findMaxD2               package:edgeR               R Documentation

_M_a_x_i_m_i_z_e_s _t_h_e _n_e_g_a_t_i_v_e _b_i_n_o_m_i_a_l _l_i_k_e_l_i_h_o_o_d

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

     Maximizes the negative binomial likelihood (a weighted version
     using the common likelihood given weight alpha) for each tag

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

      
     findMaxD2(object, alpha = 0.5, grid = TRUE, tol = 1e-05, n.iter = 10, grid.length = 200)

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

  object: list containing the raw data with elements 'data' (table of
          counts), 'group' (vector indicating group) and 'lib.size'
          (vector of library sizes)

   alpha: weight given to common likelihood, set to 0 for individual
          estimates or large (e.g. 100) for common likelihood

    grid: logical, whether to use a grid search (default = 'TRUE'); if
          'FALSE' use Newton-Rhapson steps

     tol: if 'grid=FALSE', tolerance for Newton-Rhapson iterations

  n.iter: if 'grid=FALSE', number of Newton-Rhapson iterations

grid.length: length of the grid over which to maximize; default '200'

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

     vector of the values of delta that maximize the negative binomial
     likelihood for each tag  (where delta = 'phi / (phi+1)' and 'phi'
     is the overdispersion parameter)

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

     Mark Robinson, Davis McCarthy

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

     y<-matrix(rnbinom(1000,mu=10,size=2),ncol=4)
     d<-DGEList(data=y,group=c(1,1,2,2),lib.size=c(1000:1003))
     cml1<-findMaxD2(d,alpha=10)
     cml2<-findMaxD2(d,alpha=0)

