rs.zph                package:relsurv                R Documentation

_B_e_h_a_v_i_o_u_r _o_f _C_o_v_a_r_i_a_t_e_s _i_n _T_i_m_e _f_o_r _R_e_l_a_t_i_v_e _S_u_r_v_i_v_a_l _R_e_g_r_e_s_s_i_o_n _M_o_d_e_l_s

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

     Calculates the scaled partial residuals of a relative survival
     model ('rsadd', 'rsmul' or 'rstrans')

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

     rs.zph(fit,sc,transform="identity",var.type="sum")

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

     fit: the result of fitting an additive relative survival model,
          using the 'rsadd', 'rsmul' or 'rstrans'  function. 

          In the case of multiplicative and transformation models the
          output is  identical to 'cox.zph' function, except no test is
          performed. 

      sc: partial residuals calculated by the 'resid' function. This is
          used to save time if several tests are to be calculated on
          these residuals and can otherwise be omitted. 

transform: a character string specifying how the survival times should
          be transformed. Possible values are '"km"', '"rank"',
          '"identity"' and 'log'. The default is '"identity"'.

var.type: a character string specifying the variance used to scale the
          residuals. Possible values are '"each"', which estimates the
          variance for each residual separately, and 'sum'(default), 
          which assumes the same variance for all the residuals. 

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

     an object of class 'rs.zph'. This function would usually be
     followed by a plot of the result. The plot gives an estimate of
     the time-dependent coefficient 'beta(t)'. If the proportional
     hazards assumption is true, 'beta(t)' will be a horizontal line.

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

     Stare J., Pohar M., Henderson R. "Goodness of fit for relative
     survival models." _Statistics in Medicine_. To appear in 2005.

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

     'rsadd', 'rsmul', 'rstrans', 'resid', 'cox.zph'.

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

     data(slopop)
     data(rdata)
     fit <- rsadd(Surv(time,cens)~sex+ratetable(age=age*365,sex=sex,year=year),
            ratetable=slopop,data=rdata,int=5)
     rszph <- rs.zph(fit)

