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

_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_i.

_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 iterations.

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

     This function compute the Buckley-James estimator  when your model
     do not have an intercept term. Of course, if you include a column
     of 1's in the x matrix,  it is also OK with this function and it
     is equivalent to having an intercept term. If your model do have
     an intercept term, then you probably should use the function 'bj(
     )' in the 'Design' library. It should be more refined  than
     'BJnoint' in the stopping rule for the iterations.

     This function is included here mainly to produce the estimator
     value that may provide some useful information with the function
     'bjtest( )'. For example you may want to test the beta value near
     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).  Linear regression with censored
     data. _Biometrika_, *66* 429-36.

_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)

