local.model.prior            package:nem            R Documentation

_C_o_m_p_u_t_e_s _a _p_r_i_o_r _t_o _b_e _u_s_e_d _f_o_r _e_d_g_e-_w_i_s_e _m_o_d_e_l _i_n_f_e_r_e_n_c_e

_D_e_s_c_r_i_p_t_i_o_n:

     The function 'pairwise.posterior' infers a phenotypic hierarchy
     edge by edge by  choosing between four models (unconnected,
     subset, superset, undistinguishable).  For each edge,
     'local.model.prior' computes a prior distribution over the four
     models. It can be used to ensure sparsity of the graph and high
     confidence in results.

_U_s_a_g_e:

     local.model.prior(size,n,bias)

_A_r_g_u_m_e_n_t_s:

    size: expected number of edges in the graph. 

       n: number of perturbed genes in the dataset, number of nodes in
          the graph

    bias: the factor by which the double-headed edge is preferred over
          the single-headed edges

_D_e_t_a_i_l_s:

     A graph on 'n' nodes has 'N=n*(n-1)/2' possible directed edges
     (one- or bi-directional).  If each edge occurs with probability
     $p$, we expect to see $Np$ edges in the graph. The function
     'local.model.prior' takes the number of genes ('n') and the 
     expected number of edges ('size') as an input and computes a prior
     distribution  for edge occurrence: no edge with probability
     'size/N', and the probability for  edge existence being split over
     the three edge models with a bias towards the conservative 
     double-headed model specified by 'bias'. To ensure sparsity, the
     'size' should  be chosen small compared to the number of possible
     edges.

_V_a_l_u_e:

     a distribution over four states: a vector of four positive real
     numbers summing to one

_A_u_t_h_o_r(_s):

     Florian Markowetz <URL: http://genomics.princeton.edu/~florian>

_S_e_e _A_l_s_o:

     'pairwise.posterior', 'nem'

_E_x_a_m_p_l_e_s:

     # uniform over the 3 edge models
     local.model.prior(4,4,1)
     # bias towards <->
     local.model.prior(4,4,2)

