BJnoint                package:emplik                R Documentation

_T_h_e _B_u_c_k_l_e_y-_J_a_m_e_s _c_e_n_s_o_r_e_d _r_e_g_r_e_s_s_i_o_n _e_s_t_i_m_a_t_o_r _w_i_t_h_o_u_t _i_n_t_e_r_c_e_p_t _t_e_r_m

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

     Compute the Buckley-James estimator in the regression model 

                      y_i = beta x_i + epsilon_i

     with right censored y.

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

     BJnoint(x, y, delta, beta0 = NA, maxiter=30, error = 0.00001)

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

       x: a matrix or vector containing the covariate, one row per
          observation.

       y: a numeric vector of length N, censored responses. 

   delta: a vector of length N, delta=0/1 for censored/uncensored.

   beta0: an optional vector for starting value of iteration.

 maxiter: an optional integer to control iterations.

   error: an optional positive value to control interations.

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

     If your model do have an intercept term, then you should use the
     function 'bj( )' in the Design library.

     This function is included here mainly to produce the estimator
     value that may provide some useful info with function 'bjtest( )'.
     For example you may want to test the beta value close to the
     Buckley-James estimator.

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

     A list with the following components: 

    beta: the Buckley-James estimator.

iteration: number of iterations performed.

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

     Mai Zhou.

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

     Buckley, J. and James, I. (1979). Biometrika 

     Owen, A. (1990). Empirical likelihood ratio confidence regions. 
     Ann. Statist. *18* 90-120.

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

     x <- matrix(c(rnorm(50,mean=1), rnorm(50,mean=2)), ncol=2,nrow=50)
     ## Suppose now we wish to test Ho: 2mu(1)-mu(2)=0, then
     y <- 2*x[,1]-x[,2]
     xx <- c(28,-44,29,30,26,27,22,23,33,16,24,29,24,40,21,31,34,-2,25,19)

