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 interations.

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

     This function can be used to 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 should use the function
     'bj( )' in the 'Design' library. It should be more refined  than
     'BJnoint'. 

     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 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).  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)

