survreg               package:survival               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:

     Fit a parametric survival regression model. These are
     location-scale models for an arbitrary transform of the time
     variable; the most common cases use a log transformation, leading
     to accelerated failure time models.

_U_s_a_g_e:

     survreg(formula, data, weights, subset, 
             na.action, dist="weibull", init=NULL, scale=0, 
             control,parms=NULL,model=FALSE, x=FALSE,
             y=TRUE, robust=FALSE, score=FALSE, ...)

_A_r_g_u_m_e_n_t_s:

 formula: a formula expression as for other regression models.  The
          response is usually a survival object as returned by the
          'Surv' function.  See the documentation for 'Surv', 'lm' and
          'formula' for details.  

    data: a data frame in which to interpret the variables named in 
          the 'formula', 'weights' or the 'subset' arguments.  

 weights: optional vector of case weights

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

na.action: a missing-data filter function, applied to the model.frame,
          after any  'subset' argument has been used.  Default is
          'options()\$na.action'.  

    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 the format described in
          'survreg.distributions'. 

   parms: 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'. The default value is 'survreg.control()' 

model,x,y: flags to control what is returned.  If any of these is true,
          then the model frame, the model matrix, and/or the vector of
          response times will be returned as components of the final
          result, with the same names as the flag arguments.

   score: return the score vector. (This is expected to be zero upon
          successful convergence.) 

  robust: Use robust 'sandwich' standard errors, based on independence
          of individuals if there is no 'cluster()' term in the
          formula, based on independence of clusters if there is.

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

_V_a_l_u_e:

     an object of class 'survreg' is returned.

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

     # Fit an exponential model: 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")

     # There are multiple ways to parameterize a Weibull distribution. The survreg 
     # function imbeds it in a general location-scale familiy, which is a 
     # different parameterization than the rweibull function, and often leads
     # to confusion.
     #   survreg's scale  =    1/(rweibull shape)
     #   survreg's intercept = log(rweibull scale)
     #   For the log-likelihood all parameterizations lead to the same value.
     y <- rweibull(1000, shape=2, scale=5)
     survreg(Surv(y)~1, dist="weibull")

     # Economists fit a model called `tobit regression', which is a standard
     # linear regression with Gaussian errors, but with left censored data.
     tobinfit <- survreg(Surv(durable, durable>0, type='left') ~ age + quant,
                         data=tobin, dist='gaussian')