set.default.parameters          package:nem          R Documentation

_G_e_t/_s_e_t _h_y_p_e_r_p_a_r_a_m_e_t_e_r_s

_D_e_s_c_r_i_p_t_i_o_n:

     Allows to set and retrieve various hyperparameters for different
     inference methods.

_U_s_a_g_e:

     set.default.parameters(Sgenes, ...)

_A_r_g_u_m_e_n_t_s:

  Sgenes: character vector of S-gene identifiers

     ...: parameters to set (see details)

_D_e_t_a_i_l_s:

     Since version 2.5.4 functions in the nem package do not have any
     more a large amount of individual parameters. Instead there is
     just one hyperparameter, which is passed to all functions.
     Parameter values with the hyperparameter can be set with this
     function.

     _t_y_p_e 'mLL' or 'FULLmLL' or 'CONTmLL' or 'CONTmLLBayes' or
          'CONTmLLMAP' or 'gnem'. 'CONTmLLDens' and 'CONTmLLRatio' are
          identical to 'CONTmLLBayes' and 'CONTmLLMAP' and are still
          supported for compatibility reasons. 'mLL' and 'FULLmLL' are
          used for binary data (see 'BoutrosRNAiDiscrete') and
          'CONTmLL' for a matrix of effect probabilities.
          'CONTmLLBayes' and 'CONTmLLMAP' are used, if log-odds ratios,
          p-value densities or any other model specifies effect
          likelihoods. 'CONTmLLBayes' refers to an inference scheme,
          were the linking positions of effect reporters to network
          nodes are integrated out, and 'CONTmLLMAP' to an inference
          scheme, were a MAP estimate for the linking positions is
          calculated. 'gnem' indicates Generalized Nested Effects
          Models (GNEMs).

     _p_a_r_a vector of length two: false positive rate and false negative
          rate for binary data. Used by 'mLL' 

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

     _P_e prior of effect reporter positions in the phenotypic hierarchy
          (same dimension as D). Not used type 'gnem'. Default: NULL

     _P_m prior over models (n x n matrix). Default: NULL

     _P_m_l_o_c_a_l 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'

     _l_o_c_a_l._p_r_i_o_r._s_i_z_e prior expected number of edges in the graph (for
          pairwise learning). Default: no. nodes

     _l_o_c_a_l._p_r_i_o_r._b_i_a_s bias towards double-headed edges. Default: 1 (no
          bias; for pairwise learning)

     _t_r_i_p_l_e_s._t_h_r_s_h threshold for model averaging to combine triple
          models for each edge. Default: 0.5

     _l_a_m_b_d_a regularization parameter to incorporate prior assumptions.
          May also be a vector of possible values, if
          'nemModelSelection' is used, Default: 0 (no regularization)

     _d_e_l_t_a regularization parameter for automated  subset selection of
          effect reporters (CONTmLLMAP only). Default: 1/no. nodes

     _s_e_l_E_G_e_n_e_s automated E-gene subset selection (includes tuning of
          delta for CONTmLLMAP). Default: FALSE  

     _t_r_a_n_s._c_l_o_s_e Should always transitive closed graphs be computed?
          Default: TRUE. NOTE: This has only an impact for type 
          'nem.greedyMAP' and 'gnem'. Default: TRUE

     _b_a_c_k_w_a_r_d._e_l_i_m_i_n_a_t_i_o_n For module networks and greedy hillclimbing
          inference: Try to eliminate edges increasing the likelihood.
          Works only, if trans.close=FALSE. Default: FALSE 

     _m_o_d_e For Bayesian network inference and GNEMs: 'binary_ML':
          effects come from a binomial distribution - ML learning of
          parameters (Bayesian networks only); 'binary_Bayesian':
          effects come from a binomial distribution - Bayesian learning
          of parameters (Bayesian networks only); 'continous_ML':
          effects come from a normal distribution - ML learning of
          parameters; 'continous_Bayesian': effects come from a normal
          distribution - Bayesian learning of parameters.

     _n_u._i_n_t_e_r_v_e_n_t_i_o_n, _l_a_m_b_d_a._i_n_t_e_r_v_e_n_t_i_o_n For 'gnem': For any perturbed
          node we suppose the unknown mean mu given its unknown
          variance sigma2 to be drawn from N(nu.intervention,
          sigma2/lambda.intervention). Default: nu.intervention=0.6,
          lambda.intervention=4

     _n_u._n_o__i_n_t_e_r_v_e_n_t_i_o_n, _l_a_m_b_d_a._n_o__i_n_t_e_r_v_e_n_t_i_o_n The same parameters for
          unperturbed nodes. Default: nu.no_intervention=0.95,
          lambda.no_intervention=4

     _d_f._i_n_t_e_r_v_e_n_t_i_o_n, _s_c_a_l_e._i_n_t_e_r_v_e_n_t_i_o_n For 'gnem': The unknown
          variance sigma2 for perturbed nodes is supposed to be drawn
          from Inv-$\chi^2$(df.intervention, scale.intervention).
          Default: df.intervention=4.4, scale.intervention=4.4

     _d_f._n_o__i_n_t_e_r_v_e_n_t_i_o_n, _s_c_a_l_e._n_o__i_n_t_e_r_v_e_n_t_i_o_n The same parameters for
          unperturbed nodes. Default: df.no_intervention=4.4,
          scale.no_intervention=0.023

     _m_a_p For 'gnem': Mapping of interventions to network nodes. The
          format is a named list of strings with names being the
          interventions and entries being the network nodes. Default:
          Entries and names are the network nodes. 

     _o_u_t_p_u_t_d_i_r Directory where to put diagnostic plots. Default: folder
          "QualityControl" in current working directory

     _d_e_b_u_g Print out or plot diagnostic information. Default: FALSE


_V_a_l_u_e:

     A list containing all parameters described above.

_A_u_t_h_o_r(_s):

     Holger Froehlich <URL: http:/www.dkfz.de/mga2/people/froehlich>

_E_x_a_m_p_l_e_s:

     control = set.default.parameters(LETTERS[1:5], type="CONTmLLBayes", selEGenes=TRUE) # set inference type and whether to use automatic E-gene selection for a network with nodes "A"-"E".

