

coxre(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 inten-
     sity function is fitted for each column. To fit identi-
     cal intensity functions to all response types, give the
     times as a vector.

_U_s_a_g_e_:

     coxre(repsonse, censor, nest, cov, stratified=F, cumul=F, 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 col-
          umn per type of response (or subunit).

  censor: Corresponding vector or matrix of censoring indi-
          cators.

    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.

  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.  Statis-
     tics 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(rweibull(55,2,5),ncol=5)
     # Different intensity functions
     coxre(response=y, censor=matrix(rep(1,55),ncol=5), nest=1:11,
             est=0.7, stratified=T)
     # Proportional intensity functions
     coxre(response=y, censor=matrix(rep(1,55),ncol=5),
             nest=1:11, cov=rpois(11,2), est=0.7, stratified=F)
     # Identical intensity functions
     coxre(response=as.vector(t(y)), censor=rep(1,55),
             nest=rep(1:11,rep(5,11)), est=0.7)

