intcox                package:intcox                R Documentation

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

     Intcox fits the Cox proportional hazards model for interval
     censored data by the Iterative Convex Minorant Algorithm (ICM)

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

     intcox(formula = formula(data), data = parent.frame(), subset, na.action, x = FALSE, y = TRUE, epsilon = 1e-04, itermax = 10000, no.warnings = FALSE)

_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 of type '"interval2"' as returned by the
          'Surv' function. 

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

  subset: expression saying that only a subset of the rows of the data
          should 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'. 

       x: Return the design matrix in the model object? 

       y: Return the response in the model object? 

 epsilon: convergence treshold. Iteration will continue until the
          relative change in the log-likelihood is less then epsilon.
          Default is .0001. 

 itermax: maximum number of iteration 

no.warnings: logical value indicating how to handle warnings. If
          'TRUE', warnings will be displayed. Default is 'FALSE'. 

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

     With this package the Cox proportional hazards model can be
     applied for interval censored data. It tries to maximise the 
     log-likelihood by a simultanious improvement of the coefficients
     and the cumulative hazard function in the gradient  direction
     weighted by the main diagonal elements of the negative Hessian
     matrix.

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

     an object of class '"coxph"'. See 'coxph.object' for details. Not
     all features are realised.  Additionally there are given  

lambda0 : estimated baseline hazard 

time.point : corresponding time points for the steps

likeli.vec : vector of the estimated loglik of each step

termination: indicator for the reason of termination, 
           1 - algorithm converged
           2 - no improvement of likelihood possible, the iteration
          number is shown
           3 - algorithm did not converge - maximum number of iteration
          reached
           4 - inside precondition(s) are not fulfilled at this
          iteration

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

     Ch. Heiss, V. Henschel, U. Mansmann

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

     Wei Pan, (1999), Extending the Iterative Convex Minorant Algorithm
     to the Cox Model for Interval-Censored Data,  Journal of
     Computational & Graphical Statistics, vol. 8, pp. 109-120

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

     'coxph', 'Surv'

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

     data(intcox.example)
     intcox(Surv(left,right,type="interval2")~x.1+x.2+x.3+x.4,data=intcox.example)

