coxre                 package:event                 R Documentation

_C_o_x _P_r_o_p_o_r_t_i_o_n_a_l _H_a_z_a_r_d_s _M_o_d_e_l _w_i_t_h _R_a_n_d_o_m _E_f_f_e_c_t

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

     'coxre' fits a Cox proportional hazards model to event history
     data using a gamma distribution random effect. The parameter,
     gamma, is the variance of this mixing distribution.

     If a matrix of response times is supplied, the model can be
     stratified by columns, i.e. a different intensity function is
     fitted for each column. To fit identical intensity functions to
     all response types, give the times as a vector.

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

     coxre(response, censor, nest=NULL, cov=NULL, stratified=FALSE,
             cumul=FALSE,estimate=1, iter=10, print.level=0, ndigit=10,
             gradtol=0.00001, steptol=0.00001, iterlim=100, fscale=1,
             typsiz=abs(estimate), stepmax=estimate)

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

response: Vector or matrix of times to events, with one column per type
          of response (or subunit).

  censor: Corresponding vector or matrix of censoring indicators. If
          NULL all values are set to one.

    nest: Vector indicating to which unit each observation belongs.

     cov: One covariate

stratified: If TRUE, a model stratified on type of response (the
          columns of response) is fitted instead of proportional
          intensities.

   cumul: Set to TRUE if response times are from a common origin
          instead of times to (or between) events.

estimate: Initial estimate of the frailty parameter.

    iter: Maximum number of iterations allowed for the inner EM loop.

  others: Plotting control options.

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

     D.G. Clayton and J.K. Lindsey

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

     Clayton, D. (1987) The analysis of event history data: a review of
     progress and outstanding problems.  Statistics in Medicine 7:
     819-841

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

     'kalsurv'.

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

     # 11 individuals, each with 5 responses
     y <- matrix(c(51,36,50,35,42,
             27,20,26,17,27,
             37,22,41,37,30,
             42,36,32,34,27,
             27,18,33,14,29,
             43,32,43,35,40,
             41,22,36,25,38,
             38,21,31,20,16,
             36,23,27,25,28,
             26,31,31,32,36,
             29,20,25,26,25),ncol=5,byrow=TRUE)
     # Different intensity functions
     coxre(response=y, censor=matrix(rep(1,55),ncol=5), nest=1:11,
             est=0.7, stratified=TRUE)
     # Proportional intensity functions for the five responses
     coxre(response=y, censor=matrix(rep(1,55),ncol=5), nest=1:11,
             est=0.7, stratified=FALSE)
     # Identical intensity functions
     coxre(response=as.vector(t(y)), censor=rep(1,55),
             nest=rep(1:11,rep(5,11)), est=0.7)

