EM                   package:Icens                   R Documentation

_A _f_u_n_c_t_i_o_n _t_o _c_o_m_p_u_t_e _t_h_e _N_P_M_L_E _o_f _p _b_a_s_e_d _o_n _t_h_e _i_n_c_i_d_e_n_c_e
_m_a_t_r_i_x _A.

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

     The incidence matrix, 'A' is the m by n matrix that represents the
     data. There are m probabilities that must be estimated. The EM, or
     expectation maximization, method is applied to these data.

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

     EM(A, pvec, maxiter=500, tol=1e-12)

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

       A: The incidence matrix. 

    pvec: The probability vector. 

 maxiter: The maximum number of iterations. 

     tol: The tolerance used to judge convergence. 

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

     Lots.

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

     An object of class 'icsurv' containing the following components:  

     pf : The NPMLE of the probability vector.

numiter : The number of iterations used.

converge : A boolean indicating whether the algorithm converged.

 intmap : If present indicates the real representation of the support
          for the values in 'pf'. 

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

     Alain Vandal and Robert Gentleman.

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

     The EM algorithm applied to the maximal cliques of the
     intersection graph of the censored data. _The empirical
     distribution function with arbitrarily grouped, censored and
     truncated data_, B. W. Turnbull, 1976, JRSS;B.

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

     'VEM', 'ISDM', 'EMICM', 'PGM'

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

         data(cosmesis)
         csub1 <- subset(cosmesis, subset= Trt==0, select=c(L,R))
         EM(csub1)
         data(pruitt)
         EM(pruitt)

