coxreg                  package:eha                  R Documentation

_C_o_x _r_e_g_r_e_s_s_i_o_n

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

     Performs Cox regression with some special attractions, especially
     _sampling of risksets_ and _the weird bootstrap_.

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

     coxreg(formula = formula(data), data = parent.frame(),
     na.action = getOption("na.action"), init, method = c("efron", "breslow"),
     control = list(eps = 1e-08, maxiter = 10, trace = FALSE),
     singular.ok = TRUE, model = FALSE,
     x = FALSE, y = TRUE, boot = FALSE, rs, max.survs)

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

 formula: a formula object, with the response on the left of a ~
          operator, and the terms on the right. The response must be a
          survival object as returned by the Surv function.

    data: a data.frame in which to interpret the variables named in the
          formula.

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

    init: vector of initial values of the iteration. Default initial
          value is zero for all variables.

  method: Method of treating ties, "efron" (default) or "breslow".

 control: a list with components 'eps' (convergence criterion),
          'maxiter' (maximum number of iterations), and 'silent'
          (logical, controlling amount of output). You can change any
          component without mention the other(s).

singular.ok: Not used

   model: Not used

       x: Return the design matrix in the model object?

       y: return the response in the model object?

      rs: Risk set?

    boot: Number of boot replicates. Defaults to FALSE, no boot
          samples.

max.survs: Sampling of risk sets? If given, it should (the upper limit
          on) the number of survivors in each risk set.

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

     The default method, 'efron', and the alternative, 'breslow', are
     both the same as in 'coxph' in package 'survival'.

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

     A list of class 'c("coxreg", "coxph")' with components 

coefficients: Fitted parameter estimates.

     var: Covariance matrix of the estimates.

  loglik: Vector of length two; first component is the value at the
          initial parameter values, the second componet is the
          maximized value.

   score: The score test statistic (at the initial value).

linear.predictors: The estimated linear predictors.

residuals: The martingale residuals.

  hazard: The estimated baseline hazard.

   means: Means of the columns of the design matrix.

 w.means: Weighted (against exposure time) means of covariates;
          weighted relative frequencies of levels of factors.

       n: Number of spells in indata (possibly after removal of cases
          with NA's).

  events: Number of events in data.

   terms: Used by extractor functions.

  assign: Used by extractor functions.

wald.test: The Walt test statistic (at the initial value).

       y: The Surv vector.

     isF: Logical vector indicating the covariates that are factors.

  covars: The covariates.

     ttr: Total Time at Risk.

  levels: List of levels of factors.

 formula: The calling formula.

bootstrap: The (matrix of) bootstrap replicates, if requested on input.
          It is up to the user to do whatever desirable with this
          sample.

 boot.sd: The estimated standard errors of the bootstrap replicates.

    call: The call.

  method: The method.

convergence: Did the optimization converge?

    fail: Did the optimization fail? (Is 'NULL' if not).

_W_a_r_n_i_n_g:

     The use of 'rs' is dangerous, see note. It can however speed up
     computing time considerably for huge data sets.

_N_o_t_e:

     This function starts by creating risksets, if no riskset is
     supplied via 'rs', with the aid of 'risksets'. Supplying output
     from 'risksets' via 'rs' fails if there are any NA's in the data!
     Note also that it depends on stratification, so 'rs' contains
     information about stratification. Giving another strata variable
     in the formula is an error. The same is ok, for instance to supply
     stratum interactions.

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

     Gran Brostrm

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

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

     'coxph', 'risksets'

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

      dat <- data.frame(time=  c(4, 3,1,1,2,2,3),
                     status=c(1,1,1,0,1,1,0),
                     x=     c(0, 2,1,1,1,0,0),
                     sex=   c(0, 0,0,0,1,1,1))
      coxreg( Surv(time, status) ~ x + strata(sex), data = dat) #stratified model
      # Same as:
      rs <- risksets(Surv(dat$time, dat$status), strata = dat$sex)
      coxreg( Surv(time, status) ~ x, data = dat, rs = rs) #stratified model
      

