mLL                   package:nem                   R Documentation

_M_a_r_g_i_n_a_l _l_i_k_e_l_i_h_o_o_d _o_f _a _p_h_e_n_o_t_y_p_i_c _h_i_e_r_a_r_c_h_y

_D_e_s_c_r_i_p_t_i_o_n:

     computes the marginal likelihood of observed phenotypic data given
     a phenotypic hierarchy.

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

     mLL(Phi, D1, D0, a, b, Pe)

_A_r_g_u_m_e_n_t_s:

     Phi: an adjacency matrix with unit main diagonal

      D1: count matrix: phenotypes x genes. How often did we see an
          effect after interventions?

      D0: count matrix: phenotypes x genes. How often did we NOT see an
          effect after intervention?

       a: false positive rate: how probable is it to miss an effect?

       b: false negative rate: how probable is it to see a spurious
          effect?

      Pe: prior of effect reporter positions in the phenotypic
          hierarchy

_D_e_t_a_i_l_s:

     It computes the marginal likelihood of a single phenotypic
     hierarchy. Usually called from within the function 'score'.

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

     mLL: marginal likelihood of a phenotypic hierarchy

     pos: posterior distribution of effect positions in the hierarchy

  mappos: Maximum aposteriori estimate of effect positions

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

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

_R_e_f_e_r_e_n_c_e_s:

     Markowetz F, Bloch J, Spang R, Non-transcriptional pathway
     features reconstructed from secondary effects of RNA interference,
     Bioinformatics, 2005

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

     'score', 'FULLmLL'

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

        data("BoutrosRNAi2002")
        result <- nem(BoutrosRNAiDiscrete[,9:16],type="mLL",para=c(.15,.05))

