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=NULL,a=0.15,b=0.05,Pe=NULL,Pm=NULL,lambda=0,type="mLL")

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

     Phi: an adjacency matrix with unit main diagonal

      D1: (i) count matrix for discrete data: phenotypes x genes. How
          often did we see an effect after interventions? (ii) matrix
          describing the PROBABILITIES of an effect (iii) matrix
          discribing the log-LIKELIHOOD of an effect (e.g. log-density
          matrix, log-odds matrix)

      D0: count matrix: phenotypes x genes. How often did we NOT see an
          effect after intervention? Not used for continious data

       a: false positive rate: how probable is it to miss an effect?
          (for count matrix)

       b: false negative rate: how probable is it to see a spurious
          effect? (for count matrix)

      Pe: prior of effect reporter positions in the phenotypic
          hierarchy

      Pm: prior on model graph (n x n matrix) with entries 0 <=
          priorPhi[i,j] <= 1 describing the probability of an edge
          between gene i and gene j.

  lambda: regularization parameter to incorporate prior assumptions.

    type: see 'nem'

_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

LLperGene: likelihood per E-gene

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

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

     'nem', 'score', 'FULLmLL'

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

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

