrap', 'nem.consensus', 'nem', 'nemModelSelection'

_E_x_a_m_p_l_e_s:

     ## Not run: 
        data("BoutrosRNAi2002")
        D <- BoutrosRNAiDiscrete[,9:16]
        nem.jackknife(D, control=set.default.parameters(unique(colnames(D)), para=c(0.13,0.05)))         
     ## End(Not run)

