survreg              package:survival5              R Documentation

_R_e_g_r_e_s_s_i_o_n _f_o_r _a _P_a_r_a_m_e_t_r_i_c _S_u_r_v_i_v_a_l _M_o_d_e_l

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

     Regression for a parametric survival model

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

     survreg(formula, data=sys.parent(), subset, na.action,
     dist="weibull", init, scale=0, control,
     model=F, x=F, y=T, ...)

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

 formula: a formula expression as for other regression models. See the
          documentation for `lm' and `formula' for details. 

    data: optional data frame in which to interpret the variables
          occurring in the formula. 

  subset: subset of the observations to be used in the fit. 

na.action: function to be used to handle any NAs in the data. 

    dist: assumed distribution for y variable.  If the argument is a
          character string, then it is assumed to name an element from
          `survreg.distributions'. These include `"weibull"',
          `"exponential"', `"gaussian"', `"logistic"', `"lognormal"'
          and `"loglogistic"'. Otherwise, it is assumed to be a user
          defined list conforming to this standard. 

    parm: a list of fixed parameters.  For the t-distribution for
          instance this is the degrees of freedom; most of the
          distributions have no parameters. 

    init: optional vector of initial values for the parameters. 

   scale: optional fixed value for the scale.  If set to <=0 then the
          scale is estimated. 

 control: a list of control values, in the format producted by
          `survreg.control'. 

   model: if TRUE, the model frame is returned. 

       x: if TRUE, then the X matrix is returned. 

       y: if TRUE, then the y vector (or survival times) is returned. 

     ...: other arguments which will be passed to `survreg.control'. 

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

     an object of class `survreg' is returned.

_N_O_T_E:

     This routine underwent significant changes from survival4 to
     survival5. The survreg.old function gives a backwards-compatible
     interface.

_C_O_M_P_U_T_A_T_I_O_N:

     The routine uses a Newton-Raphson iteration with step halving, 
     with provision for general penalized term. Fisher scoring is used
     for intermediate steps where the information matrix is not
     positive definite.

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

     `survreg.object', `survreg.distributions', `pspline', `frailty',
     `ridge', `survreg.old'

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

     data(ovarian)
     ## These are all the same
     survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='weibull',scale=1)
     survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian,
     dist="exponential")
     survreg.old(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='extreme',fixed=list(scale=1),link="log")

