proprate2            package:surv2sample            R Documentation

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_f_o_r _C_e_n_s_o_r_e_d _D_a_t_a

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

     'proprate2' estimates the two-sample proportional rate
     transformation model (proportional hazards, proportional odds) for
     censored data using the simplified partial likelihood.

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

     proprate2(x, group, model = 0, beta.init = 0, maxiter = 20,
               eps = 1e-09)

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

       x: a '"Surv"' object, as returned by the 'Surv' function.

   group: a vector indicating to which group each observation belongs.
          May contain values 1 and 2 only.

   model: the type of model. Possible values are 0 for proportional
          hazards, 1 for proportional odds.

beta.init: the initial parameter value for iteration.

 maxiter: the maximum number of iterations.

     eps: the convergence tolerance parameter. The convergence
          criterion is |(l-l_old)/l|<'eps'.

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

     This function fits the proportional rate model for two samples of
     censored survival data. Currently two most important models are
     implemented: proportional hazards and proportional odds. The
     estimation procedure is based on a two-sample simplification of
     the partial for the two-sample situation, see Bagdonavicius and
     Nikulin (2000). (For proportional hazards, this method is the
     usual partial likelihood.)

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

     A list of class '"proprate2.fit"' with main components: 

    beta: the estimate.

     var: its variance.

      G0: the cumulative baseline rate (at times 'time').

    time: sorted times.

    iter: the number of iterations used.

converged: logical. Did the iterations (appear to) converge?

loglik.init: the simplified partial likelihood at the initial value of
          the parameter.

  loglik: the simplified partial likelihood at the estimate.

     d11: the derivative of the score.

 sigma11: variance of the score (for proportional hazards 'sigma11'
          equals 'd11').

  G1, G2: cumulative transformation rates computed separately in the
          two groups (both of length n, at times 'time').

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

     David Kraus (<URL: http://www.davidkraus.net/>)

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

     Bagdonavicius, V. and Nikulin, M. (2000) On goodness-of-fit for
     the linear transformation and frailty models. _Statist. Probab.
     Lett._ *47*, 177-188.

     Kraus, D. (2007) Checking proportional rates in the two-sample
     transformation model. Research Report 2203, Institute of
     Information Theory and Automation, Prague. Available at <URL:
     http://www.davidkraus.net/surv2sample/>.

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

     There is a 'plot' method for objects returned by 'proprate2'.

     See 'proprate2.neyman', 'proprate2.ks', 'proprate2.gs' for tests
     of the proportional rate assumption.

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

     ## chronic active hepatitis data
     data(hepatitis)

     ## fit the proportional odds model
     fit = with(hepatitis, proprate2(Surv(time, status), treatment,
         model = 1))
     fit

     ## plot model-based and model-free estimates of odds functions
     plot(fit)

